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Title: Inference on the Change Point under a High Dimensional Covariance Shift
We consider the problem of constructing asymptotically valid confidence intervals for the change point in a high-dimensional covariance shift setting. A novel estimator for the change point parameter is developed, and its asymptotic distribution under high dimen- sional scaling obtained. We establish that the proposed estimator exhibits a sharp Op(ψ−2) rate of convergence, wherein ψ represents the jump size between model parameters before and after the change point. Further, the form of the asymptotic distributions under both a vanishing and a non-vanishing regime of the jump size are characterized. In the former case, it corresponds to the argmax of an asymmetric Brownian motion, while in the latter case to the argmax of an asymmetric random walk. We then obtain the relationship be- tween these distributions, which allows construction of regime (vanishing vs non-vanishing) adaptive confidence intervals. Easy to implement algorithms for the proposed methodology are developed and their performance illustrated on synthetic and real data sets.  more » « less
Award ID(s):
2210358
NSF-PAR ID:
10427908
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
Journal of machine learning research
Volume:
24
Issue:
168
ISSN:
1532-4435
Page Range / eLocation ID:
1-68
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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