A profile-type smoothed score function for a varying coefficient partially linear model

JOURNAL OF MULTIVARIATE ANALYSIS

卷:102

Article

Li, Gaorong¹  Feng, Sanying²  Peng, Heng³ 

[1] Beijing Univ Technol, Coll Appl Sci, Beijing 100124, Peoples R China

[2] Luoyang Normal Univ, Coll Math & Sci, Luoyang 471022, Peoples R China

[3] Hong Kong Baptist Univ, Dept Math, Hong Kong, Hong Kong, Peoples R China

关键词: Varying coefficient partially linear model;    Local likelihood;    Profile-type smoothed score function;    Confidence region;    Curse of dimensionality;   

DOI  :  10.1016/j.jmva.2010.10.007

来源: Elsevier

【 摘 要 】

The varying coefficient partially linear model is considered in this paper When the plug-in estimators of coefficient functions are used the resulting smoothing score function becomes biased due to the slow convergence rate of nonparametric estimations To reduce the bias of the resulting smoothing score function a profile-type smoothed score function is proposed to draw inferences on the parameters of interest without using the quasi-likelihood framework the least favorable curve a higher order kernel or under-smoothing The resulting profile-type statistic is still asymptotically Chi-squared under some regularity conditions The results are then used to construct confidence regions for the parameters of interest A simulation study is carried out to assess the performance of the proposed method and to compare It with the profile least-squares method A real dataset is analyzed for illustration (C) 2010 Elsevier Inc All rights reserved

【 授权许可】

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