Consider the semiparametric varying-coefficient heteroscedastic partially linear model Yi = X^T i β＋ Z^T iα（Ti） ＋ σiei, 1 ≤ i≤ n, where σ ^2i= f（Ui）, β is a p × 1 column vector of unknown parameter, ...Consider the semiparametric varying-coefficient heteroscedastic partially linear model Yi = X^T i β＋ Z^T iα（Ti） ＋ σiei, 1 ≤ i≤ n, where σ ^2i= f（Ui）, β is a p × 1 column vector of unknown parameter, （Xi, Zi, Ti, Ui） are random design q-dimensional vector of unknown functions, el points, Yi are the response variables, α（-） is a are random errors. For both cases that f（.） is known and unknown, we propose the empirical log-likelihood ratio statistics for the parameter f（.）. For each case, a nonparametric version of Wilks＇ theorem is derived. The results are then used to construct confidence regions of the parameter. Simulation studies are carried out to assess the performance of the empirical likelihood method.展开更多
The paper considers a multivariate partially linear model under independent errors,and investigates the asymptotic bias and variance-covariance for parametric component βand nonparametric component F（·）by the ...The paper considers a multivariate partially linear model under independent errors,and investigates the asymptotic bias and variance-covariance for parametric component βand nonparametric component F（·）by the GJS estimator and Kernel estimation.展开更多
In this paper, we consider the variable selection for the parametric components of varying coefficient partially linear models with censored data. By constructing a penalized auxiliary vector ingeniously, we propose a...In this paper, we consider the variable selection for the parametric components of varying coefficient partially linear models with censored data. By constructing a penalized auxiliary vector ingeniously, we propose an empirical likelihood based variable selection procedure, and show that it is consistent and satisfies the sparsity. The simulation studies show that the proposed variable selection method is workable.展开更多
This article is concerned with the estimating problem of semiparametric varyingcoefficient partially linear regression models. By combining the local polynomial and least squares procedures Fan and Huang （2005） prop...This article is concerned with the estimating problem of semiparametric varyingcoefficient partially linear regression models. By combining the local polynomial and least squares procedures Fan and Huang （2005） proposed a profile least squares estimator for the parametric component and established its asymptotic normality. We further show that the profile least squares estimator can achieve the law of iterated logarithm. Moreover, we study the estimators of the functions characterizing the non-linear part as well as the error variance. The strong convergence rate and the law of iterated logarithm are derived for them, respectively.展开更多
In this article, we study the variable selection of partially linear single-index model(PLSIM). Based on the minimized average variance estimation, the variable selection of PLSIM is done by minimizing average varianc...In this article, we study the variable selection of partially linear single-index model(PLSIM). Based on the minimized average variance estimation, the variable selection of PLSIM is done by minimizing average variance with adaptive l1 penalty. Implementation algorithm is given. Under some regular conditions, we demonstrate the oracle properties of aLASSO procedure for PLSIM. Simulations are used to investigate the effectiveness of the proposed method for variable selection of PLSIM.展开更多
Consider the regression model, n. Here the design points (xi,ti) are known and nonrandom, and ei are random errors. The family of nonparametric estimates of g() including known estimates proposed by Gasser & Mulle...Consider the regression model, n. Here the design points (xi,ti) are known and nonrandom, and ei are random errors. The family of nonparametric estimates of g() including known estimates proposed by Gasser & Muller[1] is also proposed to be a class of new nearest neighbor estimates of g(). Baed on the nonparametric regression procedures, we investigate a statistic for testing H0:g=0, and obtain some aspoptotic results about estimates.展开更多
Variable selection plays an important role in high-dimensional data analysis.But the high-dimensional data often induces the strongly correlated variables problem,which should be properly handled.In this paper,we prop...Variable selection plays an important role in high-dimensional data analysis.But the high-dimensional data often induces the strongly correlated variables problem,which should be properly handled.In this paper,we propose Elastic Net procedure for partially linear models and prove the group effect of its estimate.A simulation study shows that the Elastic Net procedure deals with the strongly correlated variables problem better than the Lasso,ALasso and the Ridge do.Based on the real world data study,we can get that the Elastic Net procedure is particularly useful when the number of predictors pffis much bigger than the sample size n.展开更多
Consider the partly linear model Y = xβ ＋ g（t） ＋ e where the explanatory x is erroneously measured, and both t and the response Y are measured exactly, the random error e is a martingale difference sequence. Let ...Consider the partly linear model Y = xβ ＋ g（t） ＋ e where the explanatory x is erroneously measured, and both t and the response Y are measured exactly, the random error e is a martingale difference sequence. Let ~ be a surrogate variable observed instead of the true x in the primary survey data. Assume that in addition to the primary data set containing N observations of {（Yj, xj, tj）n＋N j=n＋1 }, the independent validation data containing n observations of {（xj, x j, tj）n j=1 } is available. In this paper, a semiparametric method with the primary data is employed to obtain the estimator ofβ and g（-） based on the least squares criterion with the help of validation data. The proposed estimators are proved to be strongly consistent. Finite sample behavior of the estimators is investigated via simulations too.展开更多
