In this paper,we consider the limit distribution of the error density function estima-tor in the rst-order autoregressive models with negatively associated and positively associated random errors.Under mild regularity...In this paper,we consider the limit distribution of the error density function estima-tor in the rst-order autoregressive models with negatively associated and positively associated random errors.Under mild regularity assumptions,some asymptotic normality results of the residual density estimator are obtained when the autoregressive models are stationary process and explosive process.In order to illustrate these results,some simulations such as con dence intervals and mean integrated square errors are provided in this paper.It shows that the residual density estimator can replace the density\estimator"which contains errors.展开更多
Wavelets are applied to detection of the jump points of a regression function in nonlinear autoregressive model x(t) = T(x(t-1)) + epsilon t. By checking the empirical wavelet coefficients of the data,which have signi...Wavelets are applied to detection of the jump points of a regression function in nonlinear autoregressive model x(t) = T(x(t-1)) + epsilon t. By checking the empirical wavelet coefficients of the data,which have significantly large absolute values across fine scale levels, the number of the jump points and locations where the jumps occur are estimated. The jump heights are also estimated. All estimators are shown to be consistent. Wavelet method ia also applied to the threshold AR(1) model(TAR(1)). The simple estimators of the thresholds are given,which are shown to be consistent.展开更多
Change monitoring of distribution in time series models is an important issue. This paper proposes a procedure for monitoring changes in the error distribution of autoregressive time series, which is based on a weighe...Change monitoring of distribution in time series models is an important issue. This paper proposes a procedure for monitoring changes in the error distribution of autoregressive time series, which is based on a weighed empirical process of residuals with weights equal to the regressors. The asymptotic properties of our monitoring statistic are derived under the null hypothesis of no change in distribution. The finite sample properties are investigated by a simulation. As it turns out, the procedure is not only able to detect distributional changes but also changes in the regression coefficient and mean, Finally, we apply the statistic to a groups of financial data.展开更多
Consider the model Yt = βYt-1＋g（Yt-2）＋εt for 3 〈 t 〈 T. Hereg is anunknown function, β is an unknown parameter, εt are i.i.d, random errors with mean 0 andvariance σ2 and the fourth moment α4, and α4 are ...Consider the model Yt = βYt-1＋g（Yt-2）＋εt for 3 〈 t 〈 T. Hereg is anunknown function, β is an unknown parameter, εt are i.i.d, random errors with mean 0 andvariance σ2 and the fourth moment α4, and α4 are independent of Y8 for all t ≥ 3 and s = 1, 2.Pseudo-LS estimators σ, σ2T α4τ and D2T of σ^2,α4 and Var（ε2↑3） are respectively constructedbased on piecewise polynomial approximator of g. The weak consistency of α4T and D2T are proved. The asymptotic normality of σ2T is given, i.e., √T（σ2T -σ^2）/DT converges indistribution to N（0, 1）. The result can be used to establish large sample interval estimatesof σ^2 or to make large sample tests for σ^2.展开更多
In this article,we study the empirical likelihood(EL)method for autoregressive models with spatial errors.The EL ratio statistics are constructed for the parameters of the models.It is shown that the limiting distribu...In this article,we study the empirical likelihood(EL)method for autoregressive models with spatial errors.The EL ratio statistics are constructed for the parameters of the models.It is shown that the limiting distributions of the EL ratio statistics are chi-square distributions,which are used to construct confidence intervals for the parameters of the models.A simulation study is conducted to compare the performances of the EL based and the normal approximation(NA)based confidence intervals.Simulation results show that the confidence intervals based on EL are superior to the NA based confidence intervals.展开更多
The energy sector is the second largest emitter of greenhouse (GHG) gases in Kenya, emitting about 31.2% of GHG emissions in the country. The aim of this study was to model Kenya’s GHG emissions by the energy sector ...The energy sector is the second largest emitter of greenhouse (GHG) gases in Kenya, emitting about 31.2% of GHG emissions in the country. The aim of this study was to model Kenya’s GHG emissions by the energy sector using ARIMA models for forecasting future values. The data used for the study was that of Kenya’s GHG emissions by the energy sector for the period starting from 1970 to 2022 obtained for the International Monetary Fund (IMF) database that was split into training and testing sets using the 80/20 rule for modelling purposes. The best specification for the ARIMA model was identified using Akaike Information Criterion (AIC), root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and mean absolute scaled error (MASE). ARIMA (1, 1, 1) was identified as the best model for modelling Kenya’s GHG emissions and forecasting future values. Using this model, Kenya’s GHG emissions by the energy sector were forecasted to increase to a value of about 43.13 million metric tons of carbon dioxide equivalents by 2030. The study, therefore, recommends that Kenya should accelerate the adjustment of industry structure and improve the efficient use of energy, optimize the energy structure and accelerate development and promotion of energy-efficient products to reduce the emission of GHGs by the country’s energy sector.展开更多
