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Journal of Data Science


Exact Inference for Transformed Large-Scale Varying Coefficient Models with Applications
Pub. online: 23 April 2025      Type: Statistical Data Science      Open accessOpen Access
Received
16 August 2024
Accepted
20 March 2025
Published
23 April 2025

Abstract

Studying migration patterns driven by extreme environmental events is crucial for building a sustainable society and stable economy. Motivated by a real dataset about human migrations, this paper develops a transformed varying coefficient model for origin and destination (OD) regression to elucidate the complex associations of migration patterns with spatio-temporal dependencies and socioeconomic factors. Existing studies often overlook the dynamic effects of these factors in OD regression. Furthermore, with the increasing ease of collecting OD data, the scale of current OD regression data is typically large, necessitating the development of methods for efficiently fitting large-scale migration data. We address the challenge by proposing a new Bayesian interpretation for the proposed OD models, leveraging sufficient statistics for efficient big data computation. Our method, inspired by migration studies, promises broad applicability across various fields, contributing to refined statistical analysis techniques. Extensive numerical studies are provided, and insights from real data analysis are shared.

Supplementary material

 Supplementary Material
We provide more technical details, simulation results, and real data analysis as the pdf file in the supplemental material. Data files and simulation code used in the article can also be found in the supplemental material.

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Copyright
2025 The Author(s). Published by the School of Statistics and the Center for Applied Statistics, Renmin University of China.
Open access article under the CC BY license.

Keywords
big data computation dynamic dependencies nonparametric regression

Funding
This study was supported by the U.S. Department of the Treasury through the Louisiana Coastal Protection and Restoration Authority’s Center of Excellence Research Grants Program under the Resources and Ecosystems Sustainability, Tourist Opportunities, and Revived Economies of the Gulf Coast States Act of 2012 (RESTORE Act) (Award No. 1 RCEGR260007-01-00). The statements, findings, conclusions, and recommendations are those of the authors and do not necessarily reflect the views of the Department of the Treasury.

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