摘要
非线性再生散度随机效应模型是指数族非线性随机效应模型和非线性再生散度模型的推广和发展.通过视模型中的随机效应为假想的缺失数据和应用Metropolis-Hastings(MH)算法,提出了模型参数极大似然估计的Monte-Carlo EM(MCEM)算法,并用模拟研究和实例分析说明了该算法的可行性.
Nonlinear reproductive dispersion models are natural extensions of exponential family nonlinear mixed models and Nonlinear reproductive dispersion models. After treating the random effects in the models as hypothetical missing data, this paper proposes an EM algorithm with Markov chain Monte-Carlo method for maximum likelihood estimation in the models. The proposed procedure is illustrated by a simulation study and a real example.
出处
《生物数学学报》
CSCD
北大核心
2009年第2期355-362,共8页
Journal of Biomathematics
基金
国家自然科学基金资助项目(10761011)
贵州省科学技术基金资助项目
