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題 名 | Finite Mixture Multivariate Generalized Linear Models Using Gibbs Sampling and E-M Algorithms=以吉氏取樣與E-M算法估算之有限混合型廣義線性模式 |
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作 者 | 楊志堅; 楊志強; | 書刊名 | Proceedings of the National Science Council : Part A, Physical Science and Engineering |
卷 期 | 23:6 1999.11[民88.11] |
頁 次 | 頁695-702 |
分類號 | 319.711 |
關鍵詞 | 吉氏取樣; E-M算法; 有限混合型; 廣義線性模式; Finite mixture models; Multivariate generalized linear models; Gibbs sampling; E-M algorithms; |
語 文 | 英文(English) |
英文摘要 | Finite mixture multivariate generalized linear modeling has been shown to be an important analytic tool for many research fields, for example, image recognition, astronomical data classification, biomedicine diagnosis, and biological classification. Recent statistical and computational advances have further encouraged researchers to explore the modeling possibility using the Bayesian framework. We compare Expectation (E)-Maximization (M) algorithms for maximum likelihood estimation of classical statistics with Gibbs sampling methods of Bayesian statistics in estimating finite mixture multivariate generalized linear models. A Monte Carlo study to compare the two methods is provided for practical reference. We also propose two finite mixture multivariate generalized linear models that can allow more flexibility in modeling substantive applications. The Longitudinal Study of American Youth (LSAY) data set is also analyzed as a practical application. |
本系統中英文摘要資訊取自各篇刊載內容。