Bayesian model comparison and model averaging for small-area estimation

This paper considers small-area estimation with lung cancer mortality data, and discusses the choice of upper-level model for the variation over areas. Inference about the random effects for the areas may depend strongly on the choice of this model, but this choice is not a straightforward matter. We give a general methodology for both evaluating the data evidence for different models and averaging over plausible models to give robust area effect distributions. We reanalyze the data of Tsutakawa [Biometrics 41 (1985) 69--79] on lung cancer mortality rates in Missouri cities, and show the differences in conclusions about the city rates from this methodology.Comment: Published in at http://dx.doi.org/10.1214/08-AOAS205 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Crystallines of the Eastern Highlands

New England Intercollegiate Geological Conference: Hartford, Connecticut, October 9-11, 1953: Trip

Case Study: Value Added in the Oats Industry in Alberta and Western Canada 1998

Crop Production/Industries,

An Historical Overview of Crop Acres in Western Canada: A Graphical and Statistical Approach

Crop Production/Industries, Land Economics/Use,

The connections of the inferior colliculus and the organization of the brainstem auditory system in the greater horseshoe bat (Rhinolophus ferrumequinum)

The connections of the inferior colliculus, the mammalian midbrain auditory center, were determined in the greater horseshoe bat (Rhinolophus ferrumequinum), using the horseradish peroxidase method. In order to localize the auditory centers of this bat, brains were investigated with the aid of cell and fiber-stained material. The results show that most auditory centers are highly developed in this echolocating bat. However, the organization of the central auditory system does not generally differ from the mammalian scheme. This holds also for the organization of the superior olivary complex where a well-developed medial superior olivary nucleus was found. In addition to the ventral and dorsal nuclei of the lateral lemniscus a third well-developed nucleus has been defined which projects ipsilaterally to the inferior colliculus and which was called the intermediate nucleus of the lateral leminiscus. All nuclei of the central auditory pathway project ipsi-, contra-, or bilaterally to the central nucleus of the inferior colliculus with the exception of the medial nucleus of the trapezoid body and the medial geniculate body. The tonotopic organization of these projections and their possible functions are discussed in context with neurophysiological investigations

Fisher information under Gaussian quadrature models

This paper develops formulae to compute the Fisher information matrix for the regression parameters of generalized linear models with Gaussian random effects. The Fisher information matrix relies on the estimation of the response variance under the model assumptions. We propose two approaches to estimate the response variance: the first is based on an analytic formula (or a Taylor expansion for cases where we cannot obtain the closed form), and the second is an empirical approximation using the model estimates via the expectation–maximization process. Further, simulations under several response distributions and a real data application involving a factorial experiment are presented and discussed. In terms of standard errors and coverage probabilities for model parameters, the proposed methods turn out to behave more reliably than does the ‘disparity rule’ or direct extraction of results from the generalized linear model fitted in the last expectation–maximization iteration

A new Bayesian approach for determining the number of components in a finite mixture

This article evaluates a new Bayesian approach to determining the number of components in a finite mixture. We evaluate through simulation studies mixtures of normals and latent class mixtures of Bernoulli responses. For normal mixtures we use a “gold standard” set of population models based on a well-known “testbed” data set – the galaxy recession velocity data set of Roeder (1990). For Bernoulli latent class mixtures we consider models for psychiatric diagnosis (Berkhof, van Mechelen and Gelman 2003). The new approach is based on comparing models with different numbers of components through their posterior deviance distributions, based on non-informative or diffuse priors. Simulations show that even large numbers of closely spaced normal components can be identified with sufficiently large samples, while for atent classes with Bernoulli responses identification is more complex, though it again improves with increasing sample size