Bayesian Nonparametric Spatially Smoothed Density Estimation

Timothy Hanson, Haiming Zhou, Vanda Calhau Fernandes Inacio De Carvalho

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)peer-review

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 renement 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.
Original languageEnglish
Title of host publicationICSA Book Series in Statistics
PublisherSpringer International Publishing AG
Number of pages20
Publication statusAccepted/In press - 3 Mar 2018

Publication series

NameICSA Book Series in Statistics
ISSN (Print)2199-0980

Fingerprint

Dive into the research topics of 'Bayesian Nonparametric Spatially Smoothed Density Estimation'. Together they form a unique fingerprint.

Cite this