Geostatistical mixed beta regression: a Bayesian approach

Lagos-Álvarez, Bernardo; Fustos-Toribio, Roberto; Figueroa-Zúñiga, Jorge; Mateu, Jorge

doi:10.1007/s00477-016-1308-5

Cited by 6 publications

(1 citation statement)

References 32 publications

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“…The beta distribution emerges as a widely adopted choice for this scenario due to its appealing properties, including the flexibility to exhibit varying shapes in its density function based on assigned parameter values. Lagos-Alvarez et al (2017) utilized the mean-dispersion reparameterized beta distribution introduced by Ferrari and Cribari-Neto (2004) and proposed a geostatistical beta regression model based on SGLMMs as presented by Diggle et al (1998). In this paper, we adopt the fixed dispersion version of their model, which we denote as the GBR model.…”

Section: Introductionmentioning

confidence: 99%

Robust Modeling for Continuous Bounded Spatial Data

Ahmadi,

Khaledi,

Sohrabi

et al. 2024

Preprint

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This paper develops two parametric median-based spatial regression models for geostatistical data defined on a bounded support. Specifically, these models resemble spatial generalized linear mixed models (SGLMMs), wherein the response variable is modeled using Kumaraswamy and Johnson-t distributions. The proposed models are more robust than the usual spatial beta regression model against the presence of asymmetries and extreme observations. A fully Bayesian analysis is employed to make inferences using the Metropolis-Hastings-within-Gibbs (MHG) sampling algorithm. Simulation studies and two applications to real-life mathematical Olympics data from Brazil and forest canopy cover data from the Zagros forests in Iran demonstrate that our proposals significantly outperform competitors in terms of predictive accuracy and precision of estimates.

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Section: Introductionmentioning

confidence: 99%

Robust Modeling for Continuous Bounded Spatial Data

Ahmadi,

Khaledi,

Sohrabi

et al. 2024

Preprint

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Regression modelling with the tilted beta distribution: A Bayesian approach

Hahn

2020

Can J Statistics

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Beta regression models are commonly used in the case of a dependent variable y that exists on the range (0,1). However, when y can additionally take on the values of zero and/or one, limitations of the beta distribution and beta regression models become apparent. One recent approach is to use an inflated beta regression model which has discrete point-valued components. In this article, we introduce a new class of regression models for y ∈ [0, 1] that is fully continuous. This allows the entirety of y to be treated as a continuum instead of discontinuously, which appears to be a new development for the literature. We use a Bayesian approach for estimation. We also illustrate the impact of different choices of prior distributions on empirical findings and perform a simulation study examining model fit.

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