Open AccessJournal Article
A Bayesian Approach to Parameter Estimation in Simplex Regression Model: A Comparison with Beta Regression
TL;DR: The use of Bayesian techniques to estimate the parameters of the simplex regression supported on the implementation of some simulations and a comparison with Beta regression is presented.
read more
Abstract: Some variables are restricted to the open interval (0; 1) and several methods have been developed to work with them under the scheme of the regression analysis. Most of research consider maximum likelihood methods and the use of Beta or Simplex distributions. This paper presents the use of Bayesian techniques to estimate the parameters of the simplex regression supported on the implementation of some simulations and a comparison with Beta regression. We consider both models with constant variance and models with heteroscedasticity.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Efficiency in angolan hydro-electric power station: A two-stage virtual frontier dynamic DEA and simplex regression approach
TL;DR: In this article, the efficiency assessment of Angolan hydro-electric power stations using the VDRAM (Virtual Frontier Dynamic Range Adjusted Model) DEA was conducted. And the authors used Simplex Regression to handle skewed and asymmetrical efficiency scores.
30
Strategic fit of mergers and acquisitions in Latin American airlines: a two-stage DEA approach
TL;DR: In this paper, the authors assess the efficiency determinants of mergers and acquisitions (M&A) in the context of Latin American airlines based on business-related variables commonly found in the literature and identify preferable potential airline matches in light of fleet mix, ownership structure and geographical proximity.
Distance-based beta regression for prediction of mutual funds
TL;DR: In this paper, the authors proposed a distance-based model for regression with variable dispersion, which is useful for situations where the response variable is a rate, a proportion or parts per million, and this variable is related to a mixture of continuous and categorical explanatory variables.
7
Modelo de regressão beta para teste de estresse em risco de crédito de instituições financeiras
Diogo Suzart Uzêda Picco
- 30 Jul 2016
TL;DR: In this article, the authors propose a method to solve the problem of "uniformity" and "uncertainty" in the context of broadcast broadcast, and it works well.
Alternative regression models to Beta distribution under Bayesian approach
Rosineide Fernando da Paz
- 25 Aug 2017
TL;DR: In this article, the Simplex mixture model is used to model the distribution of bounded random variables and the model is extended to the context of regression models with the inclusion of covariates.
References
•Journal Article
R: A language and environment for statistical computing.
TL;DR: Copyright (©) 1999–2012 R Foundation for Statistical Computing; permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and permission notice are preserved on all copies.
410.8K
•Journal Article
Bayesian measures of model complexity and fit
TL;DR: The posterior mean deviance is suggested as a Bayesian measure of fit or adequacy, and the contributions of individual observations to the fit and complexity can give rise to a diagnostic plot of deviance residuals against leverages.
7.6K
Beta Regression for Modelling Rates and Proportions
TL;DR: In this article, the authors proposed a regression model where the response is beta distributed using a parameterization of the beta law that is indexed by mean and dispersion parameters, which is useful for situations where the variable of interest is continuous and restricted to the interval (0, 1) and is related to other variables through a regression structure.
Beta Regression in R
TL;DR: The betareg package is described which provides the class of beta regressions in the R system for statistical computing and incorporates features such as heteroskedasticity or skewness which are commonly observed in data taking values in the standard unit interval, such as rates or proportions.
R2WinBUGS: A Package for Running WinBUGS from R
TL;DR: The R2WinBUGS package provides convenient functions to call WinBUGS from R and automatically writes the data and scripts in a format readable by WinBUGs for processing in batch mode, which is possible since version 1.4.