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Bayesian Nonparametric Spatially Smoothed Density Estimation

  • Timothy Hanson
  • Haiming Zhou
  • Vanda Inácio de Carvalho
Chapter
Part of the ICSA Book Series in Statistics book series (ICSABSS)

Abstract

A Bayesian nonparametric density estimator that changes smoothly in space is developed. The estimator is built using the predictive rule from a marginalized Polya tree, modified so that observations are spatially weighted by their distance from the location of interest. A simple refinement is proposed to accommodate arbitrarily censored data and a test for whether the density is spatially varying is also developed. The method is illustrated on two real datasets, and an R function SpatDensReg is provided for general use.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Timothy Hanson
    • 1
  • Haiming Zhou
    • 2
  • Vanda Inácio de Carvalho
    • 3
  1. 1.Medtronic Inc.MinneapolisUSA
  2. 2.Northern Illinois UniversityDeKalbUSA
  3. 3.University of EdinburghEdinburghUK

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