Selecting the number of change-points in segmented line regression

Hyune Ju Kim, Binbing Yu, Eric J. Feuer

Research output: Contribution to journalArticle

33 Scopus citations

Abstract

Segmented line regression has been used in many applications, and the problem of estimating the number of change-points in segmented line regression has been discussed in Kim et al. (2000). This paper studies asymptotic properties of the number of change-points selected by the permutation procedure of Kim et al. (2000). This procedure is based on a sequential application of likelihood ratio type tests, and controls the over-fitting probability by its design. In this paper we show that, under some conditions, the number of change-points selected by the permutation procedure is consistent. Via simulations, the permutation procedure is compared with such information-based criterior as the Bayesian Information Criterion (BIC), the Akaike Information Criterion (AIC), and Generalized Cross Validation (GCV).

Original languageEnglish (US)
Pages (from-to)597-609
Number of pages13
JournalStatistica Sinica
Volume19
Issue number2
StatePublished - Apr 1 2009

Keywords

  • Change-points
  • Model selection
  • Permutation test
  • Segmented line regression

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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