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SPEAKER: |
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TITLE: |
"Order Statistics from I.NI.D. Random Variables with Applications to Robustness" |
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DAY: |
Wednesday, February 9, 2000 |
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TIME: |
3:30 p.m. [Coffee & cookies in BSB-202 at 3:00 p.m.] |
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PLACE: |
BSB-108 |
In this talk, I will discuss some of the algebraic and computational difficulties encountered when dealing with multiple outliers. I will introduce some of the mathematical methods for determining the effect of outliers on linear estimators of location and scale. By first directly considering the slippage model (with a slippage of p observations), I will illustrate the need for more general methods. This will lead to a discussion of independent and non-identically distributed random variables (I.NI.D.), and the mathematical methods for dealing with them. Some examples will also be given.
Aaron Childs has an undergraduate degree and Master's degree in Mathematics, and Ph.D. in statistics (1996) both from McMaster. During that time, he won the Governor General's Academic Medal, as well as the Graduate Students Association (GSA) Excellence in Teaching Award. He was a professor at St. Francis Xavier University in Nova Scotia for 3 years, and returned to McMaster in September 1999. He has taught a wide variety of courses in both mathematics and statistics. They include introductory statistics, statistical methods/biostatistics, mathematical statistics, stochastic processes, multivariate analysis, probability theory, real analysis, abstract algebra, number theory, calculus, linear algebra, measure and probability.
The references below have been suggested by Dr Childs as useful background for his talk. They have been placed on reserve at Thode Library (STATS 770: Statistics Seminar).
[1] Barnett, V, and Lewis, T. (1994). Outliers in Statistical Data, Third edition, John Wiley & Sons.
[2] David, H.A. (1981). Order Statistics, Second edition, John Wiley & Sons.
[3] Childs, A. and Balakrishnan, N. (1997). Some extensions in the robust estimation of parameters of exponential and double exponential distributions in the presence of multiple outliers. In: Handbook of Statistics - 15: Robust Inference (Eds., C.R. Rao and G.S. Maddala), 201-235, Elsevier Science, North-Holland, Amsterdam.