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  1. Journal of Agricultural, Biological, and Environmental Statistics
  2. Journal of Agricultural, Biological, and Environmental Statistics : Volume 14
  3. Journal of Agricultural, Biological, and Environmental Statistics : Volume 14, Issue 1, March 2009
  4. Bayesian analysis of semiparametric linear-circular models
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Journal of Agricultural, Biological, and Environmental Statistics : Volume 22
Journal of Agricultural, Biological, and Environmental Statistics : Volume 21
Journal of Agricultural, Biological, and Environmental Statistics : Volume 20
Journal of Agricultural, Biological, and Environmental Statistics : Volume 19
Journal of Agricultural, Biological, and Environmental Statistics : Volume 18
Journal of Agricultural, Biological, and Environmental Statistics : Volume 17
Journal of Agricultural, Biological, and Environmental Statistics : Volume 16
Journal of Agricultural, Biological, and Environmental Statistics : Volume 15
Journal of Agricultural, Biological, and Environmental Statistics : Volume 14
Journal of Agricultural, Biological, and Environmental Statistics : Volume 14, Issue 4, December 2009
Journal of Agricultural, Biological, and Environmental Statistics : Volume 14, Issue 3, September 2009
Journal of Agricultural, Biological, and Environmental Statistics : Volume 14, Issue 2, June 2009
Journal of Agricultural, Biological, and Environmental Statistics : Volume 14, Issue 1, March 2009
Modeling interval-censored, clustered cow udder quarter infection times through the shared gamma frailty model
Estimating the proportions of closely related species: Performance of the two-phase ratio estimator
Bayesian analysis of semiparametric linear-circular models
A mixed model for investigating a population of asymptotic growth curves using restricted B-splines
Mastitis in dairy production: Estimation of sensitivity, specificity and disease prevalence in the absence of a gold standard
A kriging approach to the analysis of climate model experiments
A model-free test for independence between time series
Erratum to: A Simple Definition of Detection Limit
Journal of Agricultural, Biological, and Environmental Statistics : Volume 13
Journal of Agricultural, Biological, and Environmental Statistics : Volume 12
Journal of Agricultural, Biological, and Environmental Statistics : Volume 11
Journal of Agricultural, Biological, and Environmental Statistics : Volume 10
Journal of Agricultural, Biological, and Environmental Statistics : Volume 9
Journal of Agricultural, Biological, and Environmental Statistics : Volume 8
Journal of Agricultural, Biological, and Environmental Statistics : Volume 7
Journal of Agricultural, Biological, and Environmental Statistics : Volume 6

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Bayesian analysis of semiparametric linear-circular models

Content Provider Springer Nature Link
Author Bhattacharya, Sourabh Sengupta, Ashis
Copyright Year 2009
Abstract In many environmental and agricultural studies, data are collected on both linear and circular random variables, with possible dependence between the variables. Classically, the analysis of such data has been carried out in a classical regression framework. We propose a Bayesian hierarchical framework to handle all forms of uncertainty arising in a linear-circular data set. One novelty of our multivariate linear-circular model is that, marginally, the circular component is assumed to be a mixture model with an unknown number of von Mises (or circular normal) distributions. We use the Dirichlet process to introduce variability in the model dimensionality, and develop a simple Gibbs sampling algorithm for simulating the mixture components. Although we illustrate our methodology on von Mises mixtures, it is widely applicable. We thus avoid complicated reversible-jump Markov chain Monte Carlo methods, which are considered ideal for analyzing mixtures of unknown number of distributions. We illustrate our methodologies with simulated and real data sets. Using pseudo-Bayes factors, we also compare different models associated with both fixed and variable numbers of von Mises distributions. Our findings suggest that models associated with varying numbers of mixture components perform at least as well as those with known numbers of mixture components. We tentatively argue that model averaging associated with variable number of mixture components improves the model’s predictive power, which compensates for the lack of knowledge of the actual number of mixture components.
Starting Page 33
Ending Page 65
Page Count 33
File Format PDF
ISSN 10857117
Journal Journal of Agricultural, Biological, and Environmental Statistics
Volume Number 14
Issue Number 1
e-ISSN 15372693
Language English
Publisher Springer-Verlag
Publisher Date 2009-01-01
Publisher Place New York
Access Restriction One Nation One Subscription (ONOS)
Subject Keyword Bayesian analysis Circular random variable Cross-validation Dirichlet process Leave-one-out posterior Markov chain Monte Carlo Pseudo-Bayes factor Statistics for Life Sciences, Medicine, Health Sciences Agriculture Environmental Monitoring/Analysis Biostatistics
Content Type Text
Resource Type Article
Subject Applied Mathematics Statistics and Probability Environmental Science Agricultural and Biological Sciences Statistics, Probability and Uncertainty
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