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RISS 인기검색어
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- 저자 : Xue Liugen (검색결과 4 건)
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Ruiqin Tian,Liugen Xue,Yuping Hu 한국통계학회 2015 Journal of the Korean Statistical Society Vol.44 No.3
In this paper, we consider the problem of variable selection for varying coefficient partially linear models with longitudinal data. A new variable selection procedure is proposed based on smooth-threshold generalized estimating equation (SGEE). The proposed procedure simultaneously selects significant variables in the parametric components and the nonparametric components. The approach avoids the convex optimization problem and is flexible and easy to implement. The consistency and asymptotic normality of the resulting estimators are established. Extensive Monte Carlo simulation studies are conducted to examine the finite sample performance of the proposed variable selection procedure. The proposed procedure is further illustrated by an application.
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Triangular angles parameterization for the correlation matrix of bivariate longitudinal data
Lu Fei,Xue Liugen,Wang Zhaoliang 한국통계학회 2020 Journal of the Korean Statistical Society Vol.49 No.2
Multivariate longitudinal data is often encountered in the jobs of statisticians and practitioners. It is challenging to model the covariance matrix due to the complex structure of correlations among multiple responses. For this modeling task, several effective Cholesky decomposition based methods have been studied. However, direct interpretation of the covariation structure among multiple responses is still less well investigated to the best of our knowledge. In this paper, we propose a joint meanvariance correlationmodelingmethod based on the triangular angles parameterization (TAP) for the correlation matrix of bivariate longitudinal data. The proposed unconstrained parameterization is able to automatically eliminate the positive definiteness constraint of the correlation matrix and leads to the aforementioned direct interpretation. Furthermore, the variance matrix is diagonal rather than block-diagonal, so the positive-definiteness constraint of this matrix can be easily satisfied. The entries of the proposed decomposition are modeled by regression models, and the maximum likelihood estimators of regression parameters are obtained. The resulting estimators are shown to be consistent and asymptotically normal. Simulations and a study of poplar growth illustrate that the proposed method performs well.
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Robust estimation with a modified Huber’s loss for partial functional linear models based on splines
Cai Xiong,Xue Liugen,Lu Fei 한국통계학회 2020 Journal of the Korean Statistical Society Vol.49 No.4
In this article, we consider a new robust estimation procedure for the partial functional linear model (PFLM) with the slope function approximated by spline basis functions. This robust estimation procedure applies a modifed Huber’s function with tail function replaced by the exponential squared loss (ESL) to achieve robustness against outliers. A data-driven procedure is presented for selecting the tuning parameters of the new estimation method, which enables us to reach better robustness and efciency than other methods in the presence of outliers or non-normal errors. We construct robust estimators of both parametric coefcients and function coefcient in the PFLM. Moreover, some asymptotic properties of the resulting estimators are established. The fnite sample performance of our proposed method is studied through simulations and illustrated with a data example.
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An empirical likelihood check with varying coefficient fixed effect model with panel data
Li Wanbin,Xue Liugen 한국통계학회 2022 Journal of the Korean Statistical Society Vol.51 No.1
Semiparametric models are often used to analyze panel data for a good trade-off between parsimony and flexibility. In this paper, we investigate a fixed effect model with a possible varying coefficient component. On the basis of empirical likelihood method, the coefficient functions are estimated as well as their confidence intervals. The estimation procedures are easily implemented. An important problem of the statistical inference with the varying coefficient model is to check the constant coefficient about the regression functions. We further develop checking procedures by constructing empirical likelihood ratio statistics and establishing the Wilks theorems. Finally, some numerical simulations and a real data analysis is presented to assess the finite sample performance.
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