Kernel-based slow feature analysis(SFA)methods have been successfully applied in the industrial process fault detection field.However,kernel-based SFA methods have high computational complexity as dealing with nonline...Kernel-based slow feature analysis(SFA)methods have been successfully applied in the industrial process fault detection field.However,kernel-based SFA methods have high computational complexity as dealing with nonlinearity,leading to delays in detecting time-varying data features.Additionally,the uncertain kernel function and kernel parameters limit the ability of the extracted features to express process characteristics,resulting in poor fault detection performance.To alleviate the above problems,a novel randomized auto-regressive dynamic slow feature analysis(RRDSFA)method is proposed to simultaneously monitor the operating point deviations and process dynamic faults,enabling real-time monitoring of data features in industrial processes.Firstly,the proposed Random Fourier mappingbased method achieves more effective nonlinear transformation,contrasting with the current kernelbased RDSFA algorithm that may lead to significant computational complexity.Secondly,a randomized RDSFA model is developed to extract nonlinear dynamic slow features.Furthermore,a Bayesian inference-based overall fault monitoring model including all RRDSFA sub-models is developed to overcome the randomness of random Fourier mapping.Finally,the superiority and effectiveness of the proposed monitoring method are demonstrated through a numerical case and a simulation of continuous stirred tank reactor.展开更多
This paper is devoted to the goodness-of-fit test for the general autoregressive models in time series. By averaging for the weighted residuals, we construct a score type test which is asymptotically standard chi-squa...This paper is devoted to the goodness-of-fit test for the general autoregressive models in time series. By averaging for the weighted residuals, we construct a score type test which is asymptotically standard chi-squared under the null and has some desirable power properties under the alternatives. Specifically, the test is sensitive to alternatives and can detect the alternatives approaching, along a direction, the null at a rate that is arbitrarily close to n-1/2. Furthermore, when the alternatives are not directional, we construct asymptotically distribution-free maximin tests for a large class of alternatives. The performance of the tests is evaluated through simulation studies.展开更多
Based on the empirical likelihood method, the subset selection and hypothesis test for parameters in a partially linear autoregressive model are investigated. We show that the empirical log-likelihood ratio at the tru...Based on the empirical likelihood method, the subset selection and hypothesis test for parameters in a partially linear autoregressive model are investigated. We show that the empirical log-likelihood ratio at the true parameters converges to the standard chi-square distribution. We then present the definitions of the empirical likelihood-based Bayes information criteria （EBIC） and Akaike information criteria （EAIC）. The results show that EBIC is consistent at selecting subset variables while EAIC is not. Simulation studies demonstrate that the proposed empirical likelihood confidence regions have better coverage probabilities than the least square method, while EBIC has a higher chance to select the true model than EAIC.展开更多
A self-weighted quantile procedure is proposed to study the inference for a spatial unilateral autoregressive model with independent and identically distributed innovations belonging to the domain of attraction of a s...A self-weighted quantile procedure is proposed to study the inference for a spatial unilateral autoregressive model with independent and identically distributed innovations belonging to the domain of attraction of a stable law with index of stabilityα,α∈(0,2).It is shown that when the model is stationary,the self-weighted quantile estimate of the parameter has a closed form and converges to a normal limiting distribution,which avoids the difficulty of Roknossadati and Zarepour(2010)in deriving their limiting distribution for an M-estimate.On the contrary,we show that when the model is not stationary,the proposed estimates have the same limiting distributions as those of Roknossadati and Zarepour.Furthermore,a Wald test statistic is proposed to consider the test for a linear restriction on the parameter,and it is shown that under a local alternative,the Wald statistic has a non-central chisquared distribution.Simulations and a real data example are also reported to assess the performance of the proposed method.展开更多
We propose a novel polynomial network autoregressive model by incorporating higher-order connected relationships to simultaneously model the effects of both direct and indirect connections. A quasimaximum likelihood e...We propose a novel polynomial network autoregressive model by incorporating higher-order connected relationships to simultaneously model the effects of both direct and indirect connections. A quasimaximum likelihood estimation method is proposed to estimate the unknown influence parameters, and we demonstrate its consistency and asymptotic normality without imposing any distribution assumption. Moreover,an extended Bayesian information criterion is set for order selection with a divergent upper order. The application of the proposed polynomial network autoregressive model is demonstrated through both the simulation and the real data analysis.展开更多
This paper develops the empirical likelihood(EL)inference procedure for parameters in autore-gressive models with the error variances scaled by an unknown nonparametric time-varying function.Compared with existing met...This paper develops the empirical likelihood(EL)inference procedure for parameters in autore-gressive models with the error variances scaled by an unknown nonparametric time-varying function.Compared with existing methods based on non-parametric and semi-parametric esti-mation,the proposed test statistic avoids estimating the variance function,while maintaining the asymptotic chi-square distribution under the null.Simulation studies demonstrate that the proposed EL procedure(a)is more stable,i.e.,depending less on the change points in the error variances,and(b)gets closer to the desired confidence level,than the traditional test statistic.展开更多