We propose the test statistic to check whether the nonpararnetric functions in two partially linear models are equality or not in this paper. We estimate the nonparametric function both in null hypothesis and the alte...We propose the test statistic to check whether the nonpararnetric functions in two partially linear models are equality or not in this paper. We estimate the nonparametric function both in null hypothesis and the alternative by the local linear method, where we ignore the parametric components, and then estimate the parameters by the two stage method. The test statistic is derived, and it is shown to be asymptotically normal under the null hypothesis.展开更多
This article concerded with a semiparametric generalized partial linear model （GPLM） with the type Ⅱ censored data. A sieve maximum likelihood estimator （MLE） is proposed to estimate the parameter component, allo...This article concerded with a semiparametric generalized partial linear model （GPLM） with the type Ⅱ censored data. A sieve maximum likelihood estimator （MLE） is proposed to estimate the parameter component, allowing exploration of the nonlinear relationship between a certain covariate and the response function. Asymptotic properties of the proposed sieve MLEs are discussed. Under some mild conditions, the estimators are shown to be strongly consistent. Moreover, the estimators of the unknown parameters are asymptotically normal and efficient, and the estimator of the nonparametric function has an optimal convergence rate.展开更多
BACKGROUND： Electrical stimulation kindling model, having epilepsy-inducing and spontaneous seizure and other advantages, is a very ideal experimental animal model. But the kindling effect might be different at diffe...BACKGROUND： Electrical stimulation kindling model, having epilepsy-inducing and spontaneous seizure and other advantages, is a very ideal experimental animal model. But the kindling effect might be different at different sites. OBJECTIVE： To compare the features of animal models of complex partial epilepsy established through unilateral, bilateral and alternate-side kindling at hippocampus and successful rate of modeling among these 3 different ways. DESIGN： A randomized and controlled animal experiment SETTING： Department of Neurology, Qilu Hospital, Shandong University MATERIALS： Totally 60 healthy adult Wistar rats, weighing 200 to 300 g, of either gender, were used in this experiment. BL-410 biological functional experimental system （Taimeng Science and Technology Co. Ltd, Chengdu） and SE-7102 type electronic stimulator （Guangdian Company, Japan） were used in the experiment. METHODS： This experiment was carried out in the Experimental Animal Center of Shandong University from April to June 2004. After rats were anesthetized, electrode was implanted into the hippocampus. From the first day of measurement of afterdischarge threshold value, rats were given two-square-wave suprathreshold stimulation once per day with 400 μA intensity, 1ms wave length, 60 Hz frequency for 1 s duration. Left hippocampus was stimulated in unilateral kindling group, bilateral hippocampi were stimulated in bilateral kindling group, and left and right hippocampi were stimulated alternately every day in the alternate-side kindling group. Seizure intensity was scored： grade 0： normal, 1： wet dog-like shivering, facial spasm, such as, winking, touching the beard, rhythmic chewing and so on; 2： rhythmic nodding; 3： forelimb spasm;4： standing accompanied by bilateral forelimb spasm;5： tumbling, losing balance, four limbs spasm. Modeling was successful when seizure intensity reached grade 5. t test was used for the comparison of mean value between two samples. MAIN OUTCOME MEASURES： Comparison of the successful rate of modeling, the times of stimulation to reach intensity of grade 5, the lasting time of seizure of grade 3 of rats in each group. RESULTS： Four rats of alternate-side kindling group dropped out due to infection-induced electrode loss, and 56 rats were involved in the result analysis. The successful rate of unilateral kindling group, bilateral kin- dling group and alternate-side kindling group was 55%（11/20）,100%（16/16）and 100%（20/20）, respective- ly. The stimuli to reach the grade 5 spasm were significantly more in the bilateral kindling group than in the unilateral kindling group [（30.63±3.48）, （19.36±3.47）times, t=8.268, P 〈 0.01], and those were significantly fewer in the alternate-side kindling group than in the unilateral kindling group [（ 10.85±1.98）times, t=-8.744, P 〈 0.01]. The duration of grade 3 spasm was significantly longer in the bilateral kindling group than in the unilateral kindling group [（9.75±2.59）, （3.21 ±1.58）days,t=-8.183,P 〈 0.01], Among 20 successful rats of al- ternate-side kindling group, grade 5 spasm was found in the left hippocampi of 11 rats, but grade 3 spasm in their right hippocampi; Grade 5 spasm was found in the right hippocampi of the other 9 rats, grade 4 spasm in the left hippocampus of 1 rat and grade 3 of 8 rats. CONCLUSION ： The speed of establishing epilepsy seizure model by alternate-side kindling is faster than that by unilateral kindling, while that by bilateral kindling is slower than that by unilateral kindling. The successful rate is very high to establish complex partial epilepsy with alternate-side or bilateral kindling. Epilepsy seizure established by alternate-side kindling has antagonistic effect of kindling and the seizure duration of grade 3 spasm is prolonged.展开更多
