Parameter Estimation for Latent Class Models via K-means and Hierarchical Procedures

• Main Author: 王素梅
• Other Authors: 黃冠華
• Format: Others
• Language: en_US
• Published: 2007
• Online Access: http://ndltd.ncl.edu.tw/handle/96260380305817809778

碩士 === 國立交通大學 === 統計學研究所 === 95 === The aim of the study is to estimate the parameters of the latent class models via clustering methods. We use k-means and hierarchical ideas of clustering methods with the correlation (or covariance) among items as the distance measure to group objects such that, for all objects who belong to the same latent class, items are ”independent”. By viewing the estimated latent class as known variable, it becomes easy to estimate the parameters in the regression extension of latent class analysis (RLCA) model. The results of our simulation study display that the k-means method with the correlation (or covariance) measurement performed well, but the hierarchical method with the covariance measurement didn’t perform well.