The Log-Beta Generalized Half-Normal Regression Model

DOI

10.2991/jsta.2013.12.4.2How to use a DOI?

Keywords

Beta generalized half normal; Censored data; Regression model; Survival function

Abstract

We introduce a log-linear regression model based on the beta generalized half-normal distribution (Pescim et al., 2010).We formulate and develop a log-linear model using a new distribution so-called the log-beta general- ized half normal distribution.We derive expansions for the cumulative distribution and density functions which do not depend on complicated functions. We obtain formal expressions for the moments and moment gener- ating function. We characterize the proposed distribution using a simple relationship between two truncated moments. An advantage of the new distribution is that it includes as special sub-models classical distributions reported in the lifetime literature. We also show that the new regression model can be applied to censored data since it represents a parametric family of models that includes as special cases several widely-known regression models. It therefore can be used more effectively in the analysis of survival data. We investigate the maximum likelihood estimates of the model parameters by considering censored data. We demonstrate that our extended regression model is very useful to the analysis of real data and may give more realistic fits than other special regression models.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Rodrigo R. Pescim
AU  - Edwin M.M. Ortega
AU  - Gauss M. Cordeiro
AU  - Clarice G.B. Demtrio
AU  - G.G. Hamedani
PY  - 2013
DA  - 2013/12/01
TI  - The Log-Beta Generalized Half-Normal Regression Model
JO  - Journal of Statistical Theory and Applications
SP  - 330
EP  - 347
VL  - 12
IS  - 4
SN  - 2214-1766
UR  - https://doi.org/10.2991/jsta.2013.12.4.2
DO  - 10.2991/jsta.2013.12.4.2
ID  - Pescim2013
ER  -