Subject-wise empirical likelihood inference in partial linear models for longitudinal data

Lianfen Qian and Suojin Wang

Computational Statistics & Data Analysis, 2017, vol. 111, issue C, 77-87

Abstract: In analyzing longitudinal data, within-subject correlations are a major factor that affects statistical efficiency. Working with a partially linear model for longitudinal data, a subject-wise empirical likelihood based method that takes the within-subject correlations into consideration is proposed to estimate the model parameters. A nonparametric version of the Wilks Theorem for the limiting distribution of the empirical likelihood ratio, which relies on a kernel regression smoothing method to properly centered data, is derived. The estimation of the nonparametric baseline function is also considered. A simulation study and an application are reported to investigate the finite sample properties of the proposed method. The numerical results demonstrate the usefulness of the proposed method.

Keywords: Confidence region; Empirical likelihood; Longitudinal data; Maximum empirical likelihood estimator (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:111:y:2017:i:c:p:77-87

DOI: 10.1016/j.csda.2017.02.001

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