Preprint
Asynchronous Distributed ECME Algorithm for Matrix Variate Non-Gaussian Responses
ArXiv.org
Cornell University
10/23/2025
DOI: 10.48550/arxiv.2510.20147
Abstract
We propose a regression model with matrix-variate skew-t response (REGMVST) for analyzing irregular longitudinal data with skewness, symmetry, or heavy tails. REGMVST models matrix-variate responses and predictors, with rows indexing longitudinal measurements per subject. It uses the matrix-variate skew-t (MVST) distribution to handle skewness and heavy tails, a damped exponential correlation (DEC) structure for row-wise dependencies across irregular time profiles, and leaves the column covariance unstructured. For estimation, we initially develop an ECME algorithm for parameter estimation and further mitigate its computational bottleneck via an asynchronous and distributed ECME (ADECME) extension. ADECME accelerates the E-step through parallelization, and retains the simplicity of the conditional M-step, enabling scalable inference. Simulations using synthetic data and a case study exploring matrix-variate periodontal disease endpoints derived from electronic health records demonstrate ADECME's superiority in efficiency and convergence, over the alternatives. We also provide theoretical support for our empirical observations and identify regularity assumptions for ADECME's optimal performance. An accompanying R package is available at https://github.com/rh8liuqy/STMATREG.
Details
- Title: Subtitle
- Asynchronous Distributed ECME Algorithm for Matrix Variate Non-Gaussian Responses
- Creators
- Qingyang LiuSanvesh SrivastavaDipankar Bandyopadhyay
- Resource Type
- Preprint
- Publication Details
- ArXiv.org
- DOI
- 10.48550/arxiv.2510.20147
- ISSN
- 2331-8422
- Publisher
- Cornell University; Ithaca, New York
- Number of pages
- 41 pages
- Language
- English
- Date posted
- 10/23/2025
- Academic Unit
- Statistics and Actuarial Science
- Record Identifier
- 9985019030002771
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