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Bayesian inference in a heteroscedastic replicated measurement error model using heavy-tailed distributions
Nanjing University of Information Science and Technology.
Nanjing University of Information Science and Technology.
Nanjing University of Information Science and Technology.
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Statistics.
2017 (English)In: Journal of Statistical Computation and Simulation, ISSN 0094-9655, E-ISSN 1563-5163, Vol. 87, no 15, 2915-2928 p.Article in journal (Refereed) Published
Abstract [en]

We introduce a multivariate heteroscedastic measurement error model for replications under scale mixtures of normal distribution. The model can provide a robust analysis and can be viewed as a generalization of multiple linear regression from both model structure and distribution assumption. An efficient method based on Markov Chain Monte Carlo is developed for parameter estimation. The deviance information criterion and the conditional predictive ordinates are used as model selection criteria. Simulation studies show robust inference behaviours of the model against both misspecification of distributions and outliers. We work out an illustrative example with a real data set on measurements of plant root decomposition.

Place, publisher, year, edition, pages
2017. Vol. 87, no 15, 2915-2928 p.
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:uu:diva-331140DOI: 10.1080/00949655.2017.1349131ISI: 000407395300004OAI: oai:DiVA.org:uu-331140DiVA: diva2:1148488
Available from: 2017-10-11 Created: 2017-10-11 Last updated: 2017-11-15Bibliographically approved

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Jin, Shaobo

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