In this paper,we develop a robust variable selection procedure based on the exponential squared loss(ESL)function for the varying coefficient partially nonlinear model.Under certain conditions,some asymptotic properti...In this paper,we develop a robust variable selection procedure based on the exponential squared loss(ESL)function for the varying coefficient partially nonlinear model.Under certain conditions,some asymptotic properties of the proposed penalized ESL estimator are established.Meanwhile,the proposed procedure can automatically eliminate the irrelevant covariates,and simultaneously estimate the nonzero regression co-efficients.Furthermore,we apply the local quadratic approximation(LQA)and minorization–maximization(MM)algorithm to calculate the estimates of non-parametric and parametric parts,and introduce a data-driven method to select the tuning parameters.Simulation studies illustrate that the proposed method is more robust than the classical least squares technique when there are outliers in the dataset.Finally,we apply the proposed procedure to analyze the Boston housing price data.The results reveal that the proposed method has a better prediction ability.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
Understanding the complex mechanisms underlying agricultural space urbanization is essential for sustainable land management.This study identified the spatiotemporal characteristics of the agricultural space urbanizat...Understanding the complex mechanisms underlying agricultural space urbanization is essential for sustainable land management.This study identified the spatiotemporal characteristics of the agricultural space urbanization from 2000 to 2020 in China’s Yangtze River Economic Belt(YREB)using a kilometer-grid-based approach.By employing the partial least squares structural equation modeling method,the intricate drivers of agricultural space urbanization were investigated.The results revealed that from 2000 to 2020,agricultural space urbanization in the YREB covered an area of 28,198 km^(2),accounting for 84.5%of the total increase in urban space.The partial least squares structural equation modeling analysis revealed regional variations in agricultural space urbanization dynamics.In the western YREB,where urbanization is in its initial stage,natural conditions play a weak and indirect role,whereas policy incentives and socioeconomic growth are equally significant in driving agricultural space urbanization.In the eastern YREB,where urbanization is more saturated,the agricultural space urbanization is less constrained by natural factors,showing a high synergy with socioeconomic development.Conversely,in the central Yangtze River Economic Belt,policy influences surpass socioeconomic factors,whereas unfavorable natural conditions and agricultural development act as key drivers of agricultural space urbanization.This study suggests that enhancing agricultural space urbanization quality requires strengthening region-specific policies,providing targeted support for remote areas,rebalancing policy orientation in rapidly urbanizing regions,and establishing a comprehensive evaluation system to ensure policy rationality.展开更多
The rapid acceleration of global warming and intensifying human activities have exacerbated the fragility and climate sensitivity of ecosystems worldwide,particularly in arid regions.Vegetation,a key component of ecos...The rapid acceleration of global warming and intensifying human activities have exacerbated the fragility and climate sensitivity of ecosystems worldwide,particularly in arid regions.Vegetation,a key component of ecosystems,is critical in enhancing the ecological environment.The Ertix River Basin(ERB)is a transboundary watershed that spans multiple countries,mostly in arid regions.However,research on the fractional vegetation coverage(FVC)and its driving factors in the ERB remains limited.Investigating the spatiotemporal changes in the FVC and its relationship with various factors in the ERB can offer scientific support for optimizing regional vegetation restoration policies and promoting the coordinated development of human-environment interactions.The Moderate-resolution Imaging Spectroradiometer(MODIS)MYD13Q1 V6 data were obtained via the Google Earth Engine platform,and methods including the pixel dichotomy method,Theil-Sen median trend analysis,and Mann‒Kendall test were employed to examine the spatiotemporal dynamics of the FVC in the ERB from 2003 to 2023,with future trend forecast using the Hurst index.The impacts of natural and socioeconomic factors on the FVC were evaluated through the partial least squares-structural equation model(PLS-SEM).The results indicated that the FVC in the ERB showed a slight degradation trend with an average annual decrease of 0.046%during 2003-2023,with significant changes occurring in 2004,2010,and 2019.Spatially,53.380%of the study area was degraded,and the change in the FVC increased gradually from southeast to northwest.The FVC in 63.000%of the study area was highly stable and displayed long-term persistence;and the direct impact of natural factors(path coefficient of 0.617)on the FVC was significantly higher than that of socioeconomic factors(0.167).Among the natural factors,precipitation(0.999)was the most significant.This study reveals the significant impacts of natural and socioeconomic factors on vegetation dynamics in arid regions,and provides a scientific basis for transnational ecological conservation.展开更多
