The pricing of moving window Asian option with an early exercise feature is considered a challenging problem in option pricing. The computational challenge lies in the unknown optimal exercise strategy and in the high...The pricing of moving window Asian option with an early exercise feature is considered a challenging problem in option pricing. The computational challenge lies in the unknown optimal exercise strategy and in the high dimensionality required for approximating the early exercise boundary. We use sparse grid basis functions in the Least Squares Monte Carlo approach to solve this “curse of dimensionality” problem. The resulting algorithm provides a general and convergent method for pricing moving window Asian options. The sparse grid technique presented in this paper can be generalized to pricing other high-dimensional, early-exercisable derivatives.展开更多
High-dimensional heterogeneous data have acquired increasing attention and discussion in the past decade.In the context of heterogeneity,semiparametric regression emerges as a popular method to model this type of data...High-dimensional heterogeneous data have acquired increasing attention and discussion in the past decade.In the context of heterogeneity,semiparametric regression emerges as a popular method to model this type of data in statistics.In this paper,we leverage the benefits of expectile regression for computational efficiency and analytical robustness in heterogeneity,and propose a regularized partially linear additive expectile regression model with a nonconvex penalty,such as SCAD or MCP,for high-dimensional heterogeneous data.We focus on a more realistic scenario where the regression error exhibits a heavy-tailed distribution with only finite moments.This scenario challenges the classical sub-gaussian distribution assumption and is more prevalent in practical applications.Under certain regular conditions,we demonstrate that with probability tending to one,the oracle estimator is one of the local minima of the induced optimization problem.Our theoretical analysis suggests that the dimensionality of linear covariates that our estimation procedure can handle is fundamentally limited by the moment condition of the regression error.Computationally,given the nonconvex and nonsmooth nature of the induced optimization problem,we have developed a two-step algorithm.Finally,our method’s effectiveness is demonstrated through its high estimation accuracy and effective model selection,as evidenced by Monte Carlo simulation studies and a real-data application.Furthermore,by taking various expectile weights,our method effectively detects heterogeneity and explores the complete conditional distribution of the response variable,underscoring its utility in analyzing high-dimensional heterogeneous data.展开更多
Owing to their global search capabilities and gradient-free operation,metaheuristic algorithms are widely applied to a wide range of optimization problems.However,their computational demands become prohibitive when ta...Owing to their global search capabilities and gradient-free operation,metaheuristic algorithms are widely applied to a wide range of optimization problems.However,their computational demands become prohibitive when tackling high-dimensional optimization challenges.To effectively address these challenges,this study introduces cooperative metaheuristics integrating dynamic dimension reduction(DR).Building upon particle swarm optimization(PSO)and differential evolution(DE),the proposed cooperative methods C-PSO and C-DE are developed.In the proposed methods,the modified principal components analysis(PCA)is utilized to reduce the dimension of design variables,thereby decreasing computational costs.The dynamic DR strategy implements periodic execution of modified PCA after a fixed number of iterations,resulting in the important dimensions being dynamically identified.Compared with the static one,the dynamic DR strategy can achieve precise identification of important dimensions,thereby enabling accelerated convergence toward optimal solutions.Furthermore,the influence of cumulative contribution rate thresholds on optimization problems with different dimensions is investigated.Metaheuristic algorithms(PSO,DE)and cooperative metaheuristics(C-PSO,C-DE)are examined by 15 benchmark functions and two engineering design problems(speed reducer and composite pressure vessel).Comparative results demonstrate that the cooperative methods achieve significantly superior performance compared to standard methods in both solution accuracy and computational efficiency.Compared to standard metaheuristic algorithms,cooperative metaheuristics achieve a reduction in computational cost of at least 40%.The cooperative metaheuristics can be effectively used to tackle both high-dimensional unconstrained and constrained optimization problems.展开更多
During the past decade,shrinkage priors have received much attention in Bayesian analysis of high-dimensional data.This paper establishes the posterior consistency for high-dimensional linear regression with a class o...During the past decade,shrinkage priors have received much attention in Bayesian analysis of high-dimensional data.This paper establishes the posterior consistency for high-dimensional linear regression with a class of shrinkage priors,which has a heavy and flat tail and allocates a sufficiently large probability mass in a very small neighborhood of zero.While enjoying its efficiency in posterior simulations,the shrinkage prior can lead to a nearly optimal posterior contraction rate and the variable selection consistency as the spike-and-slab prior.Our numerical results show that under the posterior consistency,Bayesian methods can yield much better results in variable selection than the regularization methods such as LASSO and SCAD.This paper also establishes a BvM-type result,which leads to a convenient way of uncertainty quantification for regression coefficient estimates.展开更多
Nonconvex penalties including the smoothly clipped absolute deviation penalty and the minimax concave penalty enjoy the properties of unbiasedness, continuity and sparsity,and the ridge regression can deal with the co...Nonconvex penalties including the smoothly clipped absolute deviation penalty and the minimax concave penalty enjoy the properties of unbiasedness, continuity and sparsity,and the ridge regression can deal with the collinearity problem. Combining the strengths of nonconvex penalties and ridge regression（abbreviated as NPR）, we study the oracle property of the NPR estimator in high dimensional settings with highly correlated predictors, where the dimensionality of covariates pn is allowed to increase exponentially with the sample size n. Simulation studies and a real data example are presented to verify the performance of the NPR method.展开更多
