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Authors' rejoinder to discussion We thank the discussants for their contributions and insights, and for raising numerous interesting points. We shall respond to these as best we can, though obviously there are many questions for which, as yet, only partial solutions exist. We shall also try to rectify some misunderstandings that have arisen as a result of(More)
This paper combines existing models for longitudinal and spatial data in a hierarchical Bayesian framework, with particular emphasis on the role of time- and space-varying covariate effects. Data analysis is implemented via Markov chain Monte Carlo methods. The methodology is illustrated by a tentative re-analysis of Ohio lung cancer data 1968-1988. Two(More)
We describe a probabilistic approach to simultaneous image segmentation and intensity estimation for complementary DNA microarray experiments. The approach overcomes several limitations of existing methods. In particular, it (a) uses a flexible Markov random field approach to segmentation that allows for a wider range of spot shapes than existing methods,(More)
I describe a simple procedure for investigating the convergence properties of Markov Chain Monte Carlo sampling schemes. The procedure employs multiple runs from a sampler, using the same random deviates for each run. When the sample paths from all sequences converge, it is argued that approximate equilibrium conditions hold. The procedure also provides a(More)
This paper describes a method for estimating the risk from a disease over a set of contiguous geographical regions, when data on a potentially important covariate, such as race, are not available. Conditions under which the extra margin can be recovered are suggested. An application to prostate cancer mortality among the non-white population in the counties(More)