Poverty remains one of the most pressing global challenges of this era,affecting millions of people across both developing and developed countries.The poverty alleviation resettlement(PAR)is a policy with Chinese char...Poverty remains one of the most pressing global challenges of this era,affecting millions of people across both developing and developed countries.The poverty alleviation resettlement(PAR)is a policy with Chinese characteristics for eradicating poverty.By integrating the Maslow’s Hierarchy of Needs and Amartya Sen’s Capability Approach,this study developed a theoretical framework to analyze the factors influencing the well-being of poverty alleviation migrants(PAMs).A telephone survey conducted between July and August 2022 in Hubei Province,Guizhou Province,Shaanxi Province,and Chongqing Municipality of China yielded 259 valid questionnaires.Using the partial least squares-structural equation modeling(PLS-SEM),this study revealed that financial accessibility,health level,living conditions,and social networks significantly enhanced the well-being of PAMs,with living conditions having the strongest impact on the well-being of PAMs.Furthermore,the factors affecting well-being varied across age groups.Social networks played a more significant role in the elderly group,whereas health level had a greater impact on the young and middle-aged group.These findings deepen the understanding of the PAR and its effects on the well-being of PAMs,offering valuable insights for policy-makers and practitioners to refine poverty alleviation strategies and enhance social welfare.展开更多
Partial cooperation models are studied for many years to solve the bilevel programming problems where the follower’s optimal reaction is not unique. However, in these existed models, the follower’s cooperation level...Partial cooperation models are studied for many years to solve the bilevel programming problems where the follower’s optimal reaction is not unique. However, in these existed models, the follower’s cooperation level does not depend on the leader’s decision. A new model is proposed to solve this deficiency. It is proved the feasibility of the new model when the reaction set of the lower level is lower semicontinuous. And the numerical results show that the new model has optimal solutions when the reaction set of the lower level is discrete, lower semi-continuous and non-lower semi-continuous.展开更多
When the total least squares(TLS)solution is used to solve the parameters in the errors-in-variables(EIV)model,the obtained parameter estimations will be unreliable in the observations containing systematic errors.To ...When the total least squares(TLS)solution is used to solve the parameters in the errors-in-variables(EIV)model,the obtained parameter estimations will be unreliable in the observations containing systematic errors.To solve this problem,we propose to add the nonparametric part(systematic errors)to the partial EIV model,and build the partial EIV model to weaken the influence of systematic errors.Then,having rewritten the model as a nonlinear model,we derive the formula of parameter estimations based on the penalized total least squares criterion.Furthermore,based on the second-order approximation method of precision estimation,we derive the second-order bias and covariance of parameter estimations and calculate the mean square error(MSE).Aiming at the selection of the smoothing factor,we propose to use the U curve method.The experiments show that the proposed method can mitigate the influence of systematic errors to a certain extent compared with the traditional method and get more reliable parameter estimations and its precision information,which validates the feasibility and effectiveness of the proposed method.展开更多
This paper considers the local linear regression estimators for partially linear model with censored data. Which have some nice large-sample behaviors and are easy to implement. By many simulation runs, the author als...This paper considers the local linear regression estimators for partially linear model with censored data. Which have some nice large-sample behaviors and are easy to implement. By many simulation runs, the author also found that the estimators show remarkable in the small sample case yet.展开更多
In this article, a partially linear single-index model /or longitudinal data is investigated. The generalized penalized spline least squares estimates of the unknown parameters are suggested. All parameters can be est...In this article, a partially linear single-index model /or longitudinal data is investigated. The generalized penalized spline least squares estimates of the unknown parameters are suggested. All parameters can be estimated simultaneously by the proposed method while the feature of longitudinal data is considered. The existence, strong consistency and asymptotic normality of the estimators are proved under suitable conditions. A simulation study is conducted to investigate the finite sample performance of the proposed method. Our approach can also be used to study the pure single-index model for longitudinal data.展开更多
基金Supported by the National Natural Science Foundation of China (Grant No. 71171003)Natural Science Research Project of Anhui Provincial Colleges (Grant No. KJ2011A032)+3 种基金Anhui Polytechnic University Foundation for Recruiting Talent (Grant Nos. 2011YQQ0042009YQQ005)Young Teachers Science Research Foundation of Anhui Polytechnic University (Grant No. 2009YQ035)Anhui Provincial Natural Science Foundation
文摘Consider the semiparametric varying-coefficient heteroscedastic partially linear model Yi = X^T i β＋ Z^T iα（Ti） ＋ σiei, 1 ≤ i≤ n, where σ ^2i= f（Ui）, β is a p × 1 column vector of unknown parameter, （Xi, Zi, Ti, Ui） are random design q-dimensional vector of unknown functions, el points, Yi are the response variables, α（-） is a are random errors. For both cases that f（.） is known and unknown, we propose the empirical log-likelihood ratio statistics for the parameter f（.）. For each case, a nonparametric version of Wilks＇ theorem is derived. The results are then used to construct confidence regions of the parameter. Simulation studies are carried out to assess the performance of the empirical likelihood method.
