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Modelling lifetime data: a graphical approach
Author(s) -
LouzadaNeto Francisco
Publication year - 1999
Publication title -
applied stochastic models in business and industry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.413
H-Index - 40
eISSN - 1526-4025
pISSN - 1524-1904
DOI - 10.1002/(sici)1526-4025(199904/06)15:2<123::aid-asmb371>3.0.co;2-t
Subject(s) - heteroscedasticity , simple (philosophy) , computer science , flexibility (engineering) , focus (optics) , econometrics , covariate , hazard , function (biology) , point (geometry) , data mining , statistics , mathematics , machine learning , philosophy , physics , chemistry , geometry , organic chemistry , epistemology , evolutionary biology , optics , biology
In this paper we point out the differences between the most common hazard‐based models, such as the proportional hazards and the accelerated failure time models. We focus on the heteroscestaticity‐across‐individuals problem that cannot be accommodated by them, and give motivation and general ideas about more flexible formulations. We describe hybrid and extended models, which have the former models as particular cases, but keep enough flexibility to fit data with heteroscedasticity. We show that by considering simple graphical procedures it is easy to verify whether there is heteroscedasticity in the data, whether it is possible to describe it through a simple function of the covariates, and whether it is important to take it in account for the final fit. Real datasets are considered. Copyright © 1999 John Wiley & Sons, Ltd.