Monthly Archives: October 2009

MCA – a thing of the past?

All pair-wise Multiple Comparisons(MCA) is a well known collection of procedures for the stochastic ordering of means; which is a common research task. Classical methods rely on the assumption that the null hypothesis is true. Modern alternatives can be found in the Bayesian Statistics paradigm which abandons the Type 1 error notion. In particular, for problems that can be cast in the hierarchical modeling framework, a principled Bayesian approach relies on partial pooling and shrinkage. Technical arguments supporting this approach have been around for some time. An excellent working paper by Andrew Gleman on the topic presents an overview, simulation results and examples demonstrating the benefits in an applied setting. Suggestions on the use of R and other software is mentioned for implementation.

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An improved Spatial Scan Statistic

Spatial scan statistics have been an important class of tools for cluster detection in spatial data. These are often used in support of surveillance and detection activities in public health and other fields. A common limitation of popular spatial scan statistics is the lack of accommodation in the uncertainty of the measure of interest. In a recent JASA Sept. 2009 article, Weighted Normal Spatial Scan Statistic for Heterogeneous Population Data, the authors offer a solution that addresses this problem in more generality. Weights related to local variance measures or proxies such as sample size can be created for use in a weighted likelihood approach. Extensions to non gaussian probability models are addressed. Some case studies and power simulations provided suggest excellent performance. Their solution has been implemented in the freely available software Satscan.

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mixAK: New data clustering options

Cluster Analysis(and other tools) are often deployed to investigate structure(clustering) in multidimensional data sets. One approach to model such data is the Gaussian mixture model. mixAK is a new R package for Bayesian estimation of multivariate normal mixtures allowing for selection of the number of mixture components, density estimation and optionally allows for interval-censored multivariate data. Author Arnost Komarek’s journal article Computational Statistics and Data Analysis, Volume 53, Issue 12, October 2009, presents the underlying theory and application of the new approach using RJ-MCMC estimation. The selection of the number of mixture components is aided by Deviance Information Criterion(DIC) and Penalized Expected Deviance(PED) measures.

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