Estimation in multivariate nonnormal distributions with stochastic variance function


Islam M. Q.

JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS, vol.255, pp.698-714, 2014 (Journal Indexed in SCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 255
  • Publication Date: 2014
  • Doi Number: 10.1016/j.cam.2013.06.032
  • Title of Journal : JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS
  • Page Numbers: pp.698-714
  • Keywords: Correlation coefficient, Least squares, Multivariate nonnormal distribution, Multivariate t-distribution, Modified maximum likelihood, Short-tailed distribution, LINEAR-REGRESSION MODEL, MAXIMUM-LIKELIHOOD, DESIGN, PARAMETERS, SAMPLES

Abstract

In this paper the problem of estimation of location and scatter of multivariate nonnormal distributions is considered. Estimators are derived under a maximum likelihood setup by expressing the non-linear likelihood equations in the linear form. The resulting estimators are analytical expressions in terms of sample values and, hence, are easily computable and can also be manipulated analytically. These estimators are found to be remarkably more efficient and robust as compared to the least square estimators. They also provide more powerful tests in testing various relevant statistical hypotheses. (C) 2013 Elsevier B.V. All rights reserved.