Nonnormal regression. I. Skew distributions

Date

2001-01-01

Author

İslam, Muhammed Qamarul
Yildirim, F

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In a linear regression model of the type y = thetaX + e, it is often assumed that the random error e is normally distributed. In numerous situations, e.g., when y measures life times or reaction times, e typically has a skew distribution. We consider two important families of skew distributions, (a) Weibull with support IR: (0, infinity) on the real line, and (b) generalised logistic with support IR: (-infinity, infinity). Since the maximum likelihood estimators are intractable in these situations, we derive modified likelihood estimators which have explicit algebraic forms and are, therefore, easy to compute. We show that these estimators are remarkably efficient, and robust. We develop hypothesis testing procedures and give a real life example.

Subject Keywords

Robustness, Maximum Likelihood;, Modified Maximum Likelihood, Least Squares, Weibull, Generalised Logistic

URI

https://hdl.handle.net/11511/48452

Journal

COMMUNICATIONS IN STATISTICS-THEORY AND METHODS

DOI

https://doi.org/10.1081/sta-100104347

Collections

Department of Statistics, Article

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Citation Formats

M. Q. İslam and F. Yildirim, “Nonnormal regression. I. Skew distributions,” COMMUNICATIONS IN STATISTICS-THEORY AND METHODS, pp. 993–1020, 2001, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/48452.