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Statistics Seminar - John Braun - Iterated Data Sharpening in Local Linear Regression


Title: Iterated Data Sharpening in Local Linear Regression

Speaker: John Braun (University of British Columbia, Okanagan)

Abstract: Data sharpening in kernel regression has been shown to be an effective method of reducing bias while having minimal effects on variance. Earlier efforts to iterate the data sharpening procedure have been less effective, due to the employment of an inappropriate sharpening transformation. In this talk, we propose an iterated data sharpening algorithm which reduces the asymptotic bias at each iteration. The efficacy of the iterative approach is demonstrated via a simulation study. This study also shows that after iteration, the resulting kernel regressions are less sensitive to bandwidth choice, and a further simulation study demonstrates that iterated data sharpening with data-driven bandwidth selection via cross-validation can lead to more accurate regression function estimation. Examples with real data are used to illustrate the scope of change made possible by using iterated data sharpening.

This is joint work with Hanxiao Chen, a student at Brown University

Date/Time: Tuesday November 24, 3:30 - 4:30 

Location: Virtual 

Join Zoom Meeting

Meeting ID: 950 7922 1401
Passcode: 016314
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