Estimation for an improved multilevel model based on MCMC algorithm

Author

Min Suqin ; He Xiaoqun

Author_Institution

Sch. of Sci., Commun. Univ. of China, Beijing, China

fYear

2015

fDate

23-25 May 2015

Firstpage

4127

Lastpage

4131

Abstract

Independence among groups is assumed in traditional multilevel models. There is often spatial interaction between districts when data is grouped by geographical units. The individual will be influenced by adjacent regions, and assumption of level-2 residual´s distribution in traditional multilevel model will be violated. Spatial statistical models are introduced into the multilevel model in order to deal with such spatial multilevel data. And Bayesian inferences based on MCMC method for fixed effects, variance-covariance components and spatial regression parameters in improved multilevel model are given.

Keywords

Bayes methods; Markov processes; Monte Carlo methods; geographic information systems; inference mechanisms; regression analysis; Bayesian inferences; MCMC algorithm; fixed effects; geographical units; improved multilevel model; level-2 residual distribution; spatial interaction; spatial multilevel data; spatial regression parameters; spatial statistical models; variance-covariance components; Bayes methods; Biological system modeling; Correlation; Data models; Education; Estimation; Spatial databases; Bayesian Inferences; MCMC Algorithm; Multilevel Models; Spatial Effect;

fLanguage

English

Publisher

ieee

Conference_Titel

Control and Decision Conference (CCDC), 2015 27th Chinese

Conference_Location

Qingdao

Print_ISBN

978-1-4799-7016-2

Type

conf

DOI

10.1109/CCDC.2015.7162655

Filename

7162655