This study numerically examines the heat and mass transfer characteristics of two ternary nanofluids via converging and diverg-ing channels.Furthermore,the study aims to assess two ternary nanofluids combinations to d...This study numerically examines the heat and mass transfer characteristics of two ternary nanofluids via converging and diverg-ing channels.Furthermore,the study aims to assess two ternary nanofluids combinations to determine which configuration can provide better heat and mass transfer and lower entropy production,while ensuring cost efficiency.This work bridges the gap be-tween academic research and industrial feasibility by incorporating cost analysis,entropy generation,and thermal efficiency.To compare the velocity,temperature,and concentration profiles,we examine two ternary nanofluids,i.e.,TiO_(2)+SiO_(2)+Al_(2)O_(3)/H_(2)O and TiO_(2)+SiO_(2)+Cu/H_(2)O,while considering the shape of nanoparticles.The velocity slip and Soret/Dufour effects are taken into consideration.Furthermore,regression analysis for Nusselt and Sherwood numbers of the model is carried out.The Runge-Kutta fourth-order method with shooting technique is employed to acquire the numerical solution of the governed system of ordinary differential equations.The flow pattern attributes of ternary nanofluids are meticulously examined and simulated with the fluc-tuation of flow-dominating parameters.Additionally,the influence of these parameters is demonstrated in the flow,temperature,and concentration fields.For variation in Eckert and Dufour numbers,TiO_(2)+SiO_(2)+Al_(2)O_(3)/H_(2)O has a higher temperature than TiO_(2)+SiO_(2)+Cu/H_(2)O.The results obtained indicate that the ternary nanofluid TiO_(2)+SiO_(2)+Al_(2)O_(3)/H_(2)O has a higher heat transfer rate,lesser entropy generation,greater mass transfer rate,and lower cost than that of TiO_(2)+SiO_(2)+Cu/H_(2)O ternary nanofluid.展开更多
In this study,we examine the problem of sliced inverse regression(SIR),a widely used method for sufficient dimension reduction(SDR).It was designed to find reduced-dimensional versions of multivariate predictors by re...In this study,we examine the problem of sliced inverse regression(SIR),a widely used method for sufficient dimension reduction(SDR).It was designed to find reduced-dimensional versions of multivariate predictors by replacing them with a minimally adequate collection of their linear combinations without loss of information.Recently,regularization methods have been proposed in SIR to incorporate a sparse structure of predictors for better interpretability.However,existing methods consider convex relaxation to bypass the sparsity constraint,which may not lead to the best subset,and particularly tends to include irrelevant variables when predictors are correlated.In this study,we approach sparse SIR as a nonconvex optimization problem and directly tackle the sparsity constraint by establishing the optimal conditions and iteratively solving them by means of the splicing technique.Without employing convex relaxation on the sparsity constraint and the orthogonal constraint,our algorithm exhibits superior empirical merits,as evidenced by extensive numerical studies.Computationally,our algorithm is much faster than the relaxed approach for the natural sparse SIR estimator.Statistically,our algorithm surpasses existing methods in terms of accuracy for central subspace estimation and best subset selection and sustains high performance even with correlated predictors.展开更多
The impact of different global and local variables in urban development processes requires a systematic study to fully comprehend the underlying complexities in them.The interplay between such variables is crucial for...The impact of different global and local variables in urban development processes requires a systematic study to fully comprehend the underlying complexities in them.The interplay between such variables is crucial for modelling urban growth to closely reflects reality.Despite extensive research,ambiguity remains about how variations in these input variables influence urban densification.In this study,we conduct a global sensitivity analysis(SA)using a multinomial logistic regression(MNL)model to assess the model’s explanatory and predictive power.We examine the influence of global variables,including spatial resolution,neighborhood size,and density classes,under different input combinations at a provincial scale to understand their impact on densification.Additionally,we perform a stepwise regression to identify the significant explanatory variables that are important for understanding densification in the Brussels Metropolitan Area(BMA).Our results indicate that a finer spatial resolution of 50 m and 100 m,smaller neighborhood size of 5×5 and 3×3,and specific density classes—namely 3(non-built-up,low and high built-up)and 4(non-built-up,low,medium and high built-up)—optimally explain and predict urban densification.In line with the same,the stepwise regression reveals that models with a coarser resolution of 300 m lack significant variables,reflecting a lower explanatory power for densification.This approach aids in identifying optimal and significant global variables with higher explanatory power for understanding and predicting urban densification.Furthermore,these findings are reproducible in a global urban context,offering valuable insights for planners,modelers and geographers in managing future urban growth and minimizing modelling.展开更多
Triaxial tests,a staple in rock engineering,are labor-intensive,sample-demanding,and costly,making their optimization highly advantageous.These tests are essential for characterizing rock strength,and by adopting a fa...Triaxial tests,a staple in rock engineering,are labor-intensive,sample-demanding,and costly,making their optimization highly advantageous.These tests are essential for characterizing rock strength,and by adopting a failure criterion,they allow for the derivation of criterion parameters through regression,facilitating their integration into modeling programs.In this study,we introduce the application of an underutilized statistical technique—orthogonal regression—well-suited for analyzing triaxial test data.Additionally,we present an innovation in this technique by minimizing the Euclidean distance while incorporating orthogonality between vectors as a constraint,for the case of orthogonal linear regression.Also,we consider the Modified Least Squares method.We exemplify this approach by developing the necessary equations to apply the Mohr-Coulomb,Murrell,Hoek-Brown,andÚcar criteria,and implement these equations in both spreadsheet calculations and R scripts.Finally,we demonstrate the technique's application using five datasets of varied lithologies from specialized literature,showcasing its versatility and effectiveness.展开更多
The decoherence of high-dimensional orbital angular momentum(OAM)entanglement in the weak scintillation regime has been investigated.In this study,we simulate atmospheric turbulence by utilizing a multiple-phase scree...The decoherence of high-dimensional orbital angular momentum(OAM)entanglement in the weak scintillation regime has been investigated.In this study,we simulate atmospheric turbulence by utilizing a multiple-phase screen imprinted with anisotropic non-Kolmogorov turbulence.The entanglement negativity and fidelity are introduced to quantify the entanglement of a high-dimensional OAM state.The numerical evaluation results indicate that entanglement negativity and fidelity last longer for a high-dimensional OAM state when the azimuthal mode has a lower value.Additionally,the evolution of higher-dimensional OAM entanglement is significantly influenced by OAM beam parameters and turbulence parameters.Compared to isotropic atmospheric turbulence,anisotropic turbulence has a lesser influence on highdimensional OAM entanglement.展开更多
It is known that monotone recurrence relations can induce a class of twist homeomorphisms on the high-dimensional cylinder,which is an extension of the class of monotone twist maps on the annulus or two-dimensional cy...It is known that monotone recurrence relations can induce a class of twist homeomorphisms on the high-dimensional cylinder,which is an extension of the class of monotone twist maps on the annulus or two-dimensional cylinder.By constructing a bounded solution of the monotone recurrence relation,the main conclusion in this paper is acquired:The induced homeomorphism has Birkhoff orbits provided there is a compact forward-invariant set.Therefore,it generalizes Angenent's results in low-dimensional cases.展开更多
