Empirical Likelihood Inferences for Semiparametric Varying-Coefficient Partially Linear Models with Longitudinal Data

Zhao, Peixin; Xue, Liugen

doi:10.1080/03610920902923510

Cited by 23 publications

(10 citation statements)

References 12 publications

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“…In addition, note that n i is bounded, and by [18], we have θ (u)−θ (u) = O p ((nh) −1/2 ) and β (2) −β (2) = O p (n −1/2 ). Hence, it is easy to show that s 1 = O p (h −1/2 ) and s 2 = O p (1), which can not converge to zero.…”

Section: Test Statistic For the Parametric Componentsmentioning

confidence: 97%

“…This is a standard varying coefficient partially linear model, so we can obtain the estimatorsθ (u) and β (2) by the method in [18]. More specifically, let…”

Section: Test Statistic For the Parametric Componentsmentioning

confidence: 99%

“…Wang et al [13] considered the quantile regression procedure for models (1). Based on the empirical likelihood method, You and Zhou [17] and Zhao and Xue [18] studied the construction of the confidence regions of parametric and nonparametric components. Furthermore, Zhao and Xue [19] studied the variable selection for varying coefficient partially linear model with missing response at random.…”

Section: Introductionmentioning

confidence: 99%

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Semiparametric empirical likelihood tests in varying coefficient partially linear models with repeated measurements

Zhao

Yang

2015

Statistical Methodology

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Section: Test Statistic For the Parametric Componentsmentioning

confidence: 97%

“…This is a standard varying coefficient partially linear model, so we can obtain the estimatorsθ (u) and β (2) by the method in [18]. More specifically, let…”

Section: Test Statistic For the Parametric Componentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Semiparametric empirical likelihood tests in varying coefficient partially linear models with repeated measurements

Zhao

Yang

2015

Statistical Methodology

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“…Next we consider A 2 . Note that X i is the centered covariate, then by Lemma A.2 in [33], we have that…”

Section: Proof Of Theorem 22mentioning

confidence: 99%

Penalized Empirical Likelihood based Variable Selection for Partially Linear Quantile Regression Models with Missing Responses

Tang¹,

Zhao²

2016

HJMS

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In this paper, we consider variable selection for partially linear quantile regression models with missing response at random. We first propose a profile penalized empirical likelihood based variable selection method, and show that such variable selection method is consistent and satisfies sparsity. Further more, to avoid the influence of nonparametric estimator on the variable selection for the parametric components, we also propose a double penalized empirical likelihood variable selection method. Some simulation studies and a real data application are undertaken to assess the finite sample performance of the proposed variable selection methods, and simulation results indicate that the proposed variable selection methods are workable.

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“…Xue and Zhu (2007b) considered the EL inference for a varying coefficient model with longitudinal data. Recently, the semiparametric varying-coefficient partially linear models and singleindex models with longitudinal data were studied by Zhao and Xue (2010) and Li et al (2010), respectively. A common future of these articles is that the data dependence within each subject is not taken into consideration.…”

Section: Introductionmentioning

confidence: 99%

Generalized Empirical Likelihood Inference in Generalized Linear Models for Longitudinal Data

Tian

Xue

2014

Communications in Statistics - Theory and Methods

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In this article, the generalized linear model for longitudinal data is studied. A generalized empirical likelihood method is proposed by combining generalized estimating equations and quadratic inference functions based on the working correlation matrix. It is proved that the proposed generalized empirical likelihood ratios are asymptotically chi-squared under some suitable conditions, and hence it can be used to construct the confidence regions of the parameters. In addition, the maximum empirical likelihood estimates of parameters are obtained, and their asymptotic normalities are proved. Some simulations are undertaken to compare the generalized empirical likelihood and normal approximation-based method in terms of coverage accuracies and average areas/lengths of confidence regions/intervals. An example of a real data is used for illustrating our methods.

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Empirical Likelihood Inferences for Semiparametric Varying-Coefficient Partially Linear Models with Longitudinal Data

Cited by 23 publications

References 12 publications

Semiparametric empirical likelihood tests in varying coefficient partially linear models with repeated measurements

Semiparametric empirical likelihood tests in varying coefficient partially linear models with repeated measurements

Penalized Empirical Likelihood based Variable Selection for Partially Linear Quantile Regression Models with Missing Responses

Generalized Empirical Likelihood Inference in Generalized Linear Models for Longitudinal Data

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