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Semiparametric change points detection using single index spatial random effects model in environmental epidemiology
Hamdy F F Mahmoud1,2, Inyoung Kim1
1Department of Statistics, Virginia Polytechnic Institute and State University (Virginia Tech), Blacksburg, VA, United States of America.
Plos One
|December 12, 2024
Summary
This study introduces a new semiparametric method to analyze how temperature affects mortality. It identifies key temperature change points and nonlinear relationships, improving environmental health research.
Area of Science:
- Environmental Health
- Biostatistics
- Epidemiology
Background:
- Evaluating the mortality-temperature relationship is crucial for public health.
- Nonparametric models complicate the identification of critical temperature change points.
- Spatially correlated random effects influence these relationships.
Purpose of the Study:
- To propose a unified semiparametric approach for analyzing mortality-temperature relationships.
- To simultaneously identify nonlinear patterns and detect spatially dependent change points.
- To provide a robust method for environmental health studies.
Main Methods:
- Developed a unified semiparametric model for estimation and change point detection.
- Employed a permutation-based test for significance of change points.
- Accounted for unknown numbers of change points and spatial effects.
Main Results:
- The proposed method effectively identifies nonlinear mortality-temperature relationships.
- Spatially dependent change points were detected and assessed for significance.
- Simulation studies confirmed the approach's performance.
Conclusions:
- The unified semiparametric approach offers a powerful tool for environmental health research.
- This method enhances the understanding of temperature's impact on mortality.
- It provides a robust framework for analyzing complex environmental health data.
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