缺失数据下半参数变系数部分线性模型的统计推断

Abstract

Abstract:

In this paper, we consider the statistical inference for semiparametric varying coefficient partially linear model with covariates missing at random. The profile least-squares estimators for the unknown parametric and coefficient functions are obtained by inverse probability weighted least-squares method. The asymptotic normality of the proposed estimators are proved under some appropriate conditions. In addition, we constructed an empirical log-likelihood ratio statistic for the unknown parametric components, and it is shown that the proposed statistic has the asymptotic chi-square distribution, and hence it can be used to construct the confidence regions of the parameter. Simulation studies and a real data analysis are conducted to examine the finite sample performance of the proposed procedure.

Key words: Semiparametric varying coefficient partially linear model, Missing data, Inverse probability weighted, Asymptotic normality, Empirical likelihood

CLC Number:

O212.7

Chen Panpan, Feng Sanying, Xue Liugen. Statistical Inference for Semiparametric Varying Coefficient Partially Linear Model with Missing Data[J].Acta mathematica scientia,Series A, 2015, 35(2): 345-358.

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References

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Statistical Inference for Semiparametric Varying Coefficient Partially Linear Model with Missing Data

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