基金Supported by the Anhui Provincial Natural Science Foundation（11040606M04） Supported by the National Natural Science Foundation of China（10871001,10971097）
文摘The paper considers a multivariate partially linear model under independent errors,and investigates the asymptotic bias and variance-covariance for parametric component βand nonparametric component F（·）by the GJS estimator and Kernel estimation.
基金Supported by the National Natural Science Foundation of China(Grant Nos.1110111911126332)+2 种基金the National Social Science Foundation of China(Grant No.11CTJ004)the Natural Science Foundation of Guangxi Province(Grant No.2010GXNSFB013051)the Philosophy and Social Sciences Foundation of Guangxi Province(Grant No.11FTJ002)
文摘In this paper, we consider the variable selection for the parametric components of varying coefficient partially linear models with censored data. By constructing a penalized auxiliary vector ingeniously, we propose an empirical likelihood based variable selection procedure, and show that it is consistent and satisfies the sparsity. The simulation studies show that the proposed variable selection method is workable.
基金supported by the National Natural Science Funds for Distinguished Young Scholar (70825004)National Natural Science Foundation of China (NSFC) (10731010 and 10628104)+3 种基金the National Basic Research Program (2007CB814902)Creative Research Groups of China (10721101)Leading Academic Discipline Program, the 10th five year plan of 211 Project for Shanghai University of Finance and Economics211 Project for Shanghai University of Financeand Economics (the 3rd phase)
文摘This article is concerned with the estimating problem of semiparametric varyingcoefficient partially linear regression models. By combining the local polynomial and least squares procedures Fan and Huang （2005） proposed a profile least squares estimator for the parametric component and established its asymptotic normality. We further show that the profile least squares estimator can achieve the law of iterated logarithm. Moreover, we study the estimators of the functions characterizing the non-linear part as well as the error variance. The strong convergence rate and the law of iterated logarithm are derived for them, respectively.
文摘In this article, we study the variable selection of partially linear single-index model(PLSIM). Based on the minimized average variance estimation, the variable selection of PLSIM is done by minimizing average variance with adaptive l1 penalty. Implementation algorithm is given. Under some regular conditions, we demonstrate the oracle properties of aLASSO procedure for PLSIM. Simulations are used to investigate the effectiveness of the proposed method for variable selection of PLSIM.
文摘Consider the regression model, n. Here the design points (xi,ti) are known and nonrandom, and ei are random errors. The family of nonparametric estimates of g() including known estimates proposed by Gasser & Muller[1] is also proposed to be a class of new nearest neighbor estimates of g(). Baed on the nonparametric regression procedures, we investigate a statistic for testing H0:g=0, and obtain some aspoptotic results about estimates.
基金Supported by National Natural Science Foundation of China(No.71462002)the Project for Teaching Reform of Guangxi(GXZZJG2017B084)the Project for Fostering Distinguished Youth Scholars of Guangxi(2020KY50012)。
文摘Variable selection plays an important role in high-dimensional data analysis.But the high-dimensional data often induces the strongly correlated variables problem,which should be properly handled.In this paper,we propose Elastic Net procedure for partially linear models and prove the group effect of its estimate.A simulation study shows that the Elastic Net procedure deals with the strongly correlated variables problem better than the Lasso,ALasso and the Ridge do.Based on the real world data study,we can get that the Elastic Net procedure is particularly useful when the number of predictors pffis much bigger than the sample size n.
