article

Dynamic Bayesian beta models

Authors:

C. Q. da-Silva,

L. T. CorreiaAuthors Info & Claims

Computational Statistics & Data Analysis, Volume 55, Issue 6

Pages 2074 - 2089

https://doi.org/10.1016/j.csda.2010.12.011

Published: 01 June 2011 Publication History

Abstract

We develop a dynamic Bayesian beta model for modeling and forecasting single time series of rates or proportions. This work is related to a class of dynamic generalized linear models (DGLMs), although, for convenience, we use non-conjugate priors. The proposed methodology is based on approximate analysis relying on Bayesian linear estimation, nonlinear system of equations solution and Gaussian quadrature. Intentionally we avoid MCMC strategy, keeping the desired sequential nature of the Bayesian analysis. Applications to both real and simulated data are provided.

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Cited By

Can CErgun GSoyer R(2022)Bayesian Analysis of Proportions via a Hidden Markov ModelMethodology and Computing in Applied Probability10.1007/s11009-022-09971-024:4(3121-3139)Online publication date: 3-Aug-2022
https://dl.acm.org/doi/10.1007/s11009-022-09971-0
Rodrigues GNott DSisson S(2020)Likelihood-free approximate Gibbs samplingStatistics and Computing10.1007/s11222-020-09933-x30:4(1057-1073)Online publication date: 1-Jul-2020
https://dl.acm.org/doi/10.1007/s11222-020-09933-x
Santos JCosta J(2019)An Algorithm for Prior Elicitation in Dynamic Bayesian Models for Proportions with the Logit Link FunctionMethodology and Computing in Applied Probability10.1007/s11009-018-9642-321:1(169-183)Online publication date: 1-Mar-2019
https://dl.acm.org/doi/10.1007/s11009-018-9642-3
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Dynamic Bayesian beta models
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Published In

cover image Computational Statistics & Data Analysis

Computational Statistics & Data Analysis Volume 55, Issue 6

June, 2011

203 pages

ISSN:0167-9473

Issue’s Table of Contents

Copyright © Elsevier B.V. © 2010.

Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 June 2011

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Cited By

Can CErgun GSoyer R(2022)Bayesian Analysis of Proportions via a Hidden Markov ModelMethodology and Computing in Applied Probability10.1007/s11009-022-09971-024:4(3121-3139)Online publication date: 3-Aug-2022
https://dl.acm.org/doi/10.1007/s11009-022-09971-0
Rodrigues GNott DSisson S(2020)Likelihood-free approximate Gibbs samplingStatistics and Computing10.1007/s11222-020-09933-x30:4(1057-1073)Online publication date: 1-Jul-2020
https://dl.acm.org/doi/10.1007/s11222-020-09933-x
Santos JCosta J(2019)An Algorithm for Prior Elicitation in Dynamic Bayesian Models for Proportions with the Logit Link FunctionMethodology and Computing in Applied Probability10.1007/s11009-018-9642-321:1(169-183)Online publication date: 1-Mar-2019
https://dl.acm.org/doi/10.1007/s11009-018-9642-3
Li P(2018)Efficient MCMC estimation of inflated beta regression modelsComputational Statistics10.1007/s00180-017-0747-x33:1(127-158)Online publication date: 1-Mar-2018
https://dl.acm.org/doi/10.1007/s00180-017-0747-x
Fabrizi ETrivisano C(2016)Small area estimation of the Gini concentration coefficientComputational Statistics & Data Analysis10.1016/j.csda.2016.01.01099:C(223-234)Online publication date: 1-Jul-2016
https://dl.acm.org/doi/10.1016/j.csda.2016.01.010
Cheng CHung YBalakrishnan N(2014)Generating beta random numbers and Dirichlet random vectors in RComputational Statistics & Data Analysis10.5555/2749482.274985171:C(1011-1020)Online publication date: 1-Mar-2014
https://dl.acm.org/doi/10.5555/2749482.2749851

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