Gastric cancer is the third leading cause of cancer-related mortality and remains a major global health issue^([1]).Annually,approximately 479,000individuals in China are diagnosed with gastric cancer,accounting for a...Gastric cancer is the third leading cause of cancer-related mortality and remains a major global health issue^([1]).Annually,approximately 479,000individuals in China are diagnosed with gastric cancer,accounting for almost 45%of all new cases worldwide^([2]).展开更多
Objective Humans are exposed to complex mixtures of environmental chemicals and other factors that can affect their health.Analysis of these mixture exposures presents several key challenges for environmental epidemio...Objective Humans are exposed to complex mixtures of environmental chemicals and other factors that can affect their health.Analysis of these mixture exposures presents several key challenges for environmental epidemiology and risk assessment,including high dimensionality,correlated exposure,and subtle individual effects.Methods We proposed a novel statistical approach,the generalized functional linear model(GFLM),to analyze the health effects of exposure mixtures.GFLM treats the effect of mixture exposures as a smooth function by reordering exposures based on specific mechanisms and capturing internal correlations to provide a meaningful estimation and interpretation.The robustness and efficiency was evaluated under various scenarios through extensive simulation studies.Results We applied the GFLM to two datasets from the National Health and Nutrition Examination Survey(NHANES).In the first application,we examined the effects of 37 nutrients on BMI(2011–2016 cycles).The GFLM identified a significant mixture effect,with fiber and fat emerging as the nutrients with the greatest negative and positive effects on BMI,respectively.For the second application,we investigated the association between four pre-and perfluoroalkyl substances(PFAS)and gout risk(2007–2018 cycles).Unlike traditional methods,the GFLM indicated no significant association,demonstrating its robustness to multicollinearity.Conclusion GFLM framework is a powerful tool for mixture exposure analysis,offering improved handling of correlated exposures and interpretable results.It demonstrates robust performance across various scenarios and real-world applications,advancing our understanding of complex environmental exposures and their health impacts on environmental epidemiology and toxicology.展开更多
In recent years,machine learning(ML)techniques have been shown to be effective in accelerating the development process of optoelectronic devices.However,as"black box"models,they have limited theoretical inte...In recent years,machine learning(ML)techniques have been shown to be effective in accelerating the development process of optoelectronic devices.However,as"black box"models,they have limited theoretical interpretability.In this work,we leverage symbolic regression(SR)technique for discovering the explicit symbolic relationship between the structure of the optoelectronic Fabry-Perot(FP)laser and its optical field distribution,which greatly improves model transparency compared to ML.We demonstrated that the expressions explored through SR exhibit lower errors on the test set compared to ML models,which suggests that the expressions have better fitting and generalization capabilities.展开更多
Background:The COVID-1’s impact on influenza activity is of interest to inform future flu prevention and control strategies.Our study aim to examine COVID-19’s effects on influenza in Fujian Province,China,using a r...Background:The COVID-1’s impact on influenza activity is of interest to inform future flu prevention and control strategies.Our study aim to examine COVID-19’s effects on influenza in Fujian Province,China,using a regression discontinuity design.Methods:We utilized influenza-like illness(ILI)percentage as an indicator of influenza activity,with data from all sentinel hospitals between Week 4,2020,and Week 51,2023.The data is divided into two groups:the COVID-19 epidemic period and the post-epidemic period.Statistical analysis was performed with R software using robust RD design methods to account for potential confounders including seasonality,temperature,and influenza vaccination rates.Results:There was a discernible increase in the ILI percentage during the post-epidemic period.The robustness of the findings was confirmed with various RD design bandwidth selection methods and placebo tests,with certwo bandwidth providing the largest estimated effect size:a 14.6-percentage-point increase in the ILI percentage(β=0.146;95%CI:0.096–0.196).Sensitivity analyses and adjustments for confounders consistently pointed to an increased ILI percentage during the post-epidemic period compared to the epidemic period.Conclusion:The 14.6 percentage-point increase in the ILI percentage in Fujian Province,China,after the end of the COVID-19 pandemic suggests that there may be a need to re-evaluate and possibly enhance public health measures to control influenza transmission.Further research is needed to fully understand the factors contributing to this rise and to assess the ongoing impacts of post-pandemic behavioral changes.展开更多
This opinion article discusses the original research work of Yünkül et al.(the Authors)published in the Journal of Mountain Science 21(9):3108–3122.Employing non-linear regression,fuzzy logic and artificial...This opinion article discusses the original research work of Yünkül et al.(the Authors)published in the Journal of Mountain Science 21(9):3108–3122.Employing non-linear regression,fuzzy logic and artificial neural network modeling techniques,the Authors interrogated a large database assembled from the existing research literature to assess the performance of twelve equation rules in predicting the undrained shear strength(s_(u))mobilized for remolded fine-grained soils at different values of liquidity index(I_(L))and water content ratio.Based on their analyses,the Authors proposed a simple and reportedly reliable correlation(i.e.,Eq.9 in their paper)for predicting s_(u) over the I_(L) range of 0.15 to 3.00.This article describes various shortcomings in the Authors’assembled database(including potentially anomalous data and covering an excessively wide I_(L) range in relation to routine geotechnical and transportation engineering applications)and their proposed s_(u)=f(I_(L))correlation.Contrary to the Authors’assertions,their proposed correlation is not reliable for fine-grained soils with consistencies in the general firm to stiff range(i.e.,for 0.15<I_(L)<0.40),increasingly overestimating s_(u) for reducing I_(L),and eventually predicting s_(u)→+∞for I_(L)→0.15+(while producing mathematically undefined s_(u) for I_(L)<0.15),thus rendering their correlation unconservative and potentially leading to unsafe geotechnical designs.Exponential or regular-power type s_(u)=f(I_(L))models are more s_(u)itable when developing correlations that are applicable over the full plastic range(of 0<I_(L)<1),thereby providing reasonably conservative s_(u) predictions for use in the preliminary design for routine geotechnical engineering applications.展开更多
