NONNORMAL REGRESSION. II. SYMMETRIC DISTRIBUTIONS

By Moti L. Tiku, M. Islam, A. Selçuk. 2001

57 citations recorded by the source corpus.

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 inliers. 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.

Subfields: Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/52d128aaef77dc382243f2b2f556fdabab3533c9

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In Statistics and Probability

Citations in the corpus

Multiple Linear Regression Model Under Nonnormality (2005, background): “Islam et al. (2001) and Tiku et al. (2001) considered the simple linear regression model yi ¼ y0 þ y1xi þ ei ð1 i nÞ: ð1:1Þ They considered several nonnormal distributions for the random error e, both symmetric and skew.”