Domain selection for the varying coefficient model via local polynomial regression.

TitleDomain selection for the varying coefficient model via local polynomial regression.
Publication TypeJournal Article
Year of Publication2015
AuthorsKong, Dehan, Howard Bondell, and Yichao Wu
JournalComput Stat Data Anal
Volume83
Pagination236-250
Date Published2015 Mar 01
ISSN0167-9473
Abstract

In this article, we consider the varying coefficient model, which allows the relationship between the predictors and response to vary across the domain of interest, such as time. In applications, it is possible that certain predictors only affect the response in particular regions and not everywhere. This corresponds to identifying the domain where the varying coefficient is nonzero. Towards this goal, local polynomial smoothing and penalized regression are incorporated into one framework. Asymptotic properties of our penalized estimators are provided. Specifically, the estimators enjoy the oracle properties in the sense that they have the same bias and asymptotic variance as the local polynomial estimators as if the sparsity is known as a . The choice of appropriate bandwidth and computational algorithms are discussed. The proposed method is examined via simulations and a real data example.

DOI10.1016/j.csda.2014.10.004
Alternate JournalComput Stat Data Anal
Original PublicationDomain selection for the varying coefficient model via local polynomial regression.
PubMed ID25506112
PubMed Central IDPMC4260425
Grant ListP01 CA142538 / CA / NCI NIH HHS / United States
R01 CA149569 / CA / NCI NIH HHS / United States
Project: