考虑了纵向数据下变系数部分非线性模型中参数分量置信域的构造. 为避免纵向数据组内相关性给构造经验似然比带来的困难, 提出了参数分量的分块经验对数似然比统计量, 证明了所构造的分块经验似然比统计量渐近于卡方分布. 数据模拟表明所提出的分块经验似然方法在置信域大小和覆盖率方面要优于正态逼近方法.

In this paper, empirical likelihood inferences for the parameter component in varying coefficient partially nonlinear models with longitudinal data are investigated. We propose a block empirical likelihood procedure to handle the inter-series dependence of the longitudinal data. A block empirical log-likelihood ratio statistic is suggested and its asymptotic distribution is obtained. Simulation studies indicate that our proposed method performs better than the method based on normal approximations in terms of relatively higher coverage probabilities and smaller confidence regions.