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Cook, R. D. and Weisberg, S. Residuals and Influence in Regression. Chapman and Hall. London. (1982).

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Article

Performance of Log-Beta Log-Logistic Regression Model

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


American Journal of Applied Mathematics and Statistics. 2016, Vol. 4 No. 3, 74-86
DOI: 10.12691/ajams-4-3-3
Copyright © 2016 Science and Education Publishing

Cite this paper:
Mahmoud Riad Mahmoud, Naglaa A. Morad, 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.

Correspondence to: Moshera  A. M. Ahmad, Department of Applied Statistics and Econometrics, Institute of Statistical Studies and Research, Cairo University. Email: moshera_ahmad@pg.cu.edu.eg

Abstract

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.

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