Data-driven choice of a model selection method in joinpoint regression
Hyune-Ju Kim,
Huann-Sheng Chen,
Douglas Midthune,
Bill Wheeler,
Dennis W. Buckman,
Donald Green,
Jeffrey Byrne,
Jun Luo and
Eric J. Feuer
Journal of Applied Statistics, 2023, vol. 50, issue 9, 1992-2013
Abstract:
Selecting the number of change points in segmented line regression is an important problem in trend analysis, and there have been various approaches proposed in the literature. We first study the empirical properties of several model selection procedures and propose a new method based on two Schwarz type criteria, a classical Bayes Information Criterion (BIC) and the one with a harsher penalty than BIC ( $ \hbox {BIC}_3 $ BIC3). The proposed rule is designed to use the former when effect sizes are small and the latter when the effect sizes are large and employs the partial $ R^2 $ R2 to determine the weight between BIC and $ \hbox {BIC}_3 $ BIC3. The proposed method is computationally much more efficient than the permutation test procedure that has been the default method of Joinpoint software developed for cancer trend analysis, and its satisfactory performance is observed in our simulation study. Simulations indicate that the proposed method performs well in keeping the probability of correct selection at least as large as that of $ \hbox {BIC}_3 $ BIC3, whose performance is comparable to that of the permutation test procedure, and improves $ \hbox {BIC}_3 $ BIC3 when it performs worse than $ \hbox {BIC}. $ BIC. The proposed method is applied to the U.S. prostate cancer incidence and mortality rates.
Date: 2023
References: Add references at CitEc
Citations:
Downloads: (external link)
http://hdl.handle.net/10.1080/02664763.2022.2063265 (text/html)
Access to full text is restricted to subscribers.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:taf:japsta:v:50:y:2023:i:9:p:1992-2013
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/CJAS20
DOI: 10.1080/02664763.2022.2063265
Access Statistics for this article
Journal of Applied Statistics is currently edited by Robert Aykroyd
More articles in Journal of Applied Statistics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().