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Estimation for Partially Linear Models with Missing Responses:the Fixed Design Case

Estimation for Partially Linear Models with Missing Responses:the Fixed Design Case
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摘要 Suppose that we have a partially linear model Yi = xiβ + g(ti) +εi with independent zero mean errors εi, where (xi,ti, i = 1, ... ,n} are non-random and observed completely and (Yi, i = 1,...,n} are missing at random(MAR). Two types of estimators of β and g(t) for fixed t are investigated: estimators based on semiparametric regression and inverse probability weighted imputations. Asymptotic normality of the estimators is established, which is used to construct normal approximation based confidence intervals on β and g(t). Results are reported of a simulation study on the finite sample performance of the estimators and confidence intervals proposed in this paper. Suppose that we have a partially linear model Yi = xiβ + g(ti) +εi with independent zero mean errors εi, where (xi,ti, i = 1, ... ,n} are non-random and observed completely and (Yi, i = 1,...,n} are missing at random(MAR). Two types of estimators of β and g(t) for fixed t are investigated: estimators based on semiparametric regression and inverse probability weighted imputations. Asymptotic normality of the estimators is established, which is used to construct normal approximation based confidence intervals on β and g(t). Results are reported of a simulation study on the finite sample performance of the estimators and confidence intervals proposed in this paper.
出处 《Acta Mathematicae Applicatae Sinica》 SCIE CSCD 2014年第2期447-472,共26页 应用数学学报(英文版)
基金 Supported by the National Natural Science Foundation of China(No.11271088,11361011,11201088) Guangxi"Bagui Scholar"Special Project Foundation the Natural Science Foundation of Guangxi(No.2013GXNS-FAA019004,2013GXNSFAA019007,2013GXNSFBA019001)
关键词 partially linear model fixed design point missing at random confidence interval partially linear model fixed design point missing at random confidence interval
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