Penalized B-spline estimator for regression functions using total variation penalty

Jae Hwan Jhong, Ja Yong Koo, Seong Whan Lee

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

We carry out a study on a penalized regression spline estimator with total variation penalty. In order to provide a spatially adaptive method, we consider total variation penalty for the estimating regression function. This paper adopts B-splines for both numerical implementation and asymptotic analysis because they have small supports, so the information matrices are sparse and banded. Once we express the estimator with a linear combination of B-splines, the coefficients are estimated by minimizing a penalized residual sum of squares. A new coordinate descent algorithm is introduced to handle total variation penalty determined by the B-spline coefficients. For large-sample inference, a nonasymptotic oracle inequality for penalized B-spline estimators is obtained. The oracle inequality is then used to show that the estimator is an optimal adaptive for the estimation of the regression function up to a logarithm factor.

Original languageEnglish
Pages (from-to)77-93
Number of pages17
JournalJournal of Statistical Planning and Inference
Volume184
DOIs
Publication statusPublished - 2017 May 1

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Keywords

  • Adaptive estimation
  • Coordinate descent algorithm
  • LASSO
  • Oracle inequalities
  • Penalized least squares

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty
  • Applied Mathematics

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