By Peter Congdon
Utilizing Bayesian tips on how to examine info has turn into universal in utilized records, social sciences, and medication, in addition to different disciplines requiring shut paintings with a various set of information. during this undergraduate textual content, Congdon (Queen Mary university, U. of London) takes a pragmatic and available technique, targeting statistical computing and utilized information as he covers the rules of Bayesian inference, version comparability and selection, regression for metric results, types for binary and count number results, random influence and latent variable versions for multi-category results, ordinal regression, discrete spatial info, time sequence types for discrete variables, hierarchical and panel information versions and missing-data types.
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Additional resources for Bayesian Models for Categorical Data (Wiley Series in Probability and Statistics)
Kadane, J. and O’Hagan, A. (2004) Elicitation. Journal of the American Statistical Association, to appear. Gelfand, A. (1996) Model determination using sampling-based methods. , Richardson, S. and Spiegelhalter, D. (eds). Chapman and Hall: London, 145–162. Gelfand, A. and Sahu, S. (1999) Identiﬁability, improper priors, and Gibbs sampling for generalized linear models. Journal of the American Statistical Association, 94, 247–253. Gelfand, A. and Smith, A. (1990) Sampling-based approaches to calculating marginal densities.
Non-parametric regression modelling from a Bayesian perspective, the modelling of overdispersion and the accommodation of outliers or population subgroups. Chapter 6 considers multinomial data generalizations of common links such as the multinomial logit and probit models, while Chapter 7 concerns ordinal data models. Chapter 8 considers a particular form of discrete data analysis, namely spatial data modelling, where issues of both robustness and parameter identiﬁcation ﬁgure large. Chapters 9 and 10 consider respectively time series analysis for categorical data and clustered data (multilevel and panel models).
Znm Þ from the same model assumed to produce Y. , 1995). predictions Z are obtained by À In practice, Á ; for example, zi $ Nðim ; m Þ in a repeated sampling from P ZmðtÞ jðtÞ m linear regression with im ¼ im , or zim $ Poiðim Þ in a log-link Poisson regression with log(im Þ ¼ im . Bayesian model selection or diagnostics may be based on predictions based on comparing the ‘new data’ Z with the observations. 10) can be obtained from the posterior P means and variðtÞ ðtÞ ances of sampled zim or from the posterior average of ni¼1 ðzim À yi Þ2 .