Regression model for proportions with probability masses at zero and one

Free

digital

Regression model for proportions with probability masses at zero and one

Article

journal	STATISTICA & APPLICAZIONI
issue	STATISTICA & APPLICAZIONI - 2012 - 2
title	Regression model for proportions with probability masses at zero and one
author	Raffaella Calabrese
publisher	Vita e Pensiero
format	Article \| Pdf
online since	02-2012
issn	18246672 (print)
Write a comment for this product

Offer valid until 02/12/2023

Download

Ebook format Pdf readable on these devices:

Computer
with Software:
- Adobe PDF Reader
Android
iPhone/Ipad
with App:
- Adobe PDF Reader
E-Book Reader
Ibs Tolino
Kobo

Description Readers' comments
SUMMARY In many settings, the variable of interest is a proportion with high concentration of data at the boundaries. This paper proposes a regression model for a fractional variable with nontrivial probability masses at the extremes. In particular, the dependent variable is assumed to be a mixed random variable, obtained as the mixture of a Bernoulli and a beta random variables. The endpoints of zero and one are modelled by a logistic regression model. The values belonging to the interval (0,1) are assumed to be beta distributed and their mean and dispersion are jointly modelled by using two link functions. The regression model proposed here accommodates skewness and heteroscedastic errors. Finally, an application to loan recovery process of Italian banks is also provided. Keywords: Proportions, Mixed Random Variable, Beta Regression, Skewness, Heteroscedasticity. Enter a comment for Regression model for proportions with probability masses at zero and one

Description Readers' comments

SUMMARY
In many settings, the variable of interest is a proportion with high concentration of data at the boundaries. This paper proposes a regression model for a fractional variable with nontrivial probability masses at the extremes. In particular, the dependent variable is assumed to be a mixed random variable, obtained as the mixture of a Bernoulli and a beta random variables. The endpoints of zero and one are modelled by a logistic regression model. The values belonging to the interval (0,1) are assumed to be beta distributed and their mean and dispersion are jointly modelled by using two link functions. The regression model proposed here accommodates skewness and heteroscedastic errors. Finally, an application to loan recovery process of Italian banks is also provided.

Keywords: Proportions, Mixed Random Variable, Beta Regression, Skewness, Heteroscedasticity.

Enter a comment for Regression model for proportions with probability masses at zero and one