Maximum Lq-Likelihood Estimation for the parameters of Marshall-Olkin Extended Burr XII Distribution

Abstract

Marshall-Olkin extended Burr XII (MOEBXII) distribution is proposed by Al-Saiari et al. (2014) to obtain a more ‡exible family of distributions. Some estimation methods like maximum likelihood, Bayes and M estimations are used to estimate the parameters of the MOEBXII distribution in literature. In this paper, we propose to use Maximum Lq (MLq) estimation method to …nd alternative estimators for the parameters of the MOEBXII distribution. We give some simulation studies and a real data example to compare the performance of the MLq estimators with the maximum likelihood and M estimators. According to our results MLq estimation method is a good alternative to the maximum likelihood and M estimation methods in the presence of outliers.

Conditional maximum Lq-likelihood estimation for regression model with autoregressive error terms

“…By deriving the asymptotic distribution of this test statistic, the authors have demonstrated its robustness both analytically and numerically, and they investigated the properties of both its influence function and its breakdown point. Also Ozdemir et al (2019) use the MLq estimation method to estimate the parameters of Marshall-Olkin extended Burr XII distribution and show that MLq estimation method outperform the ML. Recently, Dogru et al (2018) propose parameter estimation of the multivariate t distribution using the MLq estimation, provide that unlike the ML estimation the degrees of freedom parameters can be estimated along with the other parameters, and still gain the robustness.…”