Detection of multiple undocumented change-points using adaptive Lasso

Jie Shen, Colin M. Gallagher and QiQi Lu

Journal of Applied Statistics, 2014, vol. 41, issue 6, 1161-1173

Abstract: The problem of detecting multiple undocumented change-points in a historical temperature sequence with simple linear trend is formulated by a linear model. We apply adaptive least absolute shrinkage and selection operator (Lasso) to estimate the number and locations of change-points. Model selection criteria are used to choose the Lasso smoothing parameter. As adaptive Lasso may overestimate the number of change-points, we perform post-selection on change-points detected by adaptive Lasso using multivariate t simultaneous confidence intervals. Our method is demonstrated on the annual temperature data (year: 1902-2000) from Tuscaloosa, Alabama.

Date: 2014
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:taf:japsta:v:41:y:2014:i:6:p:1161-1173

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DOI: 10.1080/02664763.2013.862220

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