In clinical research,subgroup analysis can help identify patient groups that respond better or worse to specific treatments,improve therapeutic effect and safety,and is of great significance in precision medicine.This...In clinical research,subgroup analysis can help identify patient groups that respond better or worse to specific treatments,improve therapeutic effect and safety,and is of great significance in precision medicine.This article considers subgroup analysis methods for longitudinal data containing multiple covariates and biomarkers.We divide subgroups based on whether a linear combination of these biomarkers exceeds a predetermined threshold,and assess the heterogeneity of treatment effects across subgroups using the interaction between subgroups and exposure variables.Quantile regression is used to better characterize the global distribution of the response variable and sparsity penalties are imposed to achieve variable selection of covariates and biomarkers.The effectiveness of our proposed methodology for both variable selection and parameter estimation is verified through random simulations.Finally,we demonstrate the application of this method by analyzing data from the PA.3 trial,further illustrating the practicality of the method proposed in this paper.展开更多
Branch size is a crucial characteristic,closely linked to both tree growth and wood quality.A review of existing branch size models reveals various approaches,but the ability to estimate branch diameter and length wit...Branch size is a crucial characteristic,closely linked to both tree growth and wood quality.A review of existing branch size models reveals various approaches,but the ability to estimate branch diameter and length within the same whorl remains underexplored.In this study,a total of 77 trees were sampled from Northeast China to model the vertical distribution of branch diameter and length within each whorl along the crown.Several commonly used functions were taken as the alternative model forms,and the quantile regression method was employed and compared with the classical two-step modeling approach.The analysis incorporated stand,tree,and competition factors,with a particular focus on how these factors influence branches of varying sizes.The modified Weibull function was chosen as the optimal model,due to its excellent performance across all quantiles.Eight quantile regression curves(ranging from 0.20 to 0.85)were combined to predict branch diameter,while seven curves(ranging from 0.20 to 0.80)were used for branch length.The results showed that the quantile regression method outperformed the classical approach at model fitting and validation,likely due to its ability to estimate different rates of change across the entire branch size distribution.Lager branches in each whorl were more sensitive to changes in DBH,crown length(CL),crown ratio(CR)and dominant tree height(H_(dom)),while slenderness(HDR)more effectively influenced small and medium-sized branches.The effect of stand basal area(BAS)was relatively consistent across different branch sizes.The findings indicate that quantile regression is a good way not only a more accurate method for predicting branch size but also a valuable tool for understanding how branch growth responds to stand and tree factors.The models developed in this study are prepared to be further integrated into tree growth and yield simulation system,contributing to the assessment and promotion of wood quality.展开更多
Global Navigation Satellite System(GNSS)imaging method(GIM)has been successfully applied to global regions to investigate vertical land motion(VLM)of the Earth's surface.GNSS images derived from conventional GIM m...Global Navigation Satellite System(GNSS)imaging method(GIM)has been successfully applied to global regions to investigate vertical land motion(VLM)of the Earth's surface.GNSS images derived from conventional GIM method may present fragmented patches and encounter problems caused by excessive smoothing of velocity peaks,leading to difficulty in short-wavelength deformation detection and improper geophysical interpretation.Therefore,we propose a novel GNSS imaging method based on Gaussian process regression with velocity uncertainty considered(GPR-VU).Gaussian processing regression is introduced to describe the spatial relationship between neighboring site pairs as a priori weights and then reweight velocities by known station uncertainties,converting the discrete velocity field to a continuous one.The GPR-VU method is applied to reconstruct VLM images in the southwestern United States and the eastern Qinghai-Xizang Plateau,China,using the GNSS position time series in vertical direction.Compared to the traditional GIM method,the root-mean-square(RMS)and overall accuracy of the confusion matrix of the GPR-VU method increase by 5.0%and 14.0%from the 1°×1°checkerboard test in the southwestern United States.Similarly,the RMS and overall accuracy increase by 33.7%and 15.8%from the 6°×6°checkerboard test in the eastern Qinghai-Xizang Plateau.These checkerboard tests validate the capability to effectively capture the spatiotemporal variations characteristics of VLM and show that this algorithm outperforms the sparsely distributed network in the Qinghai-Xizang Plateau.The images from the GPR-VU method using real data in both regions show significant subsidence around Lassen Volcanic in northern California within a 30 km radius,slight uplift in the northern Sichuan Basin,and subsidence in its central and southern sections.These results further qualitatively illustrate consistency with previous findings.The GPR-VU method outperforms in diminishing the effect by fragmented patches,excessive smoothing of velocity peaks,and detecting potential short-wavelength deformations.展开更多
The packaging quality of coaxial laser diodes(CLDs)plays a pivotal role in determining their optical performance and long-term reliability.As the core packaging process,high-precision laser welding requires precise co...The packaging quality of coaxial laser diodes(CLDs)plays a pivotal role in determining their optical performance and long-term reliability.As the core packaging process,high-precision laser welding requires precise control of process parameters to suppress optical power loss.However,the complex nonlinear relationship between welding parameters and optical power loss renders traditional trial-and-error methods inefficient and imprecise.To address this challenge,a physics-informed(PI)and data-driven collaboration approach for welding parameter optimization is proposed.First,thermal-fluid-solid coupling finite element method(FEM)was employed to quantify the sensitivity of welding parameters to physical characteristics,including residual stress.This analysis facilitated the identification of critical factors contributing to optical power loss.Subsequently,a Gaussian process regression(GPR)model incorporating finite element simulation prior knowledge was constructed based on the selected features.By introducing physics-informed kernel(PIK)functions,stress distribution patterns were embedded into the prediction model,achieving high-precision optical power loss prediction.Finally,a Bayesian optimization(BO)algorithm with an adaptive sampling strategy was implemented for efficient parameter space exploration.Experimental results demonstrate that the proposedmethod effectively establishes explicit physical correlations between welding parameters and optical power loss.The optimized welding parameters reduced optical power loss by 34.1%,providing theoretical guidance and technical support for reliable CLD packaging.展开更多
文摘The pricing of moving window Asian option with an early exercise feature is considered a challenging problem in option pricing. The computational challenge lies in the unknown optimal exercise strategy and in the high dimensionality required for approximating the early exercise boundary. We use sparse grid basis functions in the Least Squares Monte Carlo approach to solve this “curse of dimensionality” problem. The resulting algorithm provides a general and convergent method for pricing moving window Asian options. The sparse grid technique presented in this paper can be generalized to pricing other high-dimensional, early-exercisable derivatives.
