American Journal of Applied Mathematics and Statistics
ISSN (Print): 2328-7306 ISSN (Online): 2328-7292 Website: Editor-in-chief: Mohamed Seddeek
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American Journal of Applied Mathematics and Statistics. 2016, 4(3), 74-86
DOI: 10.12691/ajams-4-3-3
Open AccessArticle

Performance of Log-Beta Log-Logistic Regression Model

Mahmoud Riad Mahmoud1, Naglaa A. Morad2 and Moshera A. M. Ahmad2,

1Department of Mathematical Statistics, Institute of Statistical Studies and Research, Cairo University

2Department of Applied Statistics and Econometrics, Institute of Statistical Studies and Research, Cairo University

Pub. Date: July 02, 2016

Cite this paper:
Mahmoud Riad Mahmoud, Naglaa A. Morad and Moshera A. M. Ahmad. Performance of Log-Beta Log-Logistic Regression Model. American Journal of Applied Mathematics and Statistics. 2016; 4(3):74-86. doi: 10.12691/ajams-4-3-3


For the log-beta log-logistic regression model, we derive the appropriate matrices for assessing the local influence on the parameter estimates under perturbation scheme. Using a set of real data, global and local influences of individual observations on the stated model are considered. Besides, for different parameter settings, sample sizes, and censoring percentages, various simulation studies are performed to the performance of the log-beta log-logistic regression model. In addition, the empirical distribution of the martingale residuals is displayed against the normal distribution for comparison. These studies suggest that the martingale residual has shaped normal form.

likelihood displacement local influence approach beta log-logistic distribution martingale residuals sensitivity analysis

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