University of Illinois Urbana-Champaign

Domain latent class models, equivalent set latent class models, and kernel factor analysis

Bowers, Jesse Mark

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https://hdl.handle.net/2142/127472

Description

Title
Domain latent class models, equivalent set latent class models, and kernel factor analysis
Author(s)
Bowers, Jesse Mark
Issue Date
2024-11-26
Director of Research (if dissertation) or Advisor (if thesis)
Culpepper, Steve
Doctoral Committee Chair(s)
Culpepper, Steve
Committee Member(s)
  • Douglas, Jeffrey
  • Zhu, Ruoqing
  • Park, Trevor
Department of Study
Statistics
Discipline
Statistics
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
  • Latent Class Modeling
  • Clustering
  • Factor Analysis
  • Categorical Data Analysis
  • Dimension Reduction
Abstract
We discuss main contributions to Latent Class Modelings and Factor Analysis. 1) Domain Latent Class Models (DLCMs) extend latent class models to allow for related questions. 2) Equivalence Set Restricted Latent Class Models (ESRLCMs) allow for a much greater variety of restrictions compared with restricted latent trait models. 3) Kernel Exploratory Factor Analysis (KEFA) conduct factor analysis after controlling for covariates.
Graduation Semester
2024-12
Type of Resource
Thesis
Handle URL
https://hdl.handle.net/2142/127472
Copyright and License Information
Copyright 2024 Jesse Bowers. Chapter 2 is a copy of a paper published in Bayesian Analysis (http://dx.doi.org/10.1214/24-BA1433) which is available by a creative commons license (https://creativecommons.org/licenses/by/4.0/) and reproduced here with only changes to formatting.

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Graduate Dissertations and Theses at Illinois PRIMARY
Graduate Theses and Dissertations at Illinois