Age-Period-Cohort | Volker J Schmid

Bayesian Age-Period-Cohort-Modelling and Prediction

Mon, 22 Oct 2018 09:00:00 +0000

Future Declines of Coronary Heart Disease Mortality in England and Wales Could Counter the Burden of Population Ageing

Wed, 01 Jan 2014 00:00:00 +0000

Chronic obstructive pulmonary disease: current burden and future projections

Sun, 01 Jan 2006 00:00:00 +0000

Bayesianische Raum-Zeit-Modellierung in der Epidemiologie

Mon, 30 Aug 2004 00:00:00 +0000

This thesis is concerned with the analysis of spatial and temporal structures of epidemiological data using modern Bayes techniques. Mainly autoregressive distributions as Gaussian Markov random fields or random walks are used as smoothing priors.

Such extensive models can be estimated using MCMC methods only. Some effective algorithms are introduced to get estimates in acceptable time. Especially for space time interactions such algorithms are essential.

As example spatial Bayesian models are applied for wildlife disease incidence data. Discrete and continous frameworks for spatial analysis are compared on a data set on infant mortality cases. Age-period-cohort models are discussed in detail and an extension for spatial data is presented. Finally a stochastic model for space time data on infectious diseases is described.

Bayesian Extrapolation of Space–Time Trends in Cancer Registry Data

Thu, 01 Jan 2004 00:00:00 +0000

COPD mortality trends 1950-1999 in England & Wales - Did the 1956 Clean Air Act make a detectable difference?

Tue, 01 Jan 2002 00:00:00 +0000