基金Supported by the Hangzhou Joint Fund of the Zhejiang Provincial Natural Science Foundation of Chi-na(LHZY24A010002)the MOE Project of Humanities and Social Sciences(21YJCZH235).
文摘High-dimensional heterogeneous data have acquired increasing attention and discussion in the past decade.In the context of heterogeneity,semiparametric regression emerges as a popular method to model this type of data in statistics.In this paper,we leverage the benefits of expectile regression for computational efficiency and analytical robustness in heterogeneity,and propose a regularized partially linear additive expectile regression model with a nonconvex penalty,such as SCAD or MCP,for high-dimensional heterogeneous data.We focus on a more realistic scenario where the regression error exhibits a heavy-tailed distribution with only finite moments.This scenario challenges the classical sub-gaussian distribution assumption and is more prevalent in practical applications.Under certain regular conditions,we demonstrate that with probability tending to one,the oracle estimator is one of the local minima of the induced optimization problem.Our theoretical analysis suggests that the dimensionality of linear covariates that our estimation procedure can handle is fundamentally limited by the moment condition of the regression error.Computationally,given the nonconvex and nonsmooth nature of the induced optimization problem,we have developed a two-step algorithm.Finally,our method’s effectiveness is demonstrated through its high estimation accuracy and effective model selection,as evidenced by Monte Carlo simulation studies and a real-data application.Furthermore,by taking various expectile weights,our method effectively detects heterogeneity and explores the complete conditional distribution of the response variable,underscoring its utility in analyzing high-dimensional heterogeneous data.
基金funded by National Natural Science Foundation of China(Nos.12402142,11832013 and 11572134)Natural Science Foundation of Hubei Province(No.2024AFB235)+1 种基金Hubei Provincial Department of Education Science and Technology Research Project(No.Q20221714)the Opening Foundation of Hubei Key Laboratory of Digital Textile Equipment(Nos.DTL2023019 and DTL2022012).
文摘Owing to their global search capabilities and gradient-free operation,metaheuristic algorithms are widely applied to a wide range of optimization problems.However,their computational demands become prohibitive when tackling high-dimensional optimization challenges.To effectively address these challenges,this study introduces cooperative metaheuristics integrating dynamic dimension reduction(DR).Building upon particle swarm optimization(PSO)and differential evolution(DE),the proposed cooperative methods C-PSO and C-DE are developed.In the proposed methods,the modified principal components analysis(PCA)is utilized to reduce the dimension of design variables,thereby decreasing computational costs.The dynamic DR strategy implements periodic execution of modified PCA after a fixed number of iterations,resulting in the important dimensions being dynamically identified.Compared with the static one,the dynamic DR strategy can achieve precise identification of important dimensions,thereby enabling accelerated convergence toward optimal solutions.Furthermore,the influence of cumulative contribution rate thresholds on optimization problems with different dimensions is investigated.Metaheuristic algorithms(PSO,DE)and cooperative metaheuristics(C-PSO,C-DE)are examined by 15 benchmark functions and two engineering design problems(speed reducer and composite pressure vessel).Comparative results demonstrate that the cooperative methods achieve significantly superior performance compared to standard methods in both solution accuracy and computational efficiency.Compared to standard metaheuristic algorithms,cooperative metaheuristics achieve a reduction in computational cost of at least 40%.The cooperative metaheuristics can be effectively used to tackle both high-dimensional unconstrained and constrained optimization problems.
基金supported by National Science Foundation of USA(Grant No.DMS1811812)supported by National Science Foundation of USA(Grant No.DMS-2015498)National Institutes of Health of USA(Grant Nos.R01GM117597 and R01GM126089)。
文摘During the past decade,shrinkage priors have received much attention in Bayesian analysis of high-dimensional data.This paper establishes the posterior consistency for high-dimensional linear regression with a class of shrinkage priors,which has a heavy and flat tail and allocates a sufficiently large probability mass in a very small neighborhood of zero.While enjoying its efficiency in posterior simulations,the shrinkage prior can lead to a nearly optimal posterior contraction rate and the variable selection consistency as the spike-and-slab prior.Our numerical results show that under the posterior consistency,Bayesian methods can yield much better results in variable selection than the regularization methods such as LASSO and SCAD.This paper also establishes a BvM-type result,which leads to a convenient way of uncertainty quantification for regression coefficient estimates.
基金Supported by the National Natural Science Foundation of China(Grant No.11401340)China Postdoctoral Science Foundation(Grant No.2014M561892)+1 种基金the Foundation of Qufu Normal University(Grant Nos.bsqd2012041xkj201304)
文摘Nonconvex penalties including the smoothly clipped absolute deviation penalty and the minimax concave penalty enjoy the properties of unbiasedness, continuity and sparsity,and the ridge regression can deal with the collinearity problem. Combining the strengths of nonconvex penalties and ridge regression（abbreviated as NPR）, we study the oracle property of the NPR estimator in high dimensional settings with highly correlated predictors, where the dimensionality of covariates pn is allowed to increase exponentially with the sample size n. Simulation studies and a real data example are presented to verify the performance of the NPR method.
基金supported by DST-FIST(Government of India)(Grant No.SR/FIST/MS-1/2017/13)and Seed Money Project(Grant No.DoRDC/733).
