SELECTING THE NUMBER OF CHANGE-POINTS IN SEGMENTED LINE REGRESSION
- PMID: 19738935
- PMCID: PMC2737518
SELECTING THE NUMBER OF CHANGE-POINTS IN SEGMENTED LINE REGRESSION
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).
Figures
References
-
- Akaike H. A new look at the statistical model identification. IEEE Trans Automat Control. 1974;19:716–723.
-
- Bai J, Perron P. Estimating and testing linear models with multiple structural changes. Econometrics. 1998;70:9–38.
-
- Bai J, Perron P. Computation and analysis of multiple structural change models. J Appl Econometrics. 2003;18:1–22.
-
- Boos DD, Zhang J. Monte Carlo evaluation of resampling-based hypothesis tests. J Amer Statist Assoc. 2000;95:486–492.
-
- Burnham KP, Anderson DR. Model Selection and Multimodel Inference:A Practical Information-Theoretic Approach. Springer; New York: 2002.
Grants and funding
LinkOut - more resources
Full Text Sources