Statistical inference for restricted partially linear varying coefficient errors-in-variables models

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• 作者：Chuanhua Wei ^{chweisd@yahoo.com.cn}
• 关键词：Errors-in-variables ; Partially linear varying coefficient model ; Profile least-squares approach ; Restricted estimator
• 刊名：Journal of Statistical Planning and Inference
• 出版年：2012
• 期刊代码：36_03783758
• 类别：mt
• 出版时间：August, 2012
• 卷：142
• 期：8
• 页码：2464-2472
• 文件大小：182 K

摘要

As a useful extension of partially linear models and varying coefficient models, the partially linear varying coefficient model is useful in statistical modelling. This paper considers statistical inference for the semiparametric model when the covariates in the linear part are measured with additive error and some additional linear restrictions on the parametric component are available. We propose a restricted modified profile least-squares estimator for the parametric component, and prove the asymptotic normality of the proposed estimator. To test hypotheses on the parametric component, we propose a test statistic based on the difference between the corrected residual sums of squares under the null and alterative hypotheses, and show that its limiting distribution is a weighted sum of independent chi-square distributions. We also develop an adjusted test statistic, which has an asymptotically standard chi-squared distribution. Some simulation studies are conducted to illustrate our approaches.