This paper studies the least squares model averaging methods for two non-nested linear models.It is proved that the Mallows model averaging weight of the true model is root-n consistent.Then the authors develop a pena...This paper studies the least squares model averaging methods for two non-nested linear models.It is proved that the Mallows model averaging weight of the true model is root-n consistent.Then the authors develop a penalized Mallows criterion which ensures that the weight of the true model equals 1 with probability tending to 1 and thus the averaging estimator is asymptotically normal.If neither candidate model is true,the penalized Mallows averaging estimator is asymptotically optimal.Simulation results show the selection consistency of the penalized Mallows method and the superiority of the model averaging approach compared with the model selection estimation.展开更多
We consider the statistical inference for right-censored data when censoring indicators are missing but nonignorable, and propose an adjusted imputation product-limit estimator. The proposed estimator is shown to be c...We consider the statistical inference for right-censored data when censoring indicators are missing but nonignorable, and propose an adjusted imputation product-limit estimator. The proposed estimator is shown to be consistent and converges to a Gaussian process. Furthermore, we develop an empirical processbased testing method to check the MAR (missing at random) mechanism, and establish asymptotic properties for the proposed test statistic. To determine the critical value of the test, a consistent model-based bootstrap method is suggested. We conduct simulation studies to evaluate the numerical performance of the proposed method and compare it with existing methods. We also analyze a real data set from a breast cancer study for an illustration.展开更多
This paper investigates the hypothesis test of the parametric component in partial functional linear regression models.Based on a rank score function,the authors develop a rank test using functional principal componen...This paper investigates the hypothesis test of the parametric component in partial functional linear regression models.Based on a rank score function,the authors develop a rank test using functional principal component analysis,and establish the asymptotic properties of the resulting test under null and local alternative hypotheses.A simulation study shows that the proposed test procedure has good size and power with finite sample sizes.The authors also present an illustration through fitting the Berkeley Growth Data and testing the effect of gender on the height of kids.展开更多
Currently,working with partially observed functional data has attracted a greatly increasing attention,since there are many applications in which each functional curve may be observed only on a subset of a common doma...Currently,working with partially observed functional data has attracted a greatly increasing attention,since there are many applications in which each functional curve may be observed only on a subset of a common domain,and the incompleteness makes most existing methods for functional data analysis ineffective.In this paper,motivated by the appealing characteristics of conditional quantile regression,the authors consider the functional linear quantile regression,assuming the explanatory functions are observed partially on dense but discrete point grids of some random subintervals of the domain.A functional principal component analysis(FPCA)based estimator is proposed for the slope function,and the convergence rate of the estimator is investigated.In addition,the finite sample performance of the proposed estimator is evaluated through simulation studies and a real data application.展开更多
基金supported by the National Natural Science Foundation of China under Grant Nos.11801598,12031016 and 11971323the National Statistical Research Program under Grant No.2018LY96+1 种基金the Beijing Natural Science Foundation under Grant No.1202001NQI Project under Grant No.2022YFF0609903.
文摘This paper studies the least squares model averaging methods for two non-nested linear models.It is proved that the Mallows model averaging weight of the true model is root-n consistent.Then the authors develop a penalized Mallows criterion which ensures that the weight of the true model equals 1 with probability tending to 1 and thus the averaging estimator is asymptotically normal.If neither candidate model is true,the penalized Mallows averaging estimator is asymptotically optimal.Simulation results show the selection consistency of the penalized Mallows method and the superiority of the model averaging approach compared with the model selection estimation.
基金supported by National Natural Science Foundation of China (Grant Nos. 10901162 and 10926073)China Postdoctoral Science Foundation and Foundation of the Key Laboratory of Random Complex Structures and Data Science, Chinese Academy of Sciences+2 种基金supported by National Natural Science Foundation of China (Grant Nos. 10971007 and 11101015)the fund from the government of Beijing (Grant No. 2011D005015000007)supported by National Science Foundation of US (Grant Nos. DMS0806097 and DMS1007167)
文摘We consider the statistical inference for right-censored data when censoring indicators are missing but nonignorable, and propose an adjusted imputation product-limit estimator. The proposed estimator is shown to be consistent and converges to a Gaussian process. Furthermore, we develop an empirical processbased testing method to check the MAR (missing at random) mechanism, and establish asymptotic properties for the proposed test statistic. To determine the critical value of the test, a consistent model-based bootstrap method is suggested. We conduct simulation studies to evaluate the numerical performance of the proposed method and compare it with existing methods. We also analyze a real data set from a breast cancer study for an illustration.
基金supported by the National Natural Science Foundation of China under Grant Nos.1177103211571340 and 11701020+1 种基金the Science and Technology Project of Beijing Municipal Education Commission under Grant Nos.KM201710005032 and KM201910005015the International Research Cooperation Seed Fund of Beijing University of Technology under Grant No.006000514118553。
文摘This paper investigates the hypothesis test of the parametric component in partial functional linear regression models.Based on a rank score function,the authors develop a rank test using functional principal component analysis,and establish the asymptotic properties of the resulting test under null and local alternative hypotheses.A simulation study shows that the proposed test procedure has good size and power with finite sample sizes.The authors also present an illustration through fitting the Berkeley Growth Data and testing the effect of gender on the height of kids.
基金supported by the National Natural Science Foundation of China under Grant No.11771032。
文摘Currently,working with partially observed functional data has attracted a greatly increasing attention,since there are many applications in which each functional curve may be observed only on a subset of a common domain,and the incompleteness makes most existing methods for functional data analysis ineffective.In this paper,motivated by the appealing characteristics of conditional quantile regression,the authors consider the functional linear quantile regression,assuming the explanatory functions are observed partially on dense but discrete point grids of some random subintervals of the domain.A functional principal component analysis(FPCA)based estimator is proposed for the slope function,and the convergence rate of the estimator is investigated.In addition,the finite sample performance of the proposed estimator is evaluated through simulation studies and a real data application.
