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Statistics Department Seminar Series: Gongjun Xu, Associate Professor, Department of Statistics, University of Michigan

"Identifiability and Inference for Generalized Latent Factor Models"
Friday, October 25, 2024
10:00-11:00 AM
340 West Hall Map
Abstract: Generalized latent factor analysis not only provides a useful latent embedding approach in statistics and machine learning, but also serves as a widely used tool across various scientific fields, including psychometrics, econometrics, and social sciences. Ensuring the identifiability of latent factors and the loading matrix is essential for the model's estimability and interpretability, and various identifiability conditions have been employed by practitioners. However, fundamental statistical inference issues for latent factors and factor loadings under commonly used identifiability conditions remain largely unaddressed. In this work, we focus on the maximum likelihood estimation for generalized factor models and establish statistical inference properties under popularly used identifiability conditions. The developed theory is further illustrated through numerical simulations and an application to a personality assessment dataset.

https://sites.google.com/umich.edu/gongjunxu
Building: West Hall
Website:
Event Type: Workshop / Seminar
Tags: seminar
Source: Happening @ Michigan from Department of Statistics, Department of Statistics Seminar Series