文献类型： 外文期刊

作者： Wei, Hongjie ¹ ; Zhang, Wenzhuan ² ;

作者机构： 1.Chinese Acad Trop Agr Sci, Rubber Res Inst, Danzhou 571737, Hainan, Peoples R China

2.Guizhou Coll Finance & Econ, Sch Math & Stat, Guiyang 550004, Guizhou, Peoples R China

关键词： Simplex distribution;nonlinear mixed models;maximum likelihood estimates;MCEM algorithm;M-H algorithm;Newton-Raphson iteration;longitudinal proportional data

期刊名称：INTERNATIONAL JOURNAL OF BIOMATHEMATICS （ 影响因子：2.053； 五年影响因子：1.564 ）

ISSN： 1793-5245

年卷期： 2009 年 2 卷 1 期

页码：

收录情况： SCI

摘要： Longitudinal continuous proportional data is common in many fields such as biomedical research, psychological research and so on. As shown in [16], such data can be fitted with simplex models. Based on the original models of [16] which assumed a fixed effect for every subject, this paper extends the models by adding random effects and proposes simplex distribution nonlinear mixed models which are one kind of nonlinear reproductive dispersion mixed models. By treating the random effects in the models as hypothetical missing data and applying Metropolis-Hastings (M-H) algorithm, this paper develops an EM algorithm with Markov chain Monte-Carlo method for maximum likelihood estimation in the models. The method is illustrated with the same data from an ophthalmology study on the use of intraocular gas in retinal surgeries in [16] for ease of comparison.