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Dealing with multiple local modalities in latent class profile analysis

Authors
Chang, Hsiu-ChingChung, Hwan
Issue Date
12월-2013
Publisher
ELSEVIER
Keywords
Deterministic annealing; Latent stage-sequential process; Markov chain Monte Carlo; Maximum likelihood; Recursive formula
Citation
COMPUTATIONAL STATISTICS & DATA ANALYSIS, v.68, pp.296 - 310
Indexed
SCIE
SCOPUS
Journal Title
COMPUTATIONAL STATISTICS & DATA ANALYSIS
Volume
68
Start Page
296
End Page
310
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/101478
DOI
10.1016/j.csda.2013.07.016
ISSN
0167-9473
Abstract
Parameters for latent class profile analysis (LCPA) are easily estimated by maximum likelihood via the EM algorithm or Bayesian method via Markov chain Monte Carlo. However, the local maximum problem is a long-standing issue in any hill-climbing optimization technique for the LCPA model. To deal with multiple local modalities, two probabilistic optimization techniques using the deterministic annealing framework are proposed. The deterministic annealing approaches are implemented with an efficient recursive formula in the step for the parameter update. The proposed methods are applied to the data from the National Longitudinal Survey of Youth 1997 (NLSY97), a survey that explores the transition from school to work and from adolescence to adulthood in the United States. (C) 2013 Elsevier B.V. All rights reserved.
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