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Estimation and hypothesis testing in multivariate linear regression models under non normality
Date
2017-01-01
Author
İslam, Muhammed Qamarul
Metadata
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This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
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This paper discusses the problem of statistical inference in multivariate linear regression models when the errors involved are non normally distributed. We consider multivariate t-distribution, a fat-tailed distribution, for the errors as alternative to normal distribution. Such non normality is commonly observed in working with many data sets, e.g., financial data that are usually having excess kurtosis. This distribution has a number of applications in many other areas of research as well. We use modified maximum likelihood estimation method that provides the estimator, called modified maximum likelihood estimator (MMLE), in closed form. These estimators are shown to be unbiased, efficient, and robust as compared to the widely used least square estimators (LSEs). Also, the tests based upon MMLEs are found to be more powerful than the similar tests based upon LSEs.
Subject Keywords
Statistics and Probability
URI
https://hdl.handle.net/11511/44081
Journal
COMMUNICATIONS IN STATISTICS-THEORY AND METHODS
DOI
https://doi.org/10.1080/03610926.2016.1183789
Collections
Department of Statistics, Article