ON USING ESTIMATED COEFFICIENTS OF A LINEAR MODEL FOR CHANGE POINT DETECTION: APPLICATION TO WEATHER DATA
Keywords:
linear model, change detection, ordinary least squares, residual error, Student test, Fisher testDOI:
https://doi.org/10.17654/0973514325007Abstract
This paper addresses the standard linear model, particularly focusing on the case of linear regression. After establishing certain results related to ordinary least squares, the Student’s t-test, and the Fisher test, we introduce change point detection within the linear model framework, using estimated coefficients as the test statistic. Finally, the proposed model is applied to real weather data from the N’Djamena region in Chad to detect changes in precipitation patterns.
Received: July 31, 2024
Revised: September 2, 2024
Accepted: September 13, 2024
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