Nonnormal regression. II. Symmetric distributions

Tiku M., Islam M., Selcuk A.

COMMUNICATIONS IN STATISTICS-THEORY AND METHODS, vol.30, no.6, pp.1021-1045, 2001 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 30 Issue: 6
  • Publication Date: 2001
  • Doi Number: 10.1081/sta-100104348
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.1021-1045
  • Keywords: symmetric distributions, modified likelihood, regression, robustness, kurtosis, hypothesis testing, inliers, outliers, nonnormality, TIME-SERIES MODELS, SAMPLES
  • Middle East Technical University Affiliated: Yes


Salient features of a family of short-tailed symmetric distributions, introduced recently by Tiku and Vaughan [1], are enunciated. Assuming the error distribution to be one of this family, the methodology of modified likelihood is used to derive MML estimators of parameters in a linear regression model. The estimators are shown to be efficient, and robust to inliers. This paper is essentially the first to achieve robustness to infers. The methodology is extended to long-tailed symmetric distributions and the resulting estimators are shown to be efficient, and robust to outliers. This paper should be read in conjunction with Islam et al. [2] who develop modified likelihood methodology for skew distributions in the context of linear regression.