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A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

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Clustered spatio-temporal varying coefficient regression model.

Junho Lee¹, Maria E Kamenetsky², Ronald E Gangnon^2,3

¹Statistics Program, CEMSE Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia.

Statistics in Medicine

|October 26, 2020

Summary

This summary is machine-generated.

This study introduces a new varying coefficient regression method to identify spatial-temporal clusters in regression coefficients. This helps understand localized relationships between variables in spatio-temporal data analysis.

Keywords:

regression spatial cluster detection spatial scan statistic spatio-temporal cluster detection spatio-temporal varying coefficient varying coefficient regression

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Area of Science:

Statistics
Geospatial Analysis
Epidemiology

Background:

Regression analysis for spatio-temporal data is complex.
Identifying localized relationships in space and time is crucial for understanding phenomena.
Existing methods may not adequately capture spatial-temporal heterogeneity.

Purpose of the Study:

To propose a varying coefficient regression method for spatio-temporal data.
To develop a method for detecting spatial-temporal clusters in regression coefficients.
To analyze heterogeneity in regression coefficients across space and time.

Main Methods:

Extension of varying coefficient regression models for spatial-only data to spatio-temporal data.
Development of a sequential identification approach for multiple clusters.
Testing for homogeneity of regression coefficients across spatial domains at each time point.

Main Results:

The proposed method can identify potential cylindrical clusters of regression coefficients.
Simulation studies demonstrate the power and accuracy of cluster identification.
The methodology is effective in analyzing complex spatio-temporal patterns.

Conclusions:

The developed varying coefficient regression method effectively models spatio-temporal heterogeneity.
The approach provides a robust tool for identifying localized relationships in spatio-temporal data.
Application to cancer mortality data highlights its practical utility in public health research.