Abstract
In this article, we develop a statistical inference technique for the unknown coefficient functions in the varying coeffi- cient model with random effect. A residual-adjusted block empirical likelihood (RABEL) method is suggested to inves- tigate the model by taking the within-subject correlation into account. Due to the residual adjustment, the proposed RABEL is asymptotically chi-squared distribution. We illustrate the large sample performance of the proposed method via Monte Carlo simulations and a real data application.
Highlights
Varying coefficient model has been widely used to model all kinds of data
Both [3] and [4] proposed effective inference procedure for the varying coefficient model and applied them to the analysis of CD4 count data, whose detailed information can be referred to [5], none of them considered the within-subject correlation of longitudinal data
Random effect model is frequently employed to exploit the characteristics of longitudinal data over several time periods
Summary
Varying coefficient model has been widely used to model all kinds of data. One popular application is the analysis of the longitudinal data (e.g. [1,2]). Except for [3,13] studied an empirical likelihood method for the varying coefficient error-in-variable models with longitudinal data Both of them did not consider incorporating the within-subject correlation. We propose a residual-adjusted block empirical likelihood (RABEL) method for the varying coefficient model with random effect to incorporate the within-subject correlation for longitudinal data. This approach is appealing in that it can construct the confidence interval for the unknown coefficient function, and improve estimation efficiency through considering the within-subject.
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