非线性半参数EV模型的最大经验似然估计

Acta mathematica scientia,Series A ›› 2012, Vol. 32 ›› Issue (4): 729-743.

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Maximum Empirical Likelihood Estimators in Nonlinear Semiparametric EV Regression Models

FENG San-Ying^1,2, XUE Liu-Gen¹

1.College of Applied Sciences, Beijing University of Technology, Beijing 100022;
2.College of Mathematics and Science, Luoyang Normal University, Henan Luoyang 471022

Received:2010-04-09 Revised:2011-09-03 Online:2012-08-25 Published:2012-08-25
Supported by:
国家自然科学基金(11171012, 11101014, 11001118)、国家社科基金(11CTJ004)、北京市优秀博士学位论文指导教师科技项目(20111000503)和洛阳师范学院青年基金 (2010-QNJJ-001)资助

Abstract

Abstract:

In this paper, we consider the nonlinear semiparametric models with measurement error in the nonparametric part. When the error is ordinarily smooth, we obtain the maximum empirical likelihood estimators of regression coefficient, smooth function and error variance by using the empirical likelihood method. The asymptotic normality and consistency of the proposed estimators are proved under some appropriate conditions. Finite sample performance of the proposed method is illustrated in a simulation study.

Key words: Nonlinear semiparametric model, Errors in variables, Empirical likelihood, Ordinarily smooth, Asymptotic normality

CLC Number:

62G05

FENG San-Ying, XUE Liu-Gen. Maximum Empirical Likelihood Estimators in Nonlinear Semiparametric EV Regression Models[J].Acta mathematica scientia,Series A, 2012, 32(4): 729-743.

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Maximum Empirical Likelihood Estimators in Nonlinear Semiparametric EV Regression Models

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