Department of Statistics pres.
Statistics Department Seminar Series: Yuqi Gu, Ph.D. Candidate, Department of Statistics, University of Michigan
"Uncover Hidden Fine-Grained Scientific Information: Structured Latent Attribute Models"
In the first part of this talk, I present identifiability results that advance the theoretical knowledge of how the design matrix influences the estimability of SLAMs. The new identifiability conditions guide real-world practices of designing cognitive diagnostic tests and also lay the foundation for drawing valid statistical conclusions. In the second part, I introduce a statistically consistent penalized likelihood approach to selecting significant latent patterns in the population. I also propose a scalable computational method. These developments explore an exponentially large model space involving many discrete latent variables, and they address the estimation and computation challenges of high-dimensional SLAMs arising in large-scale scientific measurements. The application of the proposed methodology to the data from an international educational assessment reveals meaningful knowledge structures and latent subgroups of the student populations.
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