Oceanic turbulence measurements made by an acoustic Doppler velocimeter(ADV)suffer from noise that potentially affects the estimates of turbulence statistics.This study examines the abilities of Kalman filtering and a...Oceanic turbulence measurements made by an acoustic Doppler velocimeter(ADV)suffer from noise that potentially affects the estimates of turbulence statistics.This study examines the abilities of Kalman filtering and autoregressive moving average models to eliminate noise in ADV velocity datasets of laboratory experiments and offshore observations.Results show that the two methods have similar performance in ADV de-noising,and both effectively reduce noise in ADV velocities,even in cases of high noise.They eliminate the noise floor at high frequencies of the velocity spectra,leading to a longer range that effectively fits the Kolmogorov-5/3 slope at midrange frequencies.After de-noising adopting the two methods,the values of the mean velocity are almost unchanged,while the root-mean-square horizontal velocities and thus turbulent kinetic energy decrease appreciably in these experiments.The Reynolds stress is also affected by high noise levels,and de-noising thus reduces uncertainties in estimating the Reynolds stress.展开更多
Based on analyzing the limitations of the commonly used back-propagation neural network （BPNN）, a wavelet neural network （WNN） is adopted as the nonlinear river channel flood forecasting method replacing the BPNN....Based on analyzing the limitations of the commonly used back-propagation neural network （BPNN）, a wavelet neural network （WNN） is adopted as the nonlinear river channel flood forecasting method replacing the BPNN. The WNN has the characteristics of fast convergence and improved capability of nonlinear approximation. For the purpose of adapting the timevarying characteristics of flood routing, the WNN is coupled with an AR real-time correction model. The AR model is utilized to calculate the forecast error. The coefficients of the AR real-time correction model are dynamically updated by an adaptive fading factor recursive least square（RLS） method. The application of the flood forecasting method in the cross section of Xijiang River at Gaoyao shows its effectiveness.展开更多
Objective:Near vision loss(NVL)is one of the leading causes of visual impairment worldwide,exerting a profound impact on individual quality of life and socio-economic development.This study aims to analyze the burden ...Objective:Near vision loss(NVL)is one of the leading causes of visual impairment worldwide,exerting a profound impact on individual quality of life and socio-economic development.This study aims to analyze the burden of NVL in China by sex and age groups from 1990 to 2021 and to project trends over the next 15 years.Methods:Using data from the Global Burden of Disease(GBD)2021 database,we conducted descriptive analyses of NVL prevalence in China,calculated age-standardized prevalence rates(ASPR)and age-standardized disability-adjusted life years rates(ASDR)to compare burden differences between sexes and age groups,and applied an autoregressive integrated moving average(ARIMA)model to predict NVL trends for the next 15 years.The model selection was based on best-fit criteria to ensure reliable projections.Results:From 1990 to 2021,China’s ASPR of NVL rose from 10096.24/100000 to 15624.54/100000,and ASDR increased from 101.75/100000 to 158.75/100000.In 2021,ASPR(16551.70/100000)and ASDR(167.69/100000)were higher among females than males(14686.21/100000 and 149.76/100000,respectively).China ranked highest globally in both NVL cases and disability-adjusted life years(DALYs),with female burden significantly exceeding male burden.Projections indicated this trend and sex gap will persist until 2036.Compared with 1990,the prevalence cases and DALYs increased by 239.20%and 238.82%,respectively in 2021,with the highest burden among females and the 55−59 age group.The ARIMA model predicted continued increases in prevalence and DALYs by 2036,with females maintaining a higher burden than males.Conclusion:This study reveals a marked increase in the NVL burden in China and predicts continued growth in the coming years.Public health policies should prioritize NVL prevention and control,with special attention to women and middle-aged populations to mitigate long-term societal and health impacts.展开更多
High-resolution spatiotemporal simulations effectively capture the complexities of atmospheric plume sion disper-in complex terrain.However,their high computational cost makes them impractical for applications requiri...High-resolution spatiotemporal simulations effectively capture the complexities of atmospheric plume sion disper-in complex terrain.However,their high computational cost makes them impractical for applications requiring rapid responses or iterative processes,such as optimization,uncertainty quantification,or inverse modeling.To address this challenge,this work introduces the Dual-Stage Temporal Three-dimensional UNet Super-resolution(DST3D-UNet-SR)model,a highly efficient deep learning model for plume dispersion predictions.DST3D-UNet-SR is composed of two sequential modules:the temporal module(TM),which predicts the transient evolution of a plume in complex terrain from low-resolution temporal data,and the spatial refinement module(SRM),which subsequently enhances the spatial resolution of the TM predictions.We train DST3D-UNet-SR using a comprehensive dataset derived from high-resolution large eddy simulations(LES)of plume transport.We propose the DST3D-UNet-SR model to significantly accelerate LES of three-dimensional(3D)plume dispersion by three orders of magnitude.Additionally,the model demonstrates the ability to dynamically adapt to evolving conditions through the incorporation of new observational data,substantially improving prediction accuracy in high-concentration regions near the source.展开更多
