Recent developments in maximum likelihood estimation of MTMM models for categorical data

Maximum likelihood (ML) estimation of categorical multitrait-multimethod (MTMM) data is challenging because the likelihood involves high-dimensional integrals over the crossed method and trait factors, with no known closed-form solution. The purpose of the study is to introduce three newly developed ML methods that are eligible for estimating MTMM models with categorical responses: Variational maximization-maximization (e.g., Rijmen and Jeon, 2013), alternating imputation posterior (e.g., Cho and Rabe-Hesketh, 2011), and Monte Carlo local likelihood (e.g., Jeon et al., under revision). Each method is briefly described and its applicability for MTMM models with categorical data are discussed.

• Publication year

  2014

• Venue

  Frontiers in Psychology

• Publication date

  2014-01-06

• Fields of study

  Mathematics, Computer Science, Medicine

• Identifiers
  DOI 10.3389/fpsyg.2014.00269PMID 24782791PMCID 3986544
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

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