Heteroscedastic Change Point Analysis and Application to Footprint Data
| Autor: | Stephen J. Ganocy, Jia-Yang Sun |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
0301 basic medicine
Estimation Heteroscedasticity Computer science Model selection Bayesian probability computer.software_genre Footprint 03 medical and health sciences 030104 developmental biology 0302 clinical medicine Change-Point Analysis 030220 oncology & carcinogenesis Statistics Change points Data mining Segmented regression computer |
| Zdroj: | Journal of Data Science. 13:157-186 |
| ISSN: | 1683-8602 1680-743X |
| DOI: | 10.6339/jds.201501_13(1).0009 |
| Popis: | Analysis of footprint data is important in the tire industry. Estimation procedures for multiple change points and unknown parameters in a segmented regression model with unknown heteroscedastic variances are developed for analyzing such data. Our approaches include both likelihood and Bayesian, with and without continuity constraints at the change points. A model selection procedure is also proposed to choose among competing models for fitting a middle segment of the data between change points. We study the performance of the two approaches and apply them to actual tire data examples. Our Maximization－Maximization－Posterior (MMP) algorithm and the likelihood－based estimation are found to be complimentary to each other. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|