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Colloquium - 21 Sept 2000

Department of Statistics
Texas A&M University

ABSTRACT: The Inverse Gaussian (IG) distribution is perhaps more useful in practice than its better known big Gaussian brother. The IG distribution goes back to Schroedinger (1915), Schmoluchowski (1915), Wald (1947) and Tweedie (1947), whereas the normal distribution can be traced to De Moivre (1738), about a century before Gauss popularized it. The two parameter IG distribution is ideally suited for modeling non-negative positively skewed data, which arose out of the analysis of Brownian motion and now is used for analyzing data from fields as diverse as ecology and the internet, see Seshadri (1999). The distribution is intriguingly similar to the normal distribution in many respects, see Folks and Chhikara (1978), Iyengar and Patwardhan (1988), and the inference methods associated with it use well-known normal theory entities such as t, chi-squared, and F tests. In this talk, we discuss the Gaussian/Inverse Gaussian Analogies using some old, some new, as well as some emerging results.

Refreshments will be served in the Blocker Building, Room 447, at 3:30 p.m.

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