Bayesian Expectation-Maximization-Maximization: a latent-mixture-modeling-based Bayesian algorithm for the three-parameter logistic model

The current study proposes a Bayesian Expectation-Maximization-Maximization (Bayesian EMM, or BEMM), which is an alternative feasible Bayesian algorithm for the three-parameter logistic model (3PLM). The Bayesian EMM takes full advantage of both the EMM and the Bayesian approach. The BEMM not only successfully solves the issue of inaccurate estimates for few items in the EMM algorithm, but also alleviates the negative effect caused by different priors in the traditional Bayesian EM. The simulation studies and real data examples indicate that: (1) The Bayesian EMM can produce more accurate and stable item estimates. (2) Standard errors (SE) yielded by the Bayesian EMM tend to be smaller than the traditional Bayesian EM. (3) The Bayesian EMM is insensitive to priors, which means that the negative influence of different priors will be minimized.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Educational Psychology

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2017

Author dc:creator

• Guo, Shaoyang

Contributors dc:contributor

• Zhang, Jinming
• Chang, Hua-Hua
• Anderson, Carolyn J.

• Bayesian Expectation-Maximization-Maximization (BEMM)
• Three-parameter logistic model (3PLM)

Statement dc:rights

• Copyright 2017 Shaoyang Guo

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/97545

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/97545