碩士 / 中原大學 / 數學研究所 / 89 / Abstract
Mixtures of distributions are used to analyze the grouped categorical data. The
estimation of parameters is an important step for mixture distributions. According to
Yang and Yu (1999), they described maximum likelihood estimation (MLE) algorithm,
expection maximization (EM) algorithm, classification maximum likelihood (CML)
algorithm and fuzzy classification maximum likelihood (FCML) algorithm to estimate
the parameters of a mixture of multivariate Bernoulli distributions. In this paper, we
will extend EM, CML and FCML algorithms to regression analysis to describe the
effects of the explanatory variables on the response variable. This paper focus on
binary responses about the logistic regression analysis with a latent class model. We
then use the extend algorithms to estimate the parameters of the latent class logistic
regression model. The numerical comparisons are also made. Finally, we give
numerical results for these algorithms.
| Identifer | oai:union.ndltd.org:TW/089CYCU5479011 |
| Date | January 2001 |
| Creators | Hui-Min Chen, 陳慧敏 |
| Contributors | Min-Shen Yang, 楊敏生 |
| Source Sets | National Digital Library of Theses and Dissertations in Taiwan |
| Language | en_US |
| Detected Language | English |
| Type | 學位論文 ; thesis |
| Format | 33 |
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