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Statistical Models Based on Counting Processes

Bry, Xavier, Andersen, Per Kragh, Borgan, Ørnulf, Gill, Richard D., Keiding, Niels, Borgan, Ornulf·1997·Population (French Edition)

Abstract

Modern survival analysis and more general event history analysis may be effectively handled in the mathematical framework of counting processes, stochastic integration, martingale central limit theory and product integration. This book presents this theory, which has been the subject of an intense research activity during the past one-and-a-half decades. The exposition of the theory is integrated with the careful presentation of many practical examples, based almost exlusively on the authors' experience, with detailed numerical and graphical illustrations. Statistical Models Based on Counting Processes may be viewed as a research monograph for mathematical statisticians and biostatisticians, although almost all methods are given in sufficient detail to be used in practice by other mathematically oriented researchers studying event histories (demographers, econometricians, epidemiologists, actuariala mathematicians, reliability engineers, biologists). Much of the material has so far only been available in the journal literature (if at all), and a wide variety of researchers will find this an invlauable survey of the subject.

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Metadata

Authors
Bry, X., Andersen, P. K., Borgan, Ø., Gill, R. D., Keiding, N., & Borgan, O.
Venue
Population (French Edition)
Publisher
JSTOR
Published
1997-07
Volume
52
Issue
4
Pages
1046
ISSN
0032-4663 (ISSN-L)
OpenAlex
W2319384619
Cited by
2,884
References
0
FWCI
166.29
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Statistical Models Based on Counting Processes. https://app.cochat.ai/papers/10.2307/1534625
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Bry, X., Andersen, P. K., Borgan, Ø., Gill, R. D., Keiding, N., & Borgan, O. (1997). Statistical Models Based on Counting Processes. Population (French Edition). https://doi.org/10.2307/1534625

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.