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SUMMARY: Multivariate versions of variable bandwidth kernel density estimators can be used to combat the eeects of the curse of dimensionality. They are also more exible than the xed bandwidth estimator to model complex (multimodal) densities. In this work, two variable bandwidth estimators are discussed: the balloon estimator which varies the smoothing(More)
Modern data analysis requires a number of tools to undercover hidden structure. For initial exploration of data, animated scatter diagrams and nonparametric density estimation in many forms and varieties are the techniques of choice. This article focuses on the application of histograms and nonparametric kernel methods to explore data. The details of(More)
In this paper, theoretical and practical aspects of the sample-point adaptive positive kernel density estimator are examined. A closed-form expression for the mean integrated squared error is obtained through the device of preprocessing the data by binning. With this expression, the exact behavior of the optimally adaptive smoothing parameter function is(More)
OBJECTIVES We examined patterns of pedestrian-motor vehicle collisions and associated environmental characteristics in Denver, Colorado. METHODS We integrated publicly available data on motor vehicle collisions, liquor licenses, land use, and sociodemographic characteristics to analyze spatial patterns and other characteristics of collisions involving(More)
[1] We present probabilistic projections for spatial patterns of future temperature change using a multivariate Bayesian analysis. The methodology is applied to the output from 21 global coupled climate models used for the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. The statistical technique is based on the assumption that(More)