基金Supported by National Natural Science Foundation of China(Grant Nos.1127115511371168+7 种基金110011051107112611071269)Specialized Research Fund for the Doctoral Program of Higher Education(Grant No.20110061110003)the Natural Science Foundation of Jilin Province(Grant Nos.20130101066JC20130522102JH20101596)"Twelfth Five-Year Plan"Science and Technology Research Project of the Education Department of Jilin Province(Grant No.2012186)
文摘Consider the partly linear model Y = xβ ＋ g（t） ＋ e where the explanatory x is erroneously measured, and both t and the response Y are measured exactly, the random error e is a martingale difference sequence. Let ~ be a surrogate variable observed instead of the true x in the primary survey data. Assume that in addition to the primary data set containing N observations of {（Yj, xj, tj）n＋N j=n＋1 }, the independent validation data containing n observations of {（xj, x j, tj）n j=1 } is available. In this paper, a semiparametric method with the primary data is employed to obtain the estimator ofβ and g（-） based on the least squares criterion with the help of validation data. The proposed estimators are proved to be strongly consistent. Finite sample behavior of the estimators is investigated via simulations too.
文摘We propose the test statistic to check whether the nonpararnetric functions in two partially linear models are equality or not in this paper. We estimate the nonparametric function both in null hypothesis and the alternative by the local linear method, where we ignore the parametric components, and then estimate the parameters by the two stage method. The test statistic is derived, and it is shown to be asymptotically normal under the null hypothesis.
基金The talent research fund launched (3004-893325) of Dalian University of Technologythe NNSF (10271049) of China.
文摘This article concerded with a semiparametric generalized partial linear model （GPLM） with the type Ⅱ censored data. A sieve maximum likelihood estimator （MLE） is proposed to estimate the parameter component, allowing exploration of the nonlinear relationship between a certain covariate and the response function. Asymptotic properties of the proposed sieve MLEs are discussed. Under some mild conditions, the estimators are shown to be strongly consistent. Moreover, the estimators of the unknown parameters are asymptotically normal and efficient, and the estimator of the nonparametric function has an optimal convergence rate.
文摘BACKGROUND： Electrical stimulation kindling model, having epilepsy-inducing and spontaneous seizure and other advantages, is a very ideal experimental animal model. But the kindling effect might be different at different sites. OBJECTIVE： To compare the features of animal models of complex partial epilepsy established through unilateral, bilateral and alternate-side kindling at hippocampus and successful rate of modeling among these 3 different ways. DESIGN： A randomized and controlled animal experiment SETTING： Department of Neurology, Qilu Hospital, Shandong University MATERIALS： Totally 60 healthy adult Wistar rats, weighing 200 to 300 g, of either gender, were used in this experiment. BL-410 biological functional experimental system （Taimeng Science and Technology Co. Ltd, Chengdu） and SE-7102 type electronic stimulator （Guangdian Company, Japan） were used in the experiment. METHODS： This experiment was carried out in the Experimental Animal Center of Shandong University from April to June 2004. After rats were anesthetized, electrode was implanted into the hippocampus. From the first day of measurement of afterdischarge threshold value, rats were given two-square-wave suprathreshold stimulation once per day with 400 μA intensity, 1ms wave length, 60 Hz frequency for 1 s duration. Left hippocampus was stimulated in unilateral kindling group, bilateral hippocampi were stimulated in bilateral kindling group, and left and right hippocampi were stimulated alternately every day in the alternate-side kindling group. Seizure intensity was scored： grade 0： normal, 1： wet dog-like shivering, facial spasm, such as, winking, touching the beard, rhythmic chewing and so on; 2： rhythmic nodding; 3： forelimb spasm;4： standing accompanied by bilateral forelimb spasm;5： tumbling, losing balance, four limbs spasm. Modeling was successful when seizure intensity reached grade 5. t test was used for the comparison of mean value between two samples. MAIN OUTCOME MEASURES： Comparison of the successful rate of modeling, the times of stimulation to reach intensity of grade 5, the lasting time of seizure of grade 3 of rats in each group. RESULTS： Four rats of alternate-side kindling group dropped out due to infection-induced electrode loss, and 56 rats were involved in the result analysis. The successful rate of unilateral kindling group, bilateral kin- dling group and alternate-side kindling group was 55%（11/20）,100%（16/16）and 100%（20/20）, respective- ly. The stimuli to reach the grade 5 spasm were significantly more in the bilateral kindling group than in the unilateral kindling group [（30.63±3.48）, （19.36±3.47）times, t=8.268, P 〈 0.01], and those were significantly fewer in the alternate-side kindling group than in the unilateral kindling group [（ 10.85±1.98）times, t=-8.744, P 〈 0.01]. The duration of grade 3 spasm was significantly longer in the bilateral kindling group than in the unilateral kindling group [（9.75±2.59）, （3.21 ±1.58）days,t=-8.183,P 〈 0.01], Among 20 successful rats of al- ternate-side kindling group, grade 5 spasm was found in the left hippocampi of 11 rats, but grade 3 spasm in their right hippocampi; Grade 5 spasm was found in the right hippocampi of the other 9 rats, grade 4 spasm in the left hippocampus of 1 rat and grade 3 of 8 rats. CONCLUSION ： The speed of establishing epilepsy seizure model by alternate-side kindling is faster than that by unilateral kindling, while that by bilateral kindling is slower than that by unilateral kindling. The successful rate is very high to establish complex partial epilepsy with alternate-side or bilateral kindling. Epilepsy seizure established by alternate-side kindling has antagonistic effect of kindling and the seizure duration of grade 3 spasm is prolonged.
