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Relevant bibliographies by topics / Zero-and-One Inflated Beta Regression

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Academic literature on the topic 'Zero-and-One Inflated Beta Regression'

Author: Grafiati

Published: 4 June 2021

Last updated: 16 February 2022

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Journal articles on the topic "Zero-and-One Inflated Beta Regression"

1

Liu, Fang, and Yunchuan Kong. "zoib: An R Package for Bayesian Inference for Beta Regression and Zero/One Inflated Beta Regression." R Journal 7, no. 2 (2015): 34. http://dx.doi.org/10.32614/rj-2015-019.

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2

Ospina, Raydonal, and Silvia L. P. Ferrari. "A general class of zero-or-one inflated beta regression models." Computational Statistics & Data Analysis 56, no. 6 (2012): 1609–23. http://dx.doi.org/10.1016/j.csda.2011.10.005.

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3

Liu, Fang, and Evercita C. Eugenio. "A review and comparison of Bayesian and likelihood-based inferences in beta regression and zero-or-one-inflated beta regression." Statistical Methods in Medical Research 27, no. 4 (2016): 1024–44. http://dx.doi.org/10.1177/0962280216650699.

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Beta regression is an increasingly popular statistical technique in medical research for modeling of outcomes that assume values in (0, 1), such as proportions and patient reported outcomes. When outcomes take values in the intervals [0,1), (0,1], or [0,1], zero-or-one-inflated beta (zoib) regression can be used. We provide a thorough review on beta regression and zoib regression in the modeling, inferential, and computational aspects via the likelihood-based and Bayesian approaches. We demonstrate the statistical and practical importance of correctly modeling the inflation at zero/one rather

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4

Abdel-Karim, Amany Hassan. "Extended zero-one inflated beta and adjusted three-part regression models for proportional data analysis." Communications in Statistics - Simulation and Computation 46, no. 8 (2016): 6155–72. http://dx.doi.org/10.1080/03610918.2016.1197248.

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5

Ríos-Pena, Laura, Thomas Kneib, Carmen Cadarso-Suárez, Nadja Klein, and Manuel Marey-Pérez. "Studying the occurrence and burnt area of wildfires using zero-one-inflated structured additive beta regression." Environmental Modelling & Software 110 (December 2018): 107–18. http://dx.doi.org/10.1016/j.envsoft.2018.03.008.

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6

Aguirre-Larracoechea, Urko, and Cruz E. Borges. "Imputation for Repeated Bounded Outcome Data: Statistical and Machine-Learning Approaches." Mathematics 9, no. 17 (2021): 2081. http://dx.doi.org/10.3390/math9172081.

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Real-life data are bounded and heavy-tailed variables. Zero-one-inflated beta (ZOIB) regression is used for modelling them. There are no appropriate methods to address the problem of missing data in repeated bounded outcomes. We developed an imputation method using ZOIB (i-ZOIB) and compared its performance with those of the naïve and machine-learning methods, using different distribution shapes and settings designed in the simulation study. The performance was measured employing the absolute error (MAE), root-mean-square-error (RMSE) and the unscaled mean bounded relative absolute error (UMBR

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7

Zhang, Xinyan, Boyi Guo, and Nengjun Yi. "Zero-Inflated gaussian mixed models for analyzing longitudinal microbiome data." PLOS ONE 15, no. 11 (2020): e0242073. http://dx.doi.org/10.1371/journal.pone.0242073.

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Motivation The human microbiome is variable and dynamic in nature. Longitudinal studies could explain the mechanisms in maintaining the microbiome in health or causing dysbiosis in disease. However, it remains challenging to properly analyze the longitudinal microbiome data from either 16S rRNA or metagenome shotgun sequencing studies, output as proportions or counts. Most microbiome data are sparse, requiring statistical models to handle zero-inflation. Moreover, longitudinal design induces correlation among the samples and thus further complicates the analysis and interpretation of the micro

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8

Di Brisco, Agnese Maria, Sonia Migliorati, and Andrea Ongaro. "Robustness against outliers: A new variance inflated regression model for proportions." Statistical Modelling 20, no. 3 (2019): 274–309. http://dx.doi.org/10.1177/1471082x18821213.

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This article addresses the issue of building regression models for bounded responses, which are robust in the presence of outliers. To this end, a new distribution on (0,1) and a regression model based on it are proposed and some properties are derived. The distribution is a mixture of two beta components. One of them, showing a higher variance (variance inflated) is expected to capture outliers. Within a Bayesian approach, an extensive robustness study is performed to compare the new model with three competing ones present in the literature. A broad range of inferential tools are considered,

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9

Wohlgemuth, Murilo, Carlos Ernani Fries, Ângelo Márcio Oliveira Sant’Anna, Ricardo Giglio, and Diego Castro Fettermann. "Assessment of the technical efficiency of Brazilian logistic operators using data envelopment analysis and one inflated beta regression." Annals of Operations Research 286, no. 1-2 (2018): 703–17. http://dx.doi.org/10.1007/s10479-018-3105-7.

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10

Wang, Yi (Victor), Paolo Gardoni, Colleen Murphy, and Stéphane Guerrier. "Predicting Fatality Rates Due to Earthquakes Accounting for Community Vulnerability." Earthquake Spectra 35, no. 2 (2019): 513–36. http://dx.doi.org/10.1193/022618eqs046m.

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The existing prediction models for earthquake fatalities usually require a detailed building inventory that might not be readily available. In addition, existing models tend to overlook the socioeconomic characteristics of communities of interest as well as zero-fatality data points. This paper presents a methodology that develops a probabilistic zero-inflated beta regression model to predict earthquake fatality rates given the geographic distributions of earthquake intensities with data reflecting community vulnerability. As an illustration, the prediction model is calibrated using fatality d

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