Monte Carlo EM algorithm for two-component mixture of generalized linear random effects models with varying coefficients

Author

Xingcai Zhou ; Changchun Tan

Author_Institution

Dept. of Math. & Comput. Sci., Tongling Univ., Tongling, China

Volume

1

fYear

2011

fDate

12-14 Aug. 2011

Firstpage

17

Lastpage

20

Abstract

Generalized linear models have many applications in agriculture, biology, and so on. With the need of applications, it was extended from various ways for more general cases. The paper proposes an extended finite mixture of generalized linear random effects models (GLMMs) with Varying Coefficients, based on the finite mixture distribution and GLMMs with varying coefficients, then parameters are estimated via Monte Carlo EM (MCEM) algorithm.

Keywords

Monte Carlo methods; statistical distributions; GLMM; Monte Carlo EM algorithm; finite mixture distribution; generalized linear random effect model; Approximation algorithms; Biological system modeling; Computational modeling; Data models; Educational institutions; Mathematical model; Monte Carlo methods; EM algorithm; Generalized linear models; Monte Carlo; Random effects; Varying coefficients;

fLanguage

English

Publisher

ieee

Conference_Titel

Electronic and Mechanical Engineering and Information Technology (EMEIT), 2011 International Conference on

Conference_Location

Harbin, Heilongjiang, China

Print_ISBN

978-1-61284-087-1

Type

conf

DOI

10.1109/EMEIT.2011.6022859

Filename

6022859