The effects of health histories on stochastic process models of aging and mortality

DOI10.1007/BF00180134zbMath0836.92017OpenAlexW2084743950WikidataQ52357236 ScholiaQ52357236MaRDI QIDQ1907236

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

Publication date: 2 May 1996

Published in: Journal of Mathematical Biology (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/bf00180134

zbMATH Keywords

maximum likelihood estimationmortalityagingmultiple jumpsKolmogorov-Fokker-Planck equationstransition ratesmultivariate stochastic processconditional semi-invariantscontinuous diffusiondiscrete jump componentshealth changeshuman health historynon- Gaussian diffusiontime varying continuous state distributions

Mathematics Subject Classification ID

Markov processes: estimation; hidden Markov models (62M05) Population dynamics (general) (92D25) Mathematical geography and demography (91D20) Applications of Brownian motions and diffusion theory (population genetics, absorption problems, etc.) (60J70) Genetics and population dynamics (92D99)

Related Items (3)

Joint Analysis of Health Histories, Physiological State, and Survival ⋮ Mortality modeling: A review ⋮ Markov Aging Process and Phase-Type Law of Mortality

Cites Work

• Mortality and aging in a heterogeneous population: A stochastic process model with observed and unobserved variables
• Dependent competing risks: a stochastic process model
• Conditional Gaussian estimation of dynamic system response on the basis of jerky observations
• A random-walk model of human mortality and aging
• Modifications of the EM algorithm for survival influenced by an unobserved stochastic process
• Relative semi-invariants in the filtration of processes with step components
• On the Filtering of Semimartingales in Case of Observations of Point Processes
• Evaluating the effects of observed and unobserved diffusion processes in survival analysis of longitudinal data
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