The rapid expansion of tobacco farming poses a significant threat to biodiversity in Yunnan Province,China,a region known for its rich biodiversity.This study aims to understand the trade-offs between tobacco farming ...The rapid expansion of tobacco farming poses a significant threat to biodiversity in Yunnan Province,China,a region known for its rich biodiversity.This study aims to understand the trade-offs between tobacco farming and higher plant species diversity,and to identify priority counties for conservation.We employed an integrated approach combining species distribution modeling,GIS overlay analysis,and empirical spatial regression to em pirically assess the impact of tobacco farming intensity on biodiversity risk.Our findings reveal a compelling negative spatial correlation between tobacco farming expansion and higher plant species diversity.Specifically,southern counties in Wenshan and Honghe prefectures are major priority areas of conservation that exhibit signif icant spatial correlations between biodiversity risks and high tobacco farming intensity.Quantitatively,at county level,a 1%increase in tobacco farming area corresponds to a 0.094%decrease in endemic higher plant species richness across the entire province.These results underscore the need for targeted and region-specific regulations to mitigate biodiversity loss and promote sustainable development in Yunnan Province.The integrated approach used in this study provides a comprehensive assessment of the tobacco-biodiversity trade-offs,offering actionable insights for policymaking.展开更多
Aquila Optimizer(AO)is a recently proposed population-based optimization technique inspired by Aquila’s behavior in catching prey.AO is applied in various applications and its numerous variants were proposed in the l...Aquila Optimizer(AO)is a recently proposed population-based optimization technique inspired by Aquila’s behavior in catching prey.AO is applied in various applications and its numerous variants were proposed in the literature.However,chaos theory has not been extensively investigated in AO.Moreover,it is still not applied in the parameter estimation of electro-hydraulic systems.In this work,ten well-defined chaotic maps were integrated into a narrowed exploitation of AO for the development of a robust chaotic optimization technique.An extensive investigation of twenty-three mathematical benchmarks and ten IEEE Congress on Evolutionary Computation(CEC)functions shows that chaotic Aquila optimization techniques perform better than the baseline technique.The investigation is further conducted on parameter estimation of an electro-hydraulic control system,which is performed on various noise levels and shows that the proposed chaotic AO with Piecewise map(CAO6)achieves the best fitness values of and at noise levels and respectively.Friedman test 2.873E-05,1.014E-04,8.728E-031.300E-03,1.300E-02,1.300E-01,for repeated measures,computational analysis,and Taguchi test reflect the superiority of CAO6 against the state of the arts,demonstrating its potential for addressing various engineering optimization problems.However,the sensitivity to parameter tuning may limit its direct application to complex optimization scenarios.展开更多
We discuss formulas and techniques for finding maximum-likelihood estimators of parameters of autoregressive (with particular emphasis on Markov and Yule) models, computing their asymptotic variance-covariance matrix ...We discuss formulas and techniques for finding maximum-likelihood estimators of parameters of autoregressive (with particular emphasis on Markov and Yule) models, computing their asymptotic variance-covariance matrix and displaying the resulting confidence regions;Monte Carlo simulation is then used to establish the accuracy of the corresponding level of confidence. The results indicate that a direct application of the Central Limit Theorem yields errors too large to be acceptable;instead, we recommend using a technique based directly on the natural logarithm of the likelihood function, verifying its substantially higher accuracy. Our study is then extended to the case of estimating only a subset of a model’s parameters, when the remaining ones (called nuisance) are of no interest to us.展开更多
The use ofrenewable energyisan important way toachieve sustainable agriculturalandeconomic development.However,there are differences in accessto renewable energy between the Global North and Global South.This study ut...The use ofrenewable energyisan important way toachieve sustainable agriculturalandeconomic development.However,there are differences in accessto renewable energy between the Global North and Global South.This study utilisedan autoregressive distributed lag-error correctionmodel and thedata spanning from 1991to 2021 to comparatively analyse the dynamic relationship amongrenewable energy consumption,the value of agricultural production,gross domestic product(GDP),economic diversificationindex,urban population,the total water extraction for agricultural withdrawal,and trade balancein the Netherlands and South Africa.In the shortrun,renewable energy consumption was increased by the value of agricultural productionbut decreased by GDPin South Africa.In the longrun,renewable energy consumption and GDP increased the value of agricultural production,while the value of agricultural production also increased GDP in South Africa.However,in the Netherlands,there was no short-and long-run relationship betweenrenewable energy consumption and agricultural and economic development.The results revealedthat there was a short-and long-run relationship in South Africa.Moreover,in the Netherlands,the adjustment speed was-1.46 forrenewable energy consumption with an error correction of 0.68 a(8.22 months).In South Africa,the adjustment speedwas-1.28 forrenewable energy consumption with an error correction of 0.78 a(9.38 months).Therefore,compared to South Africa,renewable energy consumptionin the Netherlands takes less time to return to balance after a shock.Thesefindings signify different trajectories on sectoral and economic transition initiatives spurred usingrenewable energy between the Netherlands and South Africa.Policy relating to initiatives such as“agro-energy communities”in Global South countries such as South Africa should be emphasised to promote the use of renewable energy in the agricultural sector.展开更多
基金supported by the National Natural Science Foundation of China(12131015,12071422)。
文摘In this paper,we consider the limit distribution of the error density function estima-tor in the rst-order autoregressive models with negatively associated and positively associated random errors.Under mild regularity assumptions,some asymptotic normality results of the residual density estimator are obtained when the autoregressive models are stationary process and explosive process.In order to illustrate these results,some simulations such as con dence intervals and mean integrated square errors are provided in this paper.It shows that the residual density estimator can replace the density\estimator"which contains errors.
