STATISTICS COLLOQUIUM
Gongjun Xu
Department of Statistics
University of Michigan
Identifying Latent Structures in Restricted Latent Class Models
ABSTRACT
This talk focuses on a family of restricted latent structure models with wide applications in psychological and educational assessments, where the model parameters are restricted via a latent structure matrix to reflect pre-specified assumptions on the latent attributes. Such a latent structure matrix is often provided by experts and assumed to be correct upon construction, yet it may be subjective and misspecified. Recognizing this problem, researchers have been developing methods to estimate the structure matrix from data. However, the fundamental issue of the identifiability of the structure matrix has not been addressed until now. In this work, we first introduce identifiability conditions that ensure the estimability of the structure matrix. The results provide theoretical justification for the existing estimation methods as well as a guideline for the related experimental designs. With the theoretical development, we further propose an information-based model selection method to estimate the latent structure. Simulation studies and data analysis are also presented to examine the performance of the proposed method.
DATE: Wednesday, March 22, 2017
TIME: 4:00 pm
PLACE: Philip E. Austin Bldg., room 105
Coffee will be served at 3:30 in the Noether Lounge (AUST 326)
For more information, contact: Tracy Burke at tracy.burke@uconn.edu