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http://dx.doi.org/10.5351/KJAS.2015.28.2.343

SRC-Stat Package for Fitting Double Hierarchical Generalized Linear Models  

Noh, Maengseok (Department of Statistics, Pukyong National University)
Ha, Il Do (Department of Statistics, Pukyong National University)
Lee, Youngjo (Data Science for Knowledge Creation Research Center, Seoul National University)
Lim, Johan (Data Science for Knowledge Creation Research Center, Seoul National University)
Lee, Jaeyong (Data Science for Knowledge Creation Research Center, Seoul National University)
Oh, Heeseok (Data Science for Knowledge Creation Research Center, Seoul National University)
Shin, Dongwan (Data Science for Knowledge Creation Research Center, Seoul National University)
Lee, Sanggoo (Data Science for Knowledge Creation Research Center, Seoul National University)
Seo, Jinuk (Data Science for Knowledge Creation Research Center, Seoul National University)
Park, Yonhtae (Data Science for Knowledge Creation Research Center, Seoul National University)
Cho, Sungzoon (Data Science for Knowledge Creation Research Center, Seoul National University)
Park, Jonghun (Data Science for Knowledge Creation Research Center, Seoul National University)
Kim, Youkyung (Data Science for Knowledge Creation Research Center, Seoul National University)
You, Kyungsang (Data Science for Knowledge Creation Research Center, Seoul National University)
Publication Information
The Korean Journal of Applied Statistics / v.28, no.2, 2015 , pp. 343-351 More about this Journal
Abstract
We introduce how to fit random effects models via a SRC-Stat statistical package. This package has been developed to fit double hierarchical generalized linear models where mean and dispersion parameters for the variance of random effects and residual variance (overdispersion) can be modeled as random-effect models. The estimates of fixed effects, random effects and variances are calculated by a hierarchical likelihood method. We illustrate the use of our package with practical data-sets.
Keywords
Disease mapping; double hierarchical generalized linear models; hierarchical likelihood; random effects; SRC-stat;
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