Alfred James Hipkins, Kniestück, Profil, beim Musizieren

Signatur des Originals: S 36/F11580

James Nares, Brustbild

Signatur des Originals: S 36/G00108

Costume de Melle. Louise Fitz-James, Acte II.

Signatur des Originals: S 36/G10835

James Hook, Brustbild

Signatur des Originals: S 36/G01204

James Kwast, Brustbild

Signatur des Originals: S 36/G01336

James Kwast, Brustbild

Signatur des Originals: S 36/G01337

James Smith, Ganzfigurbildnis

Signatur des Originals: S 36/G02262

View of the Orchesta Organ in Westminster Abbey under the Direction of Mr. James Wyatt, for the Commemoration of Handel

Signatur des Originals: S 36/G02677

James Bartleman, Brustbild, Profil

Signatur des Originals: S 36/G02742

James Cook, Brustbild, Profil - James King, Brustbild

Signatur des Originals: S 36/G02908

An Inside View of the Pantheon exhibiting their Majesties Box & c. as fitted up under the direction of Mr. James Wyatt, for the Commemoration of Handel.

Signatur des Originals: S 36/G03858

James Bartleman, Brustbild

Signatur des Originals: S 36/G03906

Fanny Cerrito als Sylphide, Gustave Carey als James Reuben, Rollenbild, Doppelbildnis, in: Jean-Madeleine Schweitzhoeffer: La Sylphide

Signatur des Originals: S 36/G04001

Travels and researches among the lakes and mountains of Eastern & Central Africa : from the journals of the late James Frederick Elton

from the journals of the late J. Frederick Elton, F.R.G.S., H.B.M. Consul at Mozambique ; edited and compiled by H.B. Cotterill

A Bayesian analysis for spatial processes with application to mapping

In geographical epidemiology, maps of disease rates and disease risk provide a spatial perspective for researching disease etiology. For rare diseases or when the population base is small, the rate and risk estimates may be unstable. Empirical Bayesian (EB) methods have been used to spatially smooth the estimates by permitting an area estimate to "borrow strength" from its neighbors. Such EB methods include the use of a Gamma model, of a James-Stein estimator, and of a conditional autoregressive (CAR) process. A fully Bayesian analysis of the CAR process is proposed. One advantage of this fully Bayesian analysis is that it can be implemented simply by using repeated sampling from the posterior densities. Use of a Markov chain Monte Carlo technique such as Gibbs sampler was not necessary. Direct resampling from the posterior densities provides exact small sample inferences instead of the approximate asymptotic analyses of maximum likelihood methods (Clayton & Kaldor, 1987). Further, the proposed CAR model provides for covariates to be included in the model. A simulation demonstrates the effect of sample size on the fully Bayesian analysis of the CAR process. The methods are applied to lip cancer data from Scotland, and the results are compared. ^