基金supported by the National Natural Science Foundation of China(No.12571284,No.12171203)supported by the National Natural Science Foundation of China(No.12561051)+3 种基金the Fundamental Research Funds for the Central Universities(No.23JNQMX21)supported by the University-level scientific research project of Guangdong University of Foreign Studies(NO.299-GK25G301/25TS10)supported by a grant from National Natural Foundation of China(No.12171225)Yunnan Province Xing Dian Talent Support Program(YNWR-YLXZ-2018-020)。
文摘In this paper,we develop a robust variable selection procedure based on the exponential squared loss(ESL)function for the varying coefficient partially nonlinear model.Under certain conditions,some asymptotic properties of the proposed penalized ESL estimator are established.Meanwhile,the proposed procedure can automatically eliminate the irrelevant covariates,and simultaneously estimate the nonzero regression co-efficients.Furthermore,we apply the local quadratic approximation(LQA)and minorization–maximization(MM)algorithm to calculate the estimates of non-parametric and parametric parts,and introduce a data-driven method to select the tuning parameters.Simulation studies illustrate that the proposed method is more robust than the classical least squares technique when there are outliers in the dataset.Finally,we apply the proposed procedure to analyze the Boston housing price data.The results reveal that the proposed method has a better prediction ability.
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Fellowship Program of the CPSF,No.GZC20231970National Natural Science Foundation of China,No.41871182。
文摘Understanding the complex mechanisms underlying agricultural space urbanization is essential for sustainable land management.This study identified the spatiotemporal characteristics of the agricultural space urbanization from 2000 to 2020 in China’s Yangtze River Economic Belt(YREB)using a kilometer-grid-based approach.By employing the partial least squares structural equation modeling method,the intricate drivers of agricultural space urbanization were investigated.The results revealed that from 2000 to 2020,agricultural space urbanization in the YREB covered an area of 28,198 km^(2),accounting for 84.5%of the total increase in urban space.The partial least squares structural equation modeling analysis revealed regional variations in agricultural space urbanization dynamics.In the western YREB,where urbanization is in its initial stage,natural conditions play a weak and indirect role,whereas policy incentives and socioeconomic growth are equally significant in driving agricultural space urbanization.In the eastern YREB,where urbanization is more saturated,the agricultural space urbanization is less constrained by natural factors,showing a high synergy with socioeconomic development.Conversely,in the central Yangtze River Economic Belt,policy influences surpass socioeconomic factors,whereas unfavorable natural conditions and agricultural development act as key drivers of agricultural space urbanization.This study suggests that enhancing agricultural space urbanization quality requires strengthening region-specific policies,providing targeted support for remote areas,rebalancing policy orientation in rapidly urbanizing regions,and establishing a comprehensive evaluation system to ensure policy rationality.
基金funded by the Third Xinjiang Comprehensive Scientific Investigation Project,China(2022xjkk0702)the Western Young Scholars Project of the Chinese Academy of Sciences(2022-XBQNXZ-001)the Tianshan Talent Development Program,China(2022TSYCCX0006).
文摘The rapid acceleration of global warming and intensifying human activities have exacerbated the fragility and climate sensitivity of ecosystems worldwide,particularly in arid regions.Vegetation,a key component of ecosystems,is critical in enhancing the ecological environment.The Ertix River Basin(ERB)is a transboundary watershed that spans multiple countries,mostly in arid regions.However,research on the fractional vegetation coverage(FVC)and its driving factors in the ERB remains limited.Investigating the spatiotemporal changes in the FVC and its relationship with various factors in the ERB can offer scientific support for optimizing regional vegetation restoration policies and promoting the coordinated development of human-environment interactions.The Moderate-resolution Imaging Spectroradiometer(MODIS)MYD13Q1 V6 data were obtained via the Google Earth Engine platform,and methods including the pixel dichotomy method,Theil-Sen median trend analysis,and Mann‒Kendall test were employed to examine the spatiotemporal dynamics of the FVC in the ERB from 2003 to 2023,with future trend forecast using the Hurst index.The impacts of natural and socioeconomic factors on the FVC were evaluated through the partial least squares-structural equation model(PLS-SEM).The results indicated that the FVC in the ERB showed a slight degradation trend with an average annual decrease of 0.046%during 2003-2023,with significant changes occurring in 2004,2010,and 2019.Spatially,53.380%of the study area was degraded,and the change in the FVC increased gradually from southeast to northwest.The FVC in 63.000%of the study area was highly stable and displayed long-term persistence;and the direct impact of natural factors(path coefficient of 0.617)on the FVC was significantly higher than that of socioeconomic factors(0.167).Among the natural factors,precipitation(0.999)was the most significant.This study reveals the significant impacts of natural and socioeconomic factors on vegetation dynamics in arid regions,and provides a scientific basis for transnational ecological conservation.
