지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 한국자료분석학회 Journal of The Korean Data Analysis Society Journal of The Korean Data Analysis Society 제11권 제6호
- 발행연도
- 2009.1
- 수록면
- 2,893 - 2,904 (12page)
이용수
초록· 키워드
오류제보하기
Various insurance data with independent responses have been widely analyzed using generalized linear models(GLMs). Recently, the analyses of clustered insurance data which are usually clustered by policies, regions, divisions or individuals have been studied. Such data often have not only a correlation within clusters but also a heterogeneity between clusters. Thus the data can be analyzed by using extended GLMs with random effects. However, their usual inference methods require intractable integrations and don't provide a proper prediction for random effects. In this paper we propose a new framework for analyzing the clustered claim data using hierarchical GLMs(HGLMs. Lee and Nelder, 1996). For the inferences we use the hierarchical likelihood(h-likelihood) which avoids such difficult integrations and provides a statistically efficient procedure. Furthermore, we also propose how to construct the prediction intervals of random effects which are very useful in investigating a potential heterogeneity across clusters. The proposed method is illustrated with a real data set about the number of third party claims which are clustered by 13 divisions in Australia.
목차
등록된 정보가 없습니다.
참고문헌 (28)
참고문헌 신청
Aitkin, M / 1989 / Statistical modelling in GLIMs / Oxford University press
Akaike, H / 1973 / Information theory and an extension of the maximum likelihood principle, In: 2nd International Symposium on Information Theory / Akademiai Kiado : 267 ~ 281
Booth, J. G / 1998 / Standard errors of prediction in generalized linear mixed models / Journal of the American Statistical Association 93 : 262 ~ 272
Goldstein, H / 2003 / Multilevel statistical models (3rd Edn.) / Arnold
Golub, G. H / 1969 / Calculation of Gaussian quadrature rules / Mathematics of Computation 23 : 221 ~ 230
Akaike, H / 1973 / Information theory and an extension of the maximum likelihood principle, In: 2nd International Symposium on Information Theory / Akademiai Kiado : 267 ~ 281
Booth, J. G / 1998 / Standard errors of prediction in generalized linear mixed models / Journal of the American Statistical Association 93 : 262 ~ 272
Goldstein, H / 2003 / Multilevel statistical models (3rd Edn.) / Arnold
Golub, G. H / 1969 / Calculation of Gaussian quadrature rules / Mathematics of Computation 23 : 221 ~ 230
함께 읽어보면 좋을 논문
논문 유사도에 따라 DBpia 가 추천하는 논문입니다. 함께 보면 좋을 연관 논문을 확인해보세요!
-
A Random-Effect Prediction Approach for Clustered Binominal Data
Journal of The Korean Data Analysis Society
2010 .01
-
Joint HGLM approach for repeated measures and survival data
한국데이터정보과학회지
2016 .08
-
On the Silhouette Plot in Cluster Analysis
Journal of The Korean Data Analysis Society
2009 .01
-
군집화 기법을 활용한 산업집적분석 사례 연구
Journal of The Korean Data Analysis Society
2019 .01
-
Analysis of Multivariate Binary Random Effect Models using Hierarchical Likelihood Approach
Journal of The Korean Data Analysis Society
2019 .01
-
Second - Order REML for Random Effects Models
한국데이터정보과학회지
2001 .06
-
Goodness-of-Fit for the Clustered Binomial Models
Journal of The Korean Data Analysis Society
2015 .01
-
A HGLM framework for Meta-Analysis of Clinical Trials with Binary Outcomes
한국데이터정보과학회지
2008 .12
-
On Estimation of Random Effect in Poisson HGLMs
한국데이터정보과학회지
2008 .03
-
On the assessment of validity and stability in cluster analysis
Journal of The Korean Data Analysis Society
2005 .01
-
ML estimation using Poisson HGLM approach in semi-parametric frailty models
한국데이터정보과학회지
2016 .10
이 논문의 저자 정보
최근 본 자료
댓글(0)
0