Generalized empirical likelihood inference in partially linear errors-in-variables models with longitudinal data

This article is concerned with estimations for longitudinal partial linear models with covariate that is measured with error. We propose a generalized empirical likelihood method by combining correction attenuation and quadratic inference functions. The method takes into accountthe within-subject correlation without involving direct estimation of nuisance parameters in the correlation matrix. We define a generalized empirical likelihood-based statistic for the regression coefficients and residual adjusted empirical likelihood for the baseline function. The empirical log-likelihood ratios are proven to be asymptotically chi-squared, and the corresponding confidence regions are then constructed.Compared with methods based on normal approximations, the generalized empirical likelihood does not require consistent estimators forthe asymptotic variance and bias. Furthermore, a simulation study is conducted to evaluate the performance of the proposed method.

Keywords:

Longitudinal Data, Generalized Empirical Likelihood Confidence Region, Measurement Error, Partially Linear Model,

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Hacettepe Journal of Mathematics and Statistics-Cover

Yayın Aralığı: Yılda 6 Sayı
Başlangıç: 2002
Yayıncı: Hacettepe Üniversitesi Fen Fakultesi

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