Generalized empirical likelihood in partially linear modes for longitudinal with non-monotone missing data - 文章详情页

作者：

Liu, J. (Liu, J..) | Xue, L. (Xue, L..) | Hu, Y. (Hu, Y..)

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摘要：

To　study　the　estimation　in　partially　linear　models　for　longitudinal　with　non-monotone　missing　data,　based　on　quadratic　inference　functions,　the　generalized　empirical　likelihood　method　is　used　to　estimate　the　regression　coefficients　and　the　baseline　function,　and　the　corresponding　maximum　empirical　likelihood　estimators　are　derived.　The　empirical　log-likelihood　ratios　are　proven　to　be　asymptotically　chi-squared,　and　the　corresponding　confidence　regions　and　intervals　are　then　constructed.　The　numerical　study　is　conducted　to　compare　the　finite　sample　behavior　of　the　generalized　empirical　likelihood　and　the　normal　approximation-based　method.　©　2016,　Editorial　Department　of　Journal　of　Beijing　University　of　Technology.　All　right　reserved.

关键词：

Generalized empirical likelihood; Longitudinal data; Non-monotone missing; Quadratic inference functions

作者机构：

[ 1 ] [Liu, J.]College of Applied Sciences, Beijing University of Technology, Beijing, 100124, China
[ 2 ] [Liu, J.]College of Mathematics and Sciences, Henan Normal University, Xinxiang, Henan 453007, China
[ 3 ] [Xue, L.]College of Applied Sciences, Beijing University of Technology, Beijing, 100124, China
[ 4 ] [Hu, Y.]College of Applied Sciences, Beijing University of Technology, Beijing, 100124, China
[ 5 ] [Hu, Y.]School of Data Sciences, Zhejiang University of Finance and Economics, Hangzhou, 310018, China

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