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Optimization-Based Model Fitting for Latent Class and Latent Profile Analyses

Published online by Cambridge University Press:  01 January 2025

Guan-Hua Huang ,
Su-Mei Wang  and
Chung-Chu Hsu
Guan-Hua Huang*
Affiliation:
Institute of Statistics, National Chiao Tung University, Taiwan
Su-Mei Wang
Affiliation:
Institute of Statistics, National Chiao Tung University, Taiwan
Chung-Chu Hsu
Affiliation:
Institute of Statistics, National Chiao Tung University, Taiwan
*
Requests for reprints should be sent to Guan-Hua Huang, Institute of Statistics, National Chiao Tung University, 1001 Ta Hsueh Road, Hsinchu 30010, Taiwan. E-mail: ghuang@stat.nctu.edu.tw
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Abstract

Statisticians typically estimate the parameters of latent class and latent profile models using the Expectation-Maximization algorithm. This paper proposes an alternative two-stage approach to model fitting. The first stage uses the modified k-means and hierarchical clustering algorithms to identify the latent classes that best satisfy the conditional independence assumption underlying the latent variable model. The second stage then uses mixture modeling treating the class membership as known. The proposed approach is theoretically justifiable, directly checks the conditional independence assumption, and converges much faster than the full likelihood approach when analyzing high-dimensional data. This paper also develops a new classification rule based on latent variable models. The proposed classification procedure reduces the dimensionality of measured data and explicitly recognizes the heterogeneous nature of the complex disease, which makes it perfect for analyzing high-throughput genomic data. Simulation studies and real data analysis demonstrate the advantages of the proposed method.

Keywords

classificationfinite mixturehierarchical clusteringhigh-dimensional datak-meansmicroarraytwo-stage approach
Type
Original Paper
Information
Psychometrika , Volume 76 , Issue 4 , October 2011 , pp. 584 - 611
Copyright
Copyright © 2011 The Psychometric Society

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