文摘Wavelets are applied to detection of the jump points of a regression function in nonlinear autoregressive model x(t) = T(x(t-1)) + epsilon t. By checking the empirical wavelet coefficients of the data,which have significantly large absolute values across fine scale levels, the number of the jump points and locations where the jumps occur are estimated. The jump heights are also estimated. All estimators are shown to be consistent. Wavelet method ia also applied to the threshold AR(1) model(TAR(1)). The simple estimators of the thresholds are given,which are shown to be consistent.
基金Supported by the National Natural Science Foundation of China(Grant No.11301291)the Open Fund of State Key Laboratory of Remote Sensing Science of China(Grant No.OFSLRSS201206)
文摘Change monitoring of distribution in time series models is an important issue. This paper proposes a procedure for monitoring changes in the error distribution of autoregressive time series, which is based on a weighed empirical process of residuals with weights equal to the regressors. The asymptotic properties of our monitoring statistic are derived under the null hypothesis of no change in distribution. The finite sample properties are investigated by a simulation. As it turns out, the procedure is not only able to detect distributional changes but also changes in the regression coefficient and mean, Finally, we apply the statistic to a groups of financial data.
基金Supported by the National Natural Science Foundation of China（60375003） Supported by the Chinese Aviation Foundation（03153059）
文摘Consider the model Yt = βYt-1＋g（Yt-2）＋εt for 3 〈 t 〈 T. Hereg is anunknown function, β is an unknown parameter, εt are i.i.d, random errors with mean 0 andvariance σ2 and the fourth moment α4, and α4 are independent of Y8 for all t ≥ 3 and s = 1, 2.Pseudo-LS estimators σ, σ2T α4τ and D2T of σ^2,α4 and Var（ε2↑3） are respectively constructedbased on piecewise polynomial approximator of g. The weak consistency of α4T and D2T are proved. The asymptotic normality of σ2T is given, i.e., √T（σ2T -σ^2）/DT converges indistribution to N（0, 1）. The result can be used to establish large sample interval estimatesof σ^2 or to make large sample tests for σ^2.
基金supported by the Natural Science Foundation of Guangxi(2022GXNSFAA035556)the National Natural Science Foundation of China(12161009,12061017)+1 种基金Center for Applied Mathematics of Guangxi(Guangxi Normal University)Key Laboratory of Interdisciplinary Research for Data Science,Education Department of Guangxi Zhuang Autonomous Region.
文摘In this article,we study the empirical likelihood(EL)method for autoregressive models with spatial errors.The EL ratio statistics are constructed for the parameters of the models.It is shown that the limiting distributions of the EL ratio statistics are chi-square distributions,which are used to construct confidence intervals for the parameters of the models.A simulation study is conducted to compare the performances of the EL based and the normal approximation(NA)based confidence intervals.Simulation results show that the confidence intervals based on EL are superior to the NA based confidence intervals.
文摘The energy sector is the second largest emitter of greenhouse (GHG) gases in Kenya, emitting about 31.2% of GHG emissions in the country. The aim of this study was to model Kenya’s GHG emissions by the energy sector using ARIMA models for forecasting future values. The data used for the study was that of Kenya’s GHG emissions by the energy sector for the period starting from 1970 to 2022 obtained for the International Monetary Fund (IMF) database that was split into training and testing sets using the 80/20 rule for modelling purposes. The best specification for the ARIMA model was identified using Akaike Information Criterion (AIC), root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and mean absolute scaled error (MASE). ARIMA (1, 1, 1) was identified as the best model for modelling Kenya’s GHG emissions and forecasting future values. Using this model, Kenya’s GHG emissions by the energy sector were forecasted to increase to a value of about 43.13 million metric tons of carbon dioxide equivalents by 2030. The study, therefore, recommends that Kenya should accelerate the adjustment of industry structure and improve the efficient use of energy, optimize the energy structure and accelerate development and promotion of energy-efficient products to reduce the emission of GHGs by the country’s energy sector.
基金supported by the Program of National Natural Science Foundation of China(U23A20329,62163036)Youth Academic and Technical Leaders Reserve Talent Training project(202105AC160094)Industrial Innovation Talent Special Project of Xingdian Talent Support Program(XDYC-CYCX-2022-0010).
