Volume 12, Issue 4, December 2013, Pages 330 - 347
The Log-Beta Generalized Half-Normal Regression Model
- Rodrigo R. Pescim, Edwin M.M. Ortega, Gauss M. Cordeiro, Clarice G.B. Demtrio, G.G. Hamedani
- Corresponding Author
- Rodrigo R. Pescim
Available Online 1 December 2013.
- https://doi.org/10.2991/jsta.2013.12.4.2How to use a DOI?
- Beta generalized half normal; Censored data; Regression model; Survival function
- 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
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 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 - https://doi.org/10.2991/jsta.2013.12.4.2 ID - Pescim2013 ER -