文摘This study numerically examines the heat and mass transfer characteristics of two ternary nanofluids via converging and diverg-ing channels.Furthermore,the study aims to assess two ternary nanofluids combinations to determine which configuration can provide better heat and mass transfer and lower entropy production,while ensuring cost efficiency.This work bridges the gap be-tween academic research and industrial feasibility by incorporating cost analysis,entropy generation,and thermal efficiency.To compare the velocity,temperature,and concentration profiles,we examine two ternary nanofluids,i.e.,TiO_(2)+SiO_(2)+Al_(2)O_(3)/H_(2)O and TiO_(2)+SiO_(2)+Cu/H_(2)O,while considering the shape of nanoparticles.The velocity slip and Soret/Dufour effects are taken into consideration.Furthermore,regression analysis for Nusselt and Sherwood numbers of the model is carried out.The Runge-Kutta fourth-order method with shooting technique is employed to acquire the numerical solution of the governed system of ordinary differential equations.The flow pattern attributes of ternary nanofluids are meticulously examined and simulated with the fluc-tuation of flow-dominating parameters.Additionally,the influence of these parameters is demonstrated in the flow,temperature,and concentration fields.For variation in Eckert and Dufour numbers,TiO_(2)+SiO_(2)+Al_(2)O_(3)/H_(2)O has a higher temperature than TiO_(2)+SiO_(2)+Cu/H_(2)O.The results obtained indicate that the ternary nanofluid TiO_(2)+SiO_(2)+Al_(2)O_(3)/H_(2)O has a higher heat transfer rate,lesser entropy generation,greater mass transfer rate,and lower cost than that of TiO_(2)+SiO_(2)+Cu/H_(2)O ternary nanofluid.
文摘In this study,we examine the problem of sliced inverse regression(SIR),a widely used method for sufficient dimension reduction(SDR).It was designed to find reduced-dimensional versions of multivariate predictors by replacing them with a minimally adequate collection of their linear combinations without loss of information.Recently,regularization methods have been proposed in SIR to incorporate a sparse structure of predictors for better interpretability.However,existing methods consider convex relaxation to bypass the sparsity constraint,which may not lead to the best subset,and particularly tends to include irrelevant variables when predictors are correlated.In this study,we approach sparse SIR as a nonconvex optimization problem and directly tackle the sparsity constraint by establishing the optimal conditions and iteratively solving them by means of the splicing technique.Without employing convex relaxation on the sparsity constraint and the orthogonal constraint,our algorithm exhibits superior empirical merits,as evidenced by extensive numerical studies.Computationally,our algorithm is much faster than the relaxed approach for the natural sparse SIR estimator.Statistically,our algorithm surpasses existing methods in terms of accuracy for central subspace estimation and best subset selection and sustains high performance even with correlated predictors.
基金funded by the INTER program and cofunded by the Fond National de la Recherche,Luxembourg(FNR)and the Fund for Scientific Research-FNRS,Belgium(F.R.S-FNRS),T.0233.20-‘Sustainable Residential Densification’project(SusDens,2020–2024).
文摘The impact of different global and local variables in urban development processes requires a systematic study to fully comprehend the underlying complexities in them.The interplay between such variables is crucial for modelling urban growth to closely reflects reality.Despite extensive research,ambiguity remains about how variations in these input variables influence urban densification.In this study,we conduct a global sensitivity analysis(SA)using a multinomial logistic regression(MNL)model to assess the model’s explanatory and predictive power.We examine the influence of global variables,including spatial resolution,neighborhood size,and density classes,under different input combinations at a provincial scale to understand their impact on densification.Additionally,we perform a stepwise regression to identify the significant explanatory variables that are important for understanding densification in the Brussels Metropolitan Area(BMA).Our results indicate that a finer spatial resolution of 50 m and 100 m,smaller neighborhood size of 5×5 and 3×3,and specific density classes—namely 3(non-built-up,low and high built-up)and 4(non-built-up,low,medium and high built-up)—optimally explain and predict urban densification.In line with the same,the stepwise regression reveals that models with a coarser resolution of 300 m lack significant variables,reflecting a lower explanatory power for densification.This approach aids in identifying optimal and significant global variables with higher explanatory power for understanding and predicting urban densification.Furthermore,these findings are reproducible in a global urban context,offering valuable insights for planners,modelers and geographers in managing future urban growth and minimizing modelling.
文摘Triaxial tests,a staple in rock engineering,are labor-intensive,sample-demanding,and costly,making their optimization highly advantageous.These tests are essential for characterizing rock strength,and by adopting a failure criterion,they allow for the derivation of criterion parameters through regression,facilitating their integration into modeling programs.In this study,we introduce the application of an underutilized statistical technique—orthogonal regression—well-suited for analyzing triaxial test data.Additionally,we present an innovation in this technique by minimizing the Euclidean distance while incorporating orthogonality between vectors as a constraint,for the case of orthogonal linear regression.Also,we consider the Modified Least Squares method.We exemplify this approach by developing the necessary equations to apply the Mohr-Coulomb,Murrell,Hoek-Brown,andÚcar criteria,and implement these equations in both spreadsheet calculations and R scripts.Finally,we demonstrate the technique's application using five datasets of varied lithologies from specialized literature,showcasing its versatility and effectiveness.
基金supported by the Project of the Hubei Provincial Department of Science and Technology(Grant Nos.2022CFB957,2022CFB475)the National Natural Science Foundation of China(Grant No.11847118)。
文摘The decoherence of high-dimensional orbital angular momentum(OAM)entanglement in the weak scintillation regime has been investigated.In this study,we simulate atmospheric turbulence by utilizing a multiple-phase screen imprinted with anisotropic non-Kolmogorov turbulence.The entanglement negativity and fidelity are introduced to quantify the entanglement of a high-dimensional OAM state.The numerical evaluation results indicate that entanglement negativity and fidelity last longer for a high-dimensional OAM state when the azimuthal mode has a lower value.Additionally,the evolution of higher-dimensional OAM entanglement is significantly influenced by OAM beam parameters and turbulence parameters.Compared to isotropic atmospheric turbulence,anisotropic turbulence has a lesser influence on highdimensional OAM entanglement.
基金Supported by the National Natural Science Foundation of China(12201446)the Natural Science Foundation of the Jiangsu Higher Education Institutions of China(22KJB110005)the Shuangchuang Program of Jiangsu Province(JSSCBS20220898)。
文摘It is known that monotone recurrence relations can induce a class of twist homeomorphisms on the high-dimensional cylinder,which is an extension of the class of monotone twist maps on the annulus or two-dimensional cylinder.By constructing a bounded solution of the monotone recurrence relation,the main conclusion in this paper is acquired:The induced homeomorphism has Birkhoff orbits provided there is a compact forward-invariant set.Therefore,it generalizes Angenent's results in low-dimensional cases.