文摘Kernel-based slow feature analysis(SFA)methods have been successfully applied in the industrial process fault detection field.However,kernel-based SFA methods have high computational complexity as dealing with nonlinearity,leading to delays in detecting time-varying data features.Additionally,the uncertain kernel function and kernel parameters limit the ability of the extracted features to express process characteristics,resulting in poor fault detection performance.To alleviate the above problems,a novel randomized auto-regressive dynamic slow feature analysis(RRDSFA)method is proposed to simultaneously monitor the operating point deviations and process dynamic faults,enabling real-time monitoring of data features in industrial processes.Firstly,the proposed Random Fourier mappingbased method achieves more effective nonlinear transformation,contrasting with the current kernelbased RDSFA algorithm that may lead to significant computational complexity.Secondly,a randomized RDSFA model is developed to extract nonlinear dynamic slow features.Furthermore,a Bayesian inference-based overall fault monitoring model including all RRDSFA sub-models is developed to overcome the randomness of random Fourier mapping.Finally,the superiority and effectiveness of the proposed monitoring method are demonstrated through a numerical case and a simulation of continuous stirred tank reactor.
基金grant from the Research Grants Council of Hong Kong
文摘This paper is devoted to the goodness-of-fit test for the general autoregressive models in time series. By averaging for the weighted residuals, we construct a score type test which is asymptotically standard chi-squared under the null and has some desirable power properties under the alternatives. Specifically, the test is sensitive to alternatives and can detect the alternatives approaching, along a direction, the null at a rate that is arbitrarily close to n-1/2. Furthermore, when the alternatives are not directional, we construct asymptotically distribution-free maximin tests for a large class of alternatives. The performance of the tests is evaluated through simulation studies.
基金Supported by the National Natural Science Foundation of China(No.10871188,10801123)
文摘Based on the empirical likelihood method, the subset selection and hypothesis test for parameters in a partially linear autoregressive model are investigated. We show that the empirical log-likelihood ratio at the true parameters converges to the standard chi-square distribution. We then present the definitions of the empirical likelihood-based Bayes information criteria （EBIC） and Akaike information criteria （EAIC）. The results show that EBIC is consistent at selecting subset variables while EAIC is not. Simulation studies demonstrate that the proposed empirical likelihood confidence regions have better coverage probabilities than the least square method, while EBIC has a higher chance to select the true model than EAIC.
基金Supported by NSFC(Grant Nos.11771390 and 11371318)Zhejiang Provincial Natural Science Foundation of China(Grant No.LR16A010001)the Fundamental Research Funds for the Central Universities。
文摘A self-weighted quantile procedure is proposed to study the inference for a spatial unilateral autoregressive model with independent and identically distributed innovations belonging to the domain of attraction of a stable law with index of stabilityα,α∈(0,2).It is shown that when the model is stationary,the self-weighted quantile estimate of the parameter has a closed form and converges to a normal limiting distribution,which avoids the difficulty of Roknossadati and Zarepour(2010)in deriving their limiting distribution for an M-estimate.On the contrary,we show that when the model is not stationary,the proposed estimates have the same limiting distributions as those of Roknossadati and Zarepour.Furthermore,a Wald test statistic is proposed to consider the test for a linear restriction on the parameter,and it is shown that under a local alternative,the Wald statistic has a non-central chisquared distribution.Simulations and a real data example are also reported to assess the performance of the proposed method.
基金supported by the Fundamental Research Funds for the Central Universities(Grant No.JBK2207075)The second author was supported by National Natural Science Foundation of China(Grant Nos.71991472,12171395,11931014 and 71532001)+1 种基金the Joint Lab of Data Science and Business Intelligence at Southwestern University of Finance and Economics and the Fundamental Research Funds for the Central Universities(Grant No.JBK1806002)The fourth author was supported by the Humanity and Social Science Youth Foundation of Ministry of Education of China(Grant No.19YJC790204)。
文摘We propose a novel polynomial network autoregressive model by incorporating higher-order connected relationships to simultaneously model the effects of both direct and indirect connections. A quasimaximum likelihood estimation method is proposed to estimate the unknown influence parameters, and we demonstrate its consistency and asymptotic normality without imposing any distribution assumption. Moreover,an extended Bayesian information criterion is set for order selection with a divergent upper order. The application of the proposed polynomial network autoregressive model is demonstrated through both the simulation and the real data analysis.
基金The authors thank the editor,Prof.Jun Shao,and two anony-mous reviewers for helpful comments.Yu Han was supported by the Scientific Research Foundation of Jilin Education[grant number JJKH20200102KJ]The work of C.Zhang was partially supported by U.S.National Science Foundation[grant numbers DMS-2013486 and DMS-1712418]pro-vided by the University of Wisconsin-Madison Office of the Vice Chancellor for Research and Graduate Education with funding from the Wisconsin Alumni Research Foundation.
