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Parametric and Parametrically Smoothed Distribution‐Free Proportional Hazard Models with Discrete Data
Author(s) -
Sturm Roland
Publication year - 1991
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/bimj.4710330412
Subject(s) - nonparametric statistics , hazard , parametric statistics , mathematics , statistics , parametric model , term (time) , proportional hazards model , flexibility (engineering) , discrete time and continuous time , econometrics , computer science , chemistry , physics , organic chemistry , quantum mechanics
This paper discusses discrete time proportional hazard models and suggests a new class of flexible hazard functions. Explicitly modeling the discreteness of data is important since standard continuous models are biased; allowing for flexibility in the hazard estimation is desirable since strong parametric restrictions are likely to be similarly misleading. Simulation compare continuous and discrete models when data are generated by grouping and demonstrate that simple approximations recover underlying hazards well and outperform nonparametric maximum likelihood estimates in term of mean squared error.