İktisat Bölümü Yayın Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/402
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Article 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.Article Citation - WoS: 4Citation - Scopus: 6Mahalanobis 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.Article Citation - WoS: 13Citation - Scopus: 13Multiple 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.