A comparison of counting process models for complicated life histories

Jochen Mau

Applied Stochastic Models and Data Analysis, 1988, vol. 4, issue 4, 283-298

Abstract: The modern statistical theory for counting processes lends itself to flexible non‐parametric analyses of complicated life histories. Its application rests upon a statistical model of multiplicative intensities, adequately constructed for the objectives of the analysis and usually derived from an underlying stochastic model of an individual course of disease. It is shown how survival times and suitable stochastic partitionings of the time axis can yield statistical models of intermittent and multi‐state exposure to a risk of death which otherwise are deduced, under stronger assumptions, from Markovian illness–death processes. Aalen's linear regression model for counting processes is applied in a non‐standard context of intermittent exposure.

Date: 1988
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1002/asm.3150040407

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:wly:apsmda:v:4:y:1988:i:4:p:283-298

Access Statistics for this article

More articles in Applied Stochastic Models and Data Analysis from John Wiley & Sons
Bibliographic data for series maintained by Wiley Content Delivery ().