文摘This paper develops the empirical likelihood(EL)inference procedure for parameters in autore-gressive models with the error variances scaled by an unknown nonparametric time-varying function.Compared with existing methods based on non-parametric and semi-parametric esti-mation,the proposed test statistic avoids estimating the variance function,while maintaining the asymptotic chi-square distribution under the null.Simulation studies demonstrate that the proposed EL procedure(a)is more stable,i.e.,depending less on the change points in the error variances,and(b)gets closer to the desired confidence level,than the traditional test statistic.
基金The National Key Research and Development Program of China under contract No.2017YFC1404000the Basic Scientific Fund for National Public Research Institutes of China under contract No.2018S03the National Natural Science Foundation of China under contract Nos 41776038 and 41821004
文摘Oceanic turbulence measurements made by an acoustic Doppler velocimeter(ADV)suffer from noise that potentially affects the estimates of turbulence statistics.This study examines the abilities of Kalman filtering and autoregressive moving average models to eliminate noise in ADV velocity datasets of laboratory experiments and offshore observations.Results show that the two methods have similar performance in ADV de-noising,and both effectively reduce noise in ADV velocities,even in cases of high noise.They eliminate the noise floor at high frequencies of the velocity spectra,leading to a longer range that effectively fits the Kolmogorov-5/3 slope at midrange frequencies.After de-noising adopting the two methods,the values of the mean velocity are almost unchanged,while the root-mean-square horizontal velocities and thus turbulent kinetic energy decrease appreciably in these experiments.The Reynolds stress is also affected by high noise levels,and de-noising thus reduces uncertainties in estimating the Reynolds stress.
基金The National Natural Science Foundation of China(No.50479017).
文摘Based on analyzing the limitations of the commonly used back-propagation neural network （BPNN）, a wavelet neural network （WNN） is adopted as the nonlinear river channel flood forecasting method replacing the BPNN. The WNN has the characteristics of fast convergence and improved capability of nonlinear approximation. For the purpose of adapting the timevarying characteristics of flood routing, the WNN is coupled with an AR real-time correction model. The AR model is utilized to calculate the forecast error. The coefficients of the AR real-time correction model are dynamically updated by an adaptive fading factor recursive least square（RLS） method. The application of the flood forecasting method in the cross section of Xijiang River at Gaoyao shows its effectiveness.
基金supported by the Natural Science Foundation of Hunan Province(2023JJ30817)Hunan Provincial Natural Science Foundation-Hengyang City Joint Fund Project(2025JJ70129)+1 种基金Changsha Natural Science Foundation(kq2403057)China。
文摘Objective:Near vision loss(NVL)is one of the leading causes of visual impairment worldwide,exerting a profound impact on individual quality of life and socio-economic development.This study aims to analyze the burden of NVL in China by sex and age groups from 1990 to 2021 and to project trends over the next 15 years.Methods:Using data from the Global Burden of Disease(GBD)2021 database,we conducted descriptive analyses of NVL prevalence in China,calculated age-standardized prevalence rates(ASPR)and age-standardized disability-adjusted life years rates(ASDR)to compare burden differences between sexes and age groups,and applied an autoregressive integrated moving average(ARIMA)model to predict NVL trends for the next 15 years.The model selection was based on best-fit criteria to ensure reliable projections.Results:From 1990 to 2021,China’s ASPR of NVL rose from 10096.24/100000 to 15624.54/100000,and ASDR increased from 101.75/100000 to 158.75/100000.In 2021,ASPR(16551.70/100000)and ASDR(167.69/100000)were higher among females than males(14686.21/100000 and 149.76/100000,respectively).China ranked highest globally in both NVL cases and disability-adjusted life years(DALYs),with female burden significantly exceeding male burden.Projections indicated this trend and sex gap will persist until 2036.Compared with 1990,the prevalence cases and DALYs increased by 239.20%and 238.82%,respectively in 2021,with the highest burden among females and the 55−59 age group.The ARIMA model predicted continued increases in prevalence and DALYs by 2036,with females maintaining a higher burden than males.Conclusion:This study reveals a marked increase in the NVL burden in China and predicts continued growth in the coming years.Public health policies should prioritize NVL prevention and control,with special attention to women and middle-aged populations to mitigate long-term societal and health impacts.
文摘High-resolution spatiotemporal simulations effectively capture the complexities of atmospheric plume sion disper-in complex terrain.However,their high computational cost makes them impractical for applications requiring rapid responses or iterative processes,such as optimization,uncertainty quantification,or inverse modeling.To address this challenge,this work introduces the Dual-Stage Temporal Three-dimensional UNet Super-resolution(DST3D-UNet-SR)model,a highly efficient deep learning model for plume dispersion predictions.DST3D-UNet-SR is composed of two sequential modules:the temporal module(TM),which predicts the transient evolution of a plume in complex terrain from low-resolution temporal data,and the spatial refinement module(SRM),which subsequently enhances the spatial resolution of the TM predictions.We train DST3D-UNet-SR using a comprehensive dataset derived from high-resolution large eddy simulations(LES)of plume transport.We propose the DST3D-UNet-SR model to significantly accelerate LES of three-dimensional(3D)plume dispersion by three orders of magnitude.Additionally,the model demonstrates the ability to dynamically adapt to evolving conditions through the incorporation of new observational data,substantially improving prediction accuracy in high-concentration regions near the source.
