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#1. Overview

Categorical Latent Variable ModelingBergman and Magnusson(1997)은 Latent class analysis가 Person-oriented approches라고 함Categorical Latent Variable ModelingLatent class analysis (LCA)Joint latent class (JLCA)Latent transition analysis (LTA)Latent class profile analysis (LCPA)Latent Class analysislatent class analysis(LCA) is statistical method for classifying individuals into populatio..

  • format_list_bulleted Statistic Class/Latent Class lecture
  • · 2023. 12. 20.
  • textsms

#3. Parameter Estimation and Model Selection

Latent Variable Mdoeling 3. Parameter Estimation and Model selection Model estimation 모수추정은 maximum-likelihood(ML)과 Bayesian methods로 가능 LCA에서 모수추정은 ML방식에서는 EM알고리즘 or Newton Raphson 알고리즘을 사용 Upon Convergence, Standard error는 inverting the negative Hessian matrix에 의해 성립된다. 즉, the negative second derivative matrix of loglikelihood function 다른 finite mixure models와 같이 LCA는 likelihood function에서 특이항..

  • format_list_bulleted Statistic Class/Latent Class lecture
  • · 2023. 12. 3.
  • textsms
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