Statistical Analysis For Semiparametric Variable-Coefficient Partially Linear Errors-in-Variables With Missing Responses

Semiparametric variable coefficient partly linear model in recent years is one of the hot spot of statistical studies, it covers A lot of parameters, nonparametric regression model, including linear regression model and the partial linear regression model And variable coefficient model is the model of degradation situation. Because the model combining the parameters of linear model and the parameter model, absorbs the respective advantages, Rather than the average linear-in-parameter model or semi-parametric partly linear model has stronger explain capabilities, Avoiding many "dimension curse" problem. Therefore, It in economic, financial and medical fields been widely used.In many practical problems, due to human or system, the reason of the measure error is always exist. Therefore, the study measurement error model of great practical significance. When data can be completely observation arrives, You & Chen parameters variable coefficient partly linear variable contains error model an extensive and in-depth research.But in reality the instrumentation precision, experimental expensive, or respondents refused to answer and other factors, In data collect and analysis process not only can appear measurement error will also easy, resulting in a large number of missing data of generation, So the missing data problems in practical application more aroused people's universal attention. In the background of lack of data, usually a statistical method usually cannot be applied directly, Need for data proper treatment, the method is often first to fill, get on missing value "Completely sample", then press the statistical methods of inference.In the literature, For the missing data processing is usually adopted delete defects unit (C-C methods), weighted or packing method. Commonly used to fill method has linear regression fill method, Non-parameter kernel regression fill method and semi-parametric regression fill method. This paper using C-C method was used to study the response variables random missing semi-parametric variable coefficient partly linear Variable contains error model parameter estimation and study estimated the asymptotic properties.This paper is divided into three chapters. The first chapter for preface and related literature review. The second chapter in response variables are missing at random by using C-C under method to deal with the missing data, Tectonic semi-parametric variable coefficient partly linear variable contains error model parameter estimation, And prove the estimation of asymptotic normality, and using these results are constructed respectively Model parameters based on normal approximation of the asymptotic confidence interval (regions);The third chapter in response variables are missing at random by using C-C under method to deal with the missing data, Tectonic semi-parametric variable coefficient partly linear variable con-tains error model parameters empirical likelihood ratio statistics, Proved empirical likelihood ratio statistics limit distributions was chi-square distribution, And by using this result was constructed model parameters empirical likelihood confidence interval (regions).This article features embodied in the following two aspects. First, in the response variables are missing at random study the semi-parametric variable coefficient Partly linear variable contains error model parameters estimation and asymptotic normality.Second, the empirical likelihood applied to response variables random missing semi-parametric variable coefficient Partly linear variable contains error model, the tectonic model pa-rameters of the empirical likelihood confidence interval (regions).