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- W3201620991 endingPage "107345" @default.
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- W3201620991 abstract "A new Bayesian regression framework is presented for the analysis of continuous response data with support restricted to an unknown finite interval. A four-parameter beta distribution is assumed for the response conditioning on covariates, with the mean or mode depending linearly on covariates through a known link function. An informative g -prior is proposed to incorporate the prior distribution for the marginal mean or mode of the response. Byproducts of the Markov chain Monte Carlo sampling for implementing the proposed method lead to model criteria useful for model selection. Goodness-of-fit of the model is assessed using Cox-Snell residual plots. The methodology is illustrated in simulations and demonstrated in two real-life data applications. An R package, betaBayes , is developed for easy implementation of the proposed regression methodology." @default.
- W3201620991 created "2021-09-27" @default.
- W3201620991 creator A5001818466 @default.
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- W3201620991 date "2022-03-01" @default.
- W3201620991 modified "2023-09-24" @default.
- W3201620991 title "Bayesian beta regression for bounded responses with unknown supports" @default.
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- W3201620991 doi "https://doi.org/10.1016/j.csda.2021.107345" @default.
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