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RISS 인기검색어
- 검색키워드
- 저자 : Peixin Zhao (검색결과 5 건)
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A new orthogonality-based estimation for varying-coefficient partially linear models
Peixin Zhao,Yiping Yang 한국통계학회 2019 Journal of the Korean Statistical Society Vol.48 No.1
Varying coefficient partially linear models are usually used for longitudinal data analysis, and an interest is mainly to improve efficiency of regression coefficients. By the orthogonality estimation technology and the quadratic inference function method, we propose a new orthogonality-based estimation method to estimate parameter and nonparametric components in varying coefficient partially linear models with longitudinal data. The proposed procedure can separately estimate the parametric and nonparametric components, and the resulting estimators do not affect each other. Under some mild conditions, we establish some asymptotic properties of the resulting estimators. Furthermore, the finite sample performance of the proposed procedure is assessed by some simulation experiments.
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Estimation and inference for additive partially nonlinear models
Xiaoshuang Zhou,Peixin Zhao,Zehui Liu 한국통계학회 2016 Journal of the Korean Statistical Society Vol.45 No.4
In this paper, we extend the additive partially linear model to the additive partially nonlinear model in which the linear part of the additive partially linear model is replaced by a nonlinear function of the covariates. A profile nonlinear least squares estimation procedure for the parameter vector in nonlinear function and the nonparametric functions of the additive partially nonlinear model is proposed and the asymptotic properties of the resulting estimators are established. Furthermore, we apply the empirical likelihood method to the additive partially nonlinear model. An empirical likelihood ratio for the parameter vector and a residual adjusted empirical likelihood ratio for the nonparametric functions have been proposed. Wilks phenomenon is proved and the confidence regions for the parametric vector and the nonparametric functions are constructed. Some simulations have been conducted to assess the performance of the proposed estimating procedures. The results have demonstrated that both the procedures perform well in finite samples. Compared with the results from the empirical likelihood method with those from the profile nonlinear least squares method, the empirical likelihood method performs better in terms of coverage probabilities and average widths of confidence bands.
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Xiaoshuang Zhou,Peixin Zhao,Lu Lin 한국통계학회 2014 Journal of the Korean Statistical Society Vol.43 No.1
Empirical likelihood inferences for the parameter component in an additive partially linearerrors-in-variables model with longitudinal data are investigated in this article. Acorrected-attenuation block empirical likelihood procedure is used to estimate the regressioncoefficients, a corrected-attenuation block empirical log-likelihood ratio statistic issuggested and its asymptotic distribution is obtained. Compared with the method based onnormal approximations, our proposed method does not require any consistent estimatorfor the asymptotic variance and bias. Simulation studies indicate that our proposed methodperforms better than the method based on normal approximations in terms of relativelyhigher coverage probabilities and smaller confidence regions. Furthermore, an example ofan air pollution and health data set is used to illustrate the performance of the proposedmethod.
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Liu Changqing,Zhao Peixin,Yang Yiping 한국통계학회 2021 Journal of the Korean Statistical Society Vol.50 No.1
In this paper, we consider the statistical inferences for a class of partially linear models with high dimensional endogenous covariates, when high dimensional instrumental variables are also available. A regularized estimation procedure is proposed for identifying the optimal instrumental variables, and estimating covariate efects of the parametric and nonparametric components. Under some conditions, some theoretical properties are studied, such as the consistency of the optimal instrumental variable identifcation and signifcant covariate selection. Furthermore, some simulation studies and a real data analysis are carried out to examine the fnite sample performance of the proposed method.
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Zhang Lei,Ma Li,Zhao Peixin 보안공학연구지원센터 2016 International Journal of u- and e- Service, Scienc Vol.9 No.9
Currently, the uneven distribution of logistics resources, the imbalance between supply and demand, and the unreasonable structure in the development of regional economy are important topics for governments and researchers. Innovatively, this paper uses biclustering analysis, which is a widely used method in bioinformatics, to evaluate regional logistics development levels. This paper deeply analyzes the similarity of similar regions under certain indicators from logistics demand as well as basic supply capacity based on the dimensions of logistics indexes and the actual circumstances of cities, and puts forward relevant suggestions through analyzing problems of logistic development of 17 cities in Shandong province. The results indicate that biclustering analysis can be very useful in providing reference for policy making on regional logistics development.
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