基金supported by the National Natural Science Foundation of China(T2261129477,42471297,42101203)the Central Universities Basic Scientific Research Business Fund Project(2024CDJSKXYGG06,2022CDJJJ-010).
文摘Poverty remains one of the most pressing global challenges of this era,affecting millions of people across both developing and developed countries.The poverty alleviation resettlement(PAR)is a policy with Chinese characteristics for eradicating poverty.By integrating the Maslow’s Hierarchy of Needs and Amartya Sen’s Capability Approach,this study developed a theoretical framework to analyze the factors influencing the well-being of poverty alleviation migrants(PAMs).A telephone survey conducted between July and August 2022 in Hubei Province,Guizhou Province,Shaanxi Province,and Chongqing Municipality of China yielded 259 valid questionnaires.Using the partial least squares-structural equation modeling(PLS-SEM),this study revealed that financial accessibility,health level,living conditions,and social networks significantly enhanced the well-being of PAMs,with living conditions having the strongest impact on the well-being of PAMs.Furthermore,the factors affecting well-being varied across age groups.Social networks played a more significant role in the elderly group,whereas health level had a greater impact on the young and middle-aged group.These findings deepen the understanding of the PAR and its effects on the well-being of PAMs,offering valuable insights for policy-makers and practitioners to refine poverty alleviation strategies and enhance social welfare.
基金supported by the National Natural Science Foundationof China (70771080)the National Science Foundation of Hubei Province(20091107)Hubei Province Key Laboratory of Systems Science in Metallurgical Process (B201003)
文摘Partial cooperation models are studied for many years to solve the bilevel programming problems where the follower’s optimal reaction is not unique. However, in these existed models, the follower’s cooperation level does not depend on the leader’s decision. A new model is proposed to solve this deficiency. It is proved the feasibility of the new model when the reaction set of the lower level is lower semicontinuous. And the numerical results show that the new model has optimal solutions when the reaction set of the lower level is discrete, lower semi-continuous and non-lower semi-continuous.
基金supported by the National Natural Science Foundation of China,Nos.41874001 and 41664001Support Program for Outstanding Youth Talents in Jiangxi Province,No.20162BCB23050National Key Research and Development Program,No.2016YFB0501405。
文摘When the total least squares(TLS)solution is used to solve the parameters in the errors-in-variables(EIV)model,the obtained parameter estimations will be unreliable in the observations containing systematic errors.To solve this problem,we propose to add the nonparametric part(systematic errors)to the partial EIV model,and build the partial EIV model to weaken the influence of systematic errors.Then,having rewritten the model as a nonlinear model,we derive the formula of parameter estimations based on the penalized total least squares criterion.Furthermore,based on the second-order approximation method of precision estimation,we derive the second-order bias and covariance of parameter estimations and calculate the mean square error(MSE).Aiming at the selection of the smoothing factor,we propose to use the U curve method.The experiments show that the proposed method can mitigate the influence of systematic errors to a certain extent compared with the traditional method and get more reliable parameter estimations and its precision information,which validates the feasibility and effectiveness of the proposed method.
文摘This paper considers the local linear regression estimators for partially linear model with censored data. Which have some nice large-sample behaviors and are easy to implement. By many simulation runs, the author also found that the estimators show remarkable in the small sample case yet.
基金Supported by the National Natural Science Foundation of China (10571008)the Natural Science Foundation of Henan (092300410149)the Core Teacher Foundationof Henan (2006141)
文摘In this article, a partially linear single-index model /or longitudinal data is investigated. The generalized penalized spline least squares estimates of the unknown parameters are suggested. All parameters can be estimated simultaneously by the proposed method while the feature of longitudinal data is considered. The existence, strong consistency and asymptotic normality of the estimators are proved under suitable conditions. A simulation study is conducted to investigate the finite sample performance of the proposed method. Our approach can also be used to study the pure single-index model for longitudinal data.
