Evaluating the effects of observed and unobserved diffusion processes in survival analysis of longitudinal data

DOI10.1016/0270-0255(86)90085-0zbMath0615.62136OpenAlexW2078315943MaRDI QIDQ4723118

Eric Stallard, Anatoliy I. Yashin, Kenneth G. Manton

Publication date: 1986

Published in: Mathematical Modelling (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/0270-0255(86)90085-0

zbMATH Keywords

longitudinal datasurvival analysismortality ratesmaximum-likelihood methodCameron-Martin approachContinuous and discrete time observation plansmultivariate Gaussian diffusion processtwo component processunderlying physiological process

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Medical applications (general) (92C50) Population dynamics (general) (92D25) Inference from stochastic processes (62M99)

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The effects of health histories on stochastic process models of aging and mortality ⋮ Stochastic model for analysis of longitudinal data on aging and mortality ⋮ Effects of unobserved and partially observed covariate processes on system failure: A review of models and estimation strategies

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