Bandwidth selection for the estimation of transition probabilities in the location-scale progressive three-state model

Luís Meira-Machado (), Javier Roca-Pardiñas (), Ingrid Van Keilegom () and Carmen Cadarso-Suárez ()

Computational Statistics, 2013, vol. 28, issue 5, 2185-2210

Abstract: Times between consecutive events are often of interest in medical studies. Usually the events represent different states of the disease process and are modeled using multi-state models. This paper introduces and studies a feasible estimation method for the transition probabilities in a progressive three-state model. We assume that the vector of gap times $$(T_1,T_2)$$ satisfies a nonparametric location-scale regression model $$T_2=m(T_1)+\sigma (T_1)\epsilon $$ , where the functions $$m$$ and $$\sigma $$ are ‘smooth’, and $$\epsilon $$ is independent of $$T_1$$ . Under this model, Van Keilegom et al. (J Stat Plan Inference 141:1118–1131, 2011 ) proposed estimators of the transition probabilities. However, the important issue of automatic bandwidth choice in this setting has not been examined, making the analysis of real datasets rather difficult. In this paper, we study the performance of their estimator in practice, we propose some modifications and study practical issues related to the implementation of the estimator, which involves the choice of an appropriate bandwidth. In an extensive simulation study the good performance of the method is shown. Simulations also demonstrate that the proposed estimator compares favorably with alternative estimators. Furthermore, the proposed methodology is illustrated with a real database on breast cancer. Copyright Springer-Verlag Berlin Heidelberg 2013

Keywords: Censoring; Computational statistics; Location-scale model; Nonparametric regression; Progressive three-state model; Transition probabilities (search for similar items in EconPapers)
Date: 2013
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://hdl.handle.net/10.1007/s00180-013-0402-0 (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:spr:compst:v:28:y:2013:i:5:p:2185-2210

Ordering information: This journal article can be ordered from
http://www.springer.com/statistics/journal/180/PS2

DOI: 10.1007/s00180-013-0402-0

Access Statistics for this article

Computational Statistics is currently edited by Wataru Sakamoto, Ricardo Cao and Jürgen Symanzik

More articles in Computational Statistics from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().