Browsing by Author "Tiku, Moti L."

Now showing 1 - 4 of 4

Citation - WoS: 18
Citation - Scopus: 18
Estimation in Bivariate Nonnormal Distributions With Stochastic Variance Functions
(Elsevier Science Bv, 2008) Tiku, Moti L.; Islam, M. Qamarul; Sazak, Hakan S.
Data sets in numerous areas of application can be modelled by symmetric bivariate nonnormal distributions. Estimation of parameters in such situations is considered when the mean and variance of one variable is a linear and a positive function of the other variable. This is typically true of bivariate t distribution. The resulting estimators are found to be remarkably efficient. Hypothesis testing procedures are developed and shown to be robust and powerful. Real life examples are given. (C) 2007 Elsevier B.V. All rights reserved.
Citation - WoS: 4
Citation - Scopus: 6
Mahalanobis Distance Under Non-Normality
(Taylor & Francis Ltd, 2010) Tiku, Moti L.; Islam, M. Qamarul; Qumsiyeh, Sahar B.
We give a novel estimator of Mahalanobis distance D2 between two non-normal populations. We show that it is enormously more efficient and robust than the traditional estimator based on least squares estimators. We give a test statistic for testing that D2=0 and study its power and robustness properties.
Multiple linear regression model under nonnormality
(Taylor & Francis Inc, 2004) Islam, M. Qamarul; Tiku, Moti L.
We consider multiple linear regression models under nonnormality. We derive modified maximum likelihood estimators (MMLEs) of the parameters and show that they are efficient and robust. We show that the least squares esimators are considerably less efficient. We compare the efficiencies of the MMLEs and the M estimators for symmetric distributions and show that, for plausible alternatives to an assumed distribution, the former are more efficient. We provide real-life examples.
Citation - WoS: 13
Citation - Scopus: 13
Multiple Linear Regression Model With Stochastic Design Variables
(Taylor & Francis Ltd, 2010) Islam, M. Qamarul; Tiku, Moti L.
In a simple multiple linear regression model, the design variables have traditionally been assumed to be non-stochastic. In numerous real-life situations, however, they are stochastic and non-normal. Estimators of parameters applicable to such situations are developed. It is shown that these estimators are efficient and robust. A real-life example is given.