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Hierarchical Bayesian Analysis (hierarchical + bayesian_analysis)

Distribution by Scientific Domains
Medical Sciences40%
Mathematics and Statistics40%

Selected Abstracts

Hierarchical Bayesian Analysis of Correlated Zero-inflated Count Data

BIOMETRICAL JOURNAL, Issue 6 2004
Getachew A. Dagne
Abstract This article presents two-component hierarchical Bayesian models which incorporate both overdispersion and excess zeros. The components may be resultants of some intervention (treatment) that changes the rare event generating process. The models are also expanded to take into account any heterogeneity that may exist in the data. Details of the model fitting, checking and selecting alternative models from a Bayesian perspective are also presented. The proposed methods are applied to count data on the assessment of an efficacy of pesticides in controlling the reproduction of whitefly. (© 2004 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim) [source]

Geographic Variation of Pediatric Burn Injuries in a Metropolitan Area

ACADEMIC EMERGENCY MEDICINE, Issue 7 2003
Kristine G. Williams MD
Objectives: To use a geographic information system (GIS) and spatial statistics to describe the geographic variation of burn injuries in children 0,14 years of age in a major metropolitan area. Methods: The authors reviewed patient records for burn injuries treated during 1995 at the two children's hospitals in St. Louis. Patient addresses were matched to block groups using a GIS, and block group burn injury rates were calculated. Mapping software and Bayesian analysis were used to create maps of burn injury rates and risks in the city of St. Louis. Results: Three hundred eleven children from the city of St. Louis were treated for burn injuries in 1995. The authors identified an area of high incidence for burn injuries in North St. Louis. The filtered rate contour was 6 per 1,000 children at risk, with block group rates within the area of 0 to 58.8 per 1,000 children at risk. Hierarchical Bayesian analysis of North St. Louis burn data revealed a relative risk range of 0.8771 to 1.182 for census tracts within North St. Louis, suggesting that there may be pockets of high risk within an already identified high-risk area. Conclusions: This study shows the utility of geographic mapping in providing information about injury patterns within a defined area. The combination of mapping injury rates and spatial statistical analysis provides a detailed level of injury surveillance, allowing for identification of small geographic areas with elevated rates of specific injuries. [source]

Evaluation of quality of life, and priorities in people with glaucoma

ACTA OPHTHALMOLOGICA, Issue 2009
P ASPINALL
Purpose Quality of life appears to be of increasing importance as a criterion for clinical intervention. However its meaning can be complex and its assessment varied. In social science the term has broad definitions which include terms such as autonomy, wellbeing; self esteem; sense of control etc. On the other hand within ophthalmology a narrower operational definition is mainly used which is the degree to which someone's vision impacts on a range of necessary and desirable daily tasks a person wishes to carry out. The purpose of the presentation is to compare alternative methods of quality of life assessment. Methods The assessment approaches taken in the study range from conventional questionnaire rating scales, (something NICE has questioned) and time trade off comparisons, to more recent methods of scaling generated by for example Rasch or Hierarchical Bayesian analysis. Results Data will be presented from two studies (one in Edinburgh and one in Aberdeen) on quality of life in people with glaucoma. One of the new recommended discrete choice methods (Choice based conjoint analysis with Hierarchical Bayesian estimates) will be used. The results will include quality of life outcomes and their stability; related visual factors; comparisons across methods and more general implications for quality of life assessment. Conclusion Different methods for the assessment of quality of life produce different results with relatively low correlations between them although conjoint analysis has revealed stable priorities across two independent studies. These discrepancies in quality of life assessment require further study and evaluation. [source]

A Survey of Model Evaluation Approaches With a Tutorial on Hierarchical Bayesian Methods

COGNITIVE SCIENCE - A MULTIDISCIPLINARY JOURNAL, Issue 8 2008
Richard M. Shiffrin
Abstract This article reviews current methods for evaluating models in the cognitive sciences, including theoretically based approaches, such as Bayes factors and minimum description length measures; simulation approaches, including model mimicry evaluations; and practical approaches, such as validation and generalization measures. This article argues that, although often useful in specific settings, most of these approaches are limited in their ability to give a general assessment of models. This article argues that hierarchical methods, generally, and hierarchical Bayesian methods, specifically, can provide a more thorough evaluation of models in the cognitive sciences. This article presents two worked examples of hierarchical Bayesian analyses to demonstrate how the approach addresses key questions of descriptive adequacy, parameter interference, prediction, and generalization in principled and coherent ways. [source]

The speed of adjustment of financial ratios: A hierarchical Bayesian approach using mixtures

APPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY, Issue 2 2008
Pilar Gargallo
Abstract This paper presents a hierarchical Bayesian analysis of the partial adjustment model of financial ratios using mixture models, an approach that allows us to estimate the distribution of the adjustment coefficients. More particularly, it enables us to analyse speed of reaction in the presence of shocks affecting financial ratios objectives as a basis to establish homogenous groups of firms. The proposed methodology is illustrated by examining a set of ratios for a sample of firms operating in the U.S. manufacturing sector. Copyright © 2007 John Wiley & Sons, Ltd. [source]