文摘The rapid expansion of tobacco farming poses a significant threat to biodiversity in Yunnan Province,China,a region known for its rich biodiversity.This study aims to understand the trade-offs between tobacco farming and higher plant species diversity,and to identify priority counties for conservation.We employed an integrated approach combining species distribution modeling,GIS overlay analysis,and empirical spatial regression to em pirically assess the impact of tobacco farming intensity on biodiversity risk.Our findings reveal a compelling negative spatial correlation between tobacco farming expansion and higher plant species diversity.Specifically,southern counties in Wenshan and Honghe prefectures are major priority areas of conservation that exhibit signif icant spatial correlations between biodiversity risks and high tobacco farming intensity.Quantitatively,at county level,a 1%increase in tobacco farming area corresponds to a 0.094%decrease in endemic higher plant species richness across the entire province.These results underscore the need for targeted and region-specific regulations to mitigate biodiversity loss and promote sustainable development in Yunnan Province.The integrated approach used in this study provides a comprehensive assessment of the tobacco-biodiversity trade-offs,offering actionable insights for policymaking.
基金funded by Taif University,Saudi Arabia,Project No.(TU-DSPP-2024-52).
文摘Aquila Optimizer(AO)is a recently proposed population-based optimization technique inspired by Aquila’s behavior in catching prey.AO is applied in various applications and its numerous variants were proposed in the literature.However,chaos theory has not been extensively investigated in AO.Moreover,it is still not applied in the parameter estimation of electro-hydraulic systems.In this work,ten well-defined chaotic maps were integrated into a narrowed exploitation of AO for the development of a robust chaotic optimization technique.An extensive investigation of twenty-three mathematical benchmarks and ten IEEE Congress on Evolutionary Computation(CEC)functions shows that chaotic Aquila optimization techniques perform better than the baseline technique.The investigation is further conducted on parameter estimation of an electro-hydraulic control system,which is performed on various noise levels and shows that the proposed chaotic AO with Piecewise map(CAO6)achieves the best fitness values of and at noise levels and respectively.Friedman test 2.873E-05,1.014E-04,8.728E-031.300E-03,1.300E-02,1.300E-01,for repeated measures,computational analysis,and Taguchi test reflect the superiority of CAO6 against the state of the arts,demonstrating its potential for addressing various engineering optimization problems.However,the sensitivity to parameter tuning may limit its direct application to complex optimization scenarios.
文摘We discuss formulas and techniques for finding maximum-likelihood estimators of parameters of autoregressive (with particular emphasis on Markov and Yule) models, computing their asymptotic variance-covariance matrix and displaying the resulting confidence regions;Monte Carlo simulation is then used to establish the accuracy of the corresponding level of confidence. The results indicate that a direct application of the Central Limit Theorem yields errors too large to be acceptable;instead, we recommend using a technique based directly on the natural logarithm of the likelihood function, verifying its substantially higher accuracy. Our study is then extended to the case of estimating only a subset of a model’s parameters, when the remaining ones (called nuisance) are of no interest to us.
基金research supported wholly by the National Research Foundation (NRF) of South Africathe Dutch Research Council (NWO) Project (UID 129352)
文摘The use ofrenewable energyisan important way toachieve sustainable agriculturalandeconomic development.However,there are differences in accessto renewable energy between the Global North and Global South.This study utilisedan autoregressive distributed lag-error correctionmodel and thedata spanning from 1991to 2021 to comparatively analyse the dynamic relationship amongrenewable energy consumption,the value of agricultural production,gross domestic product(GDP),economic diversificationindex,urban population,the total water extraction for agricultural withdrawal,and trade balancein the Netherlands and South Africa.In the shortrun,renewable energy consumption was increased by the value of agricultural productionbut decreased by GDPin South Africa.In the longrun,renewable energy consumption and GDP increased the value of agricultural production,while the value of agricultural production also increased GDP in South Africa.However,in the Netherlands,there was no short-and long-run relationship betweenrenewable energy consumption and agricultural and economic development.The results revealedthat there was a short-and long-run relationship in South Africa.Moreover,in the Netherlands,the adjustment speed was-1.46 forrenewable energy consumption with an error correction of 0.68 a(8.22 months).In South Africa,the adjustment speedwas-1.28 forrenewable energy consumption with an error correction of 0.78 a(9.38 months).Therefore,compared to South Africa,renewable energy consumptionin the Netherlands takes less time to return to balance after a shock.Thesefindings signify different trajectories on sectoral and economic transition initiatives spurred usingrenewable energy between the Netherlands and South Africa.Policy relating to initiatives such as“agro-energy communities”in Global South countries such as South Africa should be emphasised to promote the use of renewable energy in the agricultural sector.