基金supported by the Natural Science Foundation of Shanghai(23ZR1463600)Shanghai Pudong New Area Health Commission Research Project(PW2021A-69)Research Project of Clinical Research Center of Shanghai Health Medical University(22MC2022002)。
文摘Gastric cancer is the third leading cause of cancer-related mortality and remains a major global health issue^([1]).Annually,approximately 479,000individuals in China are diagnosed with gastric cancer,accounting for almost 45%of all new cases worldwide^([2]).
基金supported in part by the Young Scientists Fund of the National Natural Science Foundation of China(Grant Nos.82304253)(and 82273709)the Foundation for Young Talents in Higher Education of Guangdong Province(Grant No.2022KQNCX021)the PhD Starting Project of Guangdong Medical University(Grant No.GDMUB2022054).
文摘Objective Humans are exposed to complex mixtures of environmental chemicals and other factors that can affect their health.Analysis of these mixture exposures presents several key challenges for environmental epidemiology and risk assessment,including high dimensionality,correlated exposure,and subtle individual effects.Methods We proposed a novel statistical approach,the generalized functional linear model(GFLM),to analyze the health effects of exposure mixtures.GFLM treats the effect of mixture exposures as a smooth function by reordering exposures based on specific mechanisms and capturing internal correlations to provide a meaningful estimation and interpretation.The robustness and efficiency was evaluated under various scenarios through extensive simulation studies.Results We applied the GFLM to two datasets from the National Health and Nutrition Examination Survey(NHANES).In the first application,we examined the effects of 37 nutrients on BMI(2011–2016 cycles).The GFLM identified a significant mixture effect,with fiber and fat emerging as the nutrients with the greatest negative and positive effects on BMI,respectively.For the second application,we investigated the association between four pre-and perfluoroalkyl substances(PFAS)and gout risk(2007–2018 cycles).Unlike traditional methods,the GFLM indicated no significant association,demonstrating its robustness to multicollinearity.Conclusion GFLM framework is a powerful tool for mixture exposure analysis,offering improved handling of correlated exposures and interpretable results.It demonstrates robust performance across various scenarios and real-world applications,advancing our understanding of complex environmental exposures and their health impacts on environmental epidemiology and toxicology.
基金supported by the National Natural Science Foundation of China(No.92370117)the CAS Project for Young Scientists in Basic Research(No.YSBR-090)。
文摘In recent years,machine learning(ML)techniques have been shown to be effective in accelerating the development process of optoelectronic devices.However,as"black box"models,they have limited theoretical interpretability.In this work,we leverage symbolic regression(SR)technique for discovering the explicit symbolic relationship between the structure of the optoelectronic Fabry-Perot(FP)laser and its optical field distribution,which greatly improves model transparency compared to ML.We demonstrated that the expressions explored through SR exhibit lower errors on the test set compared to ML models,which suggests that the expressions have better fitting and generalization capabilities.
基金supported by the Youth Scientific Research Project of Fujian Provincial Center for Disease Control and Prevention(2022QN02)the Fujian Provincial Health Youth Scientific Research Project(2023QNA040).
文摘Background:The COVID-1’s impact on influenza activity is of interest to inform future flu prevention and control strategies.Our study aim to examine COVID-19’s effects on influenza in Fujian Province,China,using a regression discontinuity design.Methods:We utilized influenza-like illness(ILI)percentage as an indicator of influenza activity,with data from all sentinel hospitals between Week 4,2020,and Week 51,2023.The data is divided into two groups:the COVID-19 epidemic period and the post-epidemic period.Statistical analysis was performed with R software using robust RD design methods to account for potential confounders including seasonality,temperature,and influenza vaccination rates.Results:There was a discernible increase in the ILI percentage during the post-epidemic period.The robustness of the findings was confirmed with various RD design bandwidth selection methods and placebo tests,with certwo bandwidth providing the largest estimated effect size:a 14.6-percentage-point increase in the ILI percentage(β=0.146;95%CI:0.096–0.196).Sensitivity analyses and adjustments for confounders consistently pointed to an increased ILI percentage during the post-epidemic period compared to the epidemic period.Conclusion:The 14.6 percentage-point increase in the ILI percentage in Fujian Province,China,after the end of the COVID-19 pandemic suggests that there may be a need to re-evaluate and possibly enhance public health measures to control influenza transmission.Further research is needed to fully understand the factors contributing to this rise and to assess the ongoing impacts of post-pandemic behavioral changes.
文摘This opinion article discusses the original research work of Yünkül et al.(the Authors)published in the Journal of Mountain Science 21(9):3108–3122.Employing non-linear regression,fuzzy logic and artificial neural network modeling techniques,the Authors interrogated a large database assembled from the existing research literature to assess the performance of twelve equation rules in predicting the undrained shear strength(s_(u))mobilized for remolded fine-grained soils at different values of liquidity index(I_(L))and water content ratio.Based on their analyses,the Authors proposed a simple and reportedly reliable correlation(i.e.,Eq.9 in their paper)for predicting s_(u) over the I_(L) range of 0.15 to 3.00.This article describes various shortcomings in the Authors’assembled database(including potentially anomalous data and covering an excessively wide I_(L) range in relation to routine geotechnical and transportation engineering applications)and their proposed s_(u)=f(I_(L))correlation.Contrary to the Authors’assertions,their proposed correlation is not reliable for fine-grained soils with consistencies in the general firm to stiff range(i.e.,for 0.15<I_(L)<0.40),increasingly overestimating s_(u) for reducing I_(L),and eventually predicting s_(u)→+∞for I_(L)→0.15+(while producing mathematically undefined s_(u) for I_(L)<0.15),thus rendering their correlation unconservative and potentially leading to unsafe geotechnical designs.Exponential or regular-power type s_(u)=f(I_(L))models are more s_(u)itable when developing correlations that are applicable over the full plastic range(of 0<I_(L)<1),thereby providing reasonably conservative s_(u) predictions for use in the preliminary design for routine geotechnical engineering applications.
