Abstract / Description of output
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.
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 language | English |
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Title of host publication | New Frontiers of Biostatistics and Bioinformatics |
Editors | Y Zhao, DG Chen |
Publisher | Springer |
Pages | 87-105 |
ISBN (Electronic) | 978-3-319-99389-8 |
ISBN (Print) | 978-3-319-99388-1 |
DOIs | |
Publication status | Published - 6 Dec 2018 |
Publication series
Name | ICSA Book Series in Statistics |
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ISSN (Print) | 2199-0980 |