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    188宝金博页面版: Weighted local linear CQR for varying-coefficient models with missing covariates

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    内容提示： TEST (2015) 24:583–604DOI 10.1007/s11749-014-0425-zORIGINAL PAPERWeighted local linear CQR for varying-coeff i cientmodels with missing covariatesLinjun Tang · Zhangong ZhouReceived: 31 August 2014 / Accepted: 25 December 2014 / Published online: 21 January 2015? Sociedad de Estadística e Investigación Operativa 2015Abstract This paper considers composite quantile regression (CQR) estimation andinference for varying-coeff i cient models with missing covariates. We propose theweighted local linear CQR ...

    文档格式：PDF | 页数：22 | 浏览次数：21 | 上传日期：2016-01-21 22:40:28 | 文档星级：
    TEST (2015) 24:583–604DOI 10.1007/s11749-014-0425-zORIGINAL PAPERWeighted local linear CQR for varying-coeff i cientmodels with missing covariatesLinjun Tang · Zhangong ZhouReceived: 31 August 2014 / Accepted: 25 December 2014 / Published online: 21 January 2015© Sociedad de Estadística e Investigación Operativa 2015Abstract This paper considers composite quantile regression (CQR) estimation andinference for varying-coeff i cient models with missing covariates. We propose theweighted local linear CQR (WLLCQR) estimators for unknown coeff i cient functionwhen selection probabilities are known, estimated nonparametrically or parametri-cally. Theoretical and numerical results demonstrate that the WLLCQR estimatorswithestimatingweightsaremoreeff i cientthanthetrueweights.Moreover,agoodness-of-f i t test based on the WLLCQR f i ttings is developed to test whether the coeff i cientfunctionsareactuallyvarying.Thef i nite-sampleperformanceoftheproposedmethod-ology is assessed by simulation studies. A real data set is conducted to illustrate ourproposed method.Keywords Composite quantile regression · Varying-coeff i cient model ·Missing at random · Inverse probability weightingMathematics Subject Classif i cation 60G70 · 60F051 IntroductionVarying-coeff i cientmodelswereoriginallyproposedbyHastieandTibishirani(1993)to examine how regression coeff i cients change over some factors such as time.A classical varying-coeff i cient model is of the following form:Y = X T β(T) + ε, (1)L. Tang ( B ) · Z. ZhouDepartment of Statistics, Jiaxing University, Jiaxing 314001, People’s Republic of Chinae-mail: tljlqz@163.com123

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