基金Supported by the Natural Science Foundation of Fujian Province(2022J011177,2024J01903)the Key Project of Fujian Provincial Education Department(JZ230054)。
文摘In clinical research,subgroup analysis can help identify patient groups that respond better or worse to specific treatments,improve therapeutic effect and safety,and is of great significance in precision medicine.This article considers subgroup analysis methods for longitudinal data containing multiple covariates and biomarkers.We divide subgroups based on whether a linear combination of these biomarkers exceeds a predetermined threshold,and assess the heterogeneity of treatment effects across subgroups using the interaction between subgroups and exposure variables.Quantile regression is used to better characterize the global distribution of the response variable and sparsity penalties are imposed to achieve variable selection of covariates and biomarkers.The effectiveness of our proposed methodology for both variable selection and parameter estimation is verified through random simulations.Finally,we demonstrate the application of this method by analyzing data from the PA.3 trial,further illustrating the practicality of the method proposed in this paper.
基金supported by the Young Scientists Fund of the National Key R&D Program of China(No.2022YFD2201800)the Youth Science Fund Program of National Natural Science Foundation of China(No.32301581)+2 种基金the Joint Funds for Regional Innovation and Development of the National Natural Science Foundation of China(No.U21A20244)the China Postdoctoral Science Foundation(No.2024M750383)the Heilongjiang Touyan Innovation Team Program(Technology Development Team for High-Efficiency Silviculture of Forest Resources).
文摘Branch size is a crucial characteristic,closely linked to both tree growth and wood quality.A review of existing branch size models reveals various approaches,but the ability to estimate branch diameter and length within the same whorl remains underexplored.In this study,a total of 77 trees were sampled from Northeast China to model the vertical distribution of branch diameter and length within each whorl along the crown.Several commonly used functions were taken as the alternative model forms,and the quantile regression method was employed and compared with the classical two-step modeling approach.The analysis incorporated stand,tree,and competition factors,with a particular focus on how these factors influence branches of varying sizes.The modified Weibull function was chosen as the optimal model,due to its excellent performance across all quantiles.Eight quantile regression curves(ranging from 0.20 to 0.85)were combined to predict branch diameter,while seven curves(ranging from 0.20 to 0.80)were used for branch length.The results showed that the quantile regression method outperformed the classical approach at model fitting and validation,likely due to its ability to estimate different rates of change across the entire branch size distribution.Lager branches in each whorl were more sensitive to changes in DBH,crown length(CL),crown ratio(CR)and dominant tree height(H_(dom)),while slenderness(HDR)more effectively influenced small and medium-sized branches.The effect of stand basal area(BAS)was relatively consistent across different branch sizes.The findings indicate that quantile regression is a good way not only a more accurate method for predicting branch size but also a valuable tool for understanding how branch growth responds to stand and tree factors.The models developed in this study are prepared to be further integrated into tree growth and yield simulation system,contributing to the assessment and promotion of wood quality.
基金supported by the National Natural Science Foundation of China(Grant No.42274035)the Major Science and Technology Program for Hubei Province(No.2022AAA002)the Hunan Provincial Land Surveying and Mapping Project(HNGTCH-2023-05)。
文摘Global Navigation Satellite System(GNSS)imaging method(GIM)has been successfully applied to global regions to investigate vertical land motion(VLM)of the Earth's surface.GNSS images derived from conventional GIM method may present fragmented patches and encounter problems caused by excessive smoothing of velocity peaks,leading to difficulty in short-wavelength deformation detection and improper geophysical interpretation.Therefore,we propose a novel GNSS imaging method based on Gaussian process regression with velocity uncertainty considered(GPR-VU).Gaussian processing regression is introduced to describe the spatial relationship between neighboring site pairs as a priori weights and then reweight velocities by known station uncertainties,converting the discrete velocity field to a continuous one.The GPR-VU method is applied to reconstruct VLM images in the southwestern United States and the eastern Qinghai-Xizang Plateau,China,using the GNSS position time series in vertical direction.Compared to the traditional GIM method,the root-mean-square(RMS)and overall accuracy of the confusion matrix of the GPR-VU method increase by 5.0%and 14.0%from the 1°×1°checkerboard test in the southwestern United States.Similarly,the RMS and overall accuracy increase by 33.7%and 15.8%from the 6°×6°checkerboard test in the eastern Qinghai-Xizang Plateau.These checkerboard tests validate the capability to effectively capture the spatiotemporal variations characteristics of VLM and show that this algorithm outperforms the sparsely distributed network in the Qinghai-Xizang Plateau.The images from the GPR-VU method using real data in both regions show significant subsidence around Lassen Volcanic in northern California within a 30 km radius,slight uplift in the northern Sichuan Basin,and subsidence in its central and southern sections.These results further qualitatively illustrate consistency with previous findings.The GPR-VU method outperforms in diminishing the effect by fragmented patches,excessive smoothing of velocity peaks,and detecting potential short-wavelength deformations.
基金funded by the National Key R&D Program of China,Grant No.2024YFF0504904.
文摘The packaging quality of coaxial laser diodes(CLDs)plays a pivotal role in determining their optical performance and long-term reliability.As the core packaging process,high-precision laser welding requires precise control of process parameters to suppress optical power loss.However,the complex nonlinear relationship between welding parameters and optical power loss renders traditional trial-and-error methods inefficient and imprecise.To address this challenge,a physics-informed(PI)and data-driven collaboration approach for welding parameter optimization is proposed.First,thermal-fluid-solid coupling finite element method(FEM)was employed to quantify the sensitivity of welding parameters to physical characteristics,including residual stress.This analysis facilitated the identification of critical factors contributing to optical power loss.Subsequently,a Gaussian process regression(GPR)model incorporating finite element simulation prior knowledge was constructed based on the selected features.By introducing physics-informed kernel(PIK)functions,stress distribution patterns were embedded into the prediction model,achieving high-precision optical power loss prediction.Finally,a Bayesian optimization(BO)algorithm with an adaptive sampling strategy was implemented for efficient parameter space exploration.Experimental results demonstrate that the proposedmethod effectively establishes explicit physical correlations between welding parameters and optical power loss.The optimized welding parameters reduced optical power loss by 34.1%,providing theoretical guidance and technical support for reliable CLD packaging.
