Detection of multiple undocumented change-points using adaptive Lasso |
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Authors: | Jie Shen Colin M. Gallagher QiQi Lu |
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Affiliation: | 1. Department of Mathematical Sciences, Clemson University, Clemson, SC 29634, USA;2. Department of Statistical Sciences and Operations Research, Virginia Commonwealth University, Richmond, VA 23284, USA |
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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. |
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Keywords: | multiple undocumented change-points adaptive Lasso model selection criterion multivariate t simultaneous confidence intervals successive GLRT |
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