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Activity Number: 178 - Novel Applications and Extensions of Dimension Reduction Methods
Type: Contributed
Date/Time: Monday, July 29, 2019 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #306424
Title: Learning Attribute Patterns in High-Dimensional Structured Latent Attribute Models
Author(s): Yuqi Gu* and Gongjun Xu
Companies: University of Michigan and University of Michigan
Keywords: latent variable models; mixture models; identifiability; model selection; sparse estimation

Structured latent attribute models (SLAMs) are a special family of discrete latent variable models widely used in social and biological sciences. This paper considers the problem of learning significant latent attribute patterns from a SLAM with potentially high-dimensional patterns. We address the theoretical identifiability issue, propose a penalized likelihood method for the selection of the attribute patterns, and further establish the selection consistency in the overfitted SLAM with diverging number of latent mixture components. The good performance of the proposed methodology is illustrated by simulation studies and two real datasets in educational assessment.

Authors who are presenting talks have a * after their name.

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