Three Contributions to Latent Variable Modeling
By: Xiang Liu
Publisher: [publisher not identified]
Published: 2019
Place of publication: [New York, N.Y.?]
Language: eng
Description: The dissertation includes three papers that address some theoretical and technical issues of latent variable models. The first paper extends the uniformly most powerful test approach for testing person parameter in IRT to the two-parameter logistic models. In addition, an efficient branch-and-bound algorithm for computing the exact p-value is proposed. The second paper proposes a reparameterization of the log-linear CDM model. A Gibbs sampler is developed for posterior computation. The third paper proposes an ordered latent class model with infinite classes using a stochastic process prior. Furthermore, a nonparametric IRT application is also discussed.
Notes: Department: Measurement and Evaluation.

Thesis advisor: Lawrence DeCarlo.

Thesis (Ph.D.)--Columbia University, 2019.