Spatial Beta Regression Model with Random Effect

Kalhori, Lida; Mohhamadzadeh, Mohsen

doi:10.18869/acadpub.jsri.13.2.215

Cited by 2 publications

(2 citation statements)

References 12 publications

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“…Beta regression has been applied to describe many socioeconomic phenomena, such as the poverty (e.g., Do, Wang, and Elliott ) and migration rates (e.g., Kalhori and Mohhamadzadeh ), which are continuous variables and constrained to the interval [0, 1]. This list can be extended by rescaling any limited variable to the interval (0, 1).…”

Section: Literature Reviewmentioning

confidence: 99%

Impacts of Spatial Autocorrelation in Georeferenced Beta and Multinomial Random Variables

Griffith

Chun

2019

Geographical Analysis

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The literature is replete with acknowledgments that spatial autocorrelation (SA) inflates the variance of a random variable (RV), and that it also may alter other RV distributional properties. In most studies, impacts of SA have been examined only for the three most commonly used distributions: the normal, Poisson (and its negative binomial counterpart), and binomial distributions; much less is known about its effects on two other RVs that are utilized in GIScience research: the beta and the multinomial. The beta distributionwhich is considered to be very flexible because it can mimic a uniform, exponential, sinusoidal, and normal RV-can be utilized to analyze the radiance of a remotely sensed image, for example. The multinomial distribution, a generalization of the binomial distribution, has been widely used for land use classification, and to describe land use change. The literature also suggests that RV impacts of negative SA, a neglected topic in spatial analysis, may differ from those of positive SA, at least for some RVs (e.g., the Poisson RV). The purpose of this article is to extend the investigation of effects of SA to beta and multinomial RVs, with both positive SA and negative SA assessed and contrasted with each other, using simulation experiments. The simulation experiments are designed to support this assessment. One of the major discoveries is that impacts of positive SA and negative SA behave similarly when a RV conforms to a normal distribution; however, maximum negative SA is unable to materialize for asymmetric RV, whereas positive SA always converges upon its maximum.

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Section: Literature Reviewmentioning

confidence: 99%

Impacts of Spatial Autocorrelation in Georeferenced Beta and Multinomial Random Variables

Griffith

Chun

2019

Geographical Analysis

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“…Other extensions of the Beta regression includes spatial data analysis [13] and spatial analysis of structured additive regression model [14]. In the last recent paper in the Beta regression content [15], the authors focused on random effect models to study spatially correlated rate data to account for the spatial correlation of data in the model. They implemented Bayesian approach for parameter estimation in a two level Beta regression for the correlated data.…”

Section: Introductionmentioning

confidence: 99%

Multilevel Zero-inflated Censored Beta Regression Modeling for Proportions and Rate Data with Extra-zeros

Tapak

Hamidi

Sadeghifar

et al. 2019

Preprint

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Objectives Zero-inflated proportion or rate data nested in clusters due to the sampling structure can be found in many disciplines. Sometimes, the rate response may not be observed for some study units because of some limitations (false negative) like failure in recording data and the zeros are observed instead of the actual value of the rate/proportions (low incidence). In this study, we proposed a multilevel zero-inflated censored Beta regression model that can address zero-inflation rate data with low incidence.Methods We assumed that the random effects are independent and normally distributed. The performance of the proposed approach was evaluated by application on a three level real data set and a simulation study. We applied the proposed model to analyze brucellosis diagnosis rate data and investigate the effects of climatic and geographical position. For comparison, we also applied the standard zero-inflated censored Beta regression model that does not account for correlation.Results Results showed the proposed model performed better than zero-inflated censored Beta based on AIC criterion. Height (p-value <0.0001), temperature (p-value <0.0001) and precipitation (p-value = 0.0006) significantly affected brucellosis rates. While, precipitation in ZICBETA model was not statistically significant (p-value =0.385). Simulation study also showed that the estimations obtained by maximum likelihood approach had reasonable in terms of mean square error.Conclusions The results showed that the proposed method can capture the correlations in the real data set and yields accurate parameter estimates.

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Spatial Beta Regression Model with Random Effect

Cited by 2 publications

References 12 publications

Impacts of Spatial Autocorrelation in Georeferenced Beta and Multinomial Random Variables

Impacts of Spatial Autocorrelation in Georeferenced Beta and Multinomial Random Variables

Multilevel Zero-inflated Censored Beta Regression Modeling for Proportions and Rate Data with Extra-zeros

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