Event
Statistics Seminar: Ben Kedem, Repeated out of Sample Fusion in Small Tail Probability Estimation
Thursday, April 13, 2017
3:30 p.m.
1313 MTH
"Thinking Out of the Sample": Repeated out of Sample Fusion in the Estimation of Small Tail Probabilities
Ben Kedem
Professor
Statistics Program, Department of Mathematics
Affiliate Faculty, Institute for Systems Research
When: Thu, April 13, 2017 - 3:30pm
Where: MTH 1313
Abstract
Often, it is required to estimate the probability that a quantity such as mercury, lead, toxicity level, plutonium, temperature, rainfall, damage, wind speed, risk, etc., exceeds an unsafe high threshold. The probability in question is then very small. To estimate such a probability, we need information about large values of the quantity of interest. However, in many cases, the data only contain values far below the designated threshold, let alone exceedingly large values, which ostensibly renders the problem insolvable. It is shown that by repeated fusion of the data with externally generated random data, more information about small tail probabilities is obtained with the aid of certain new statistical functions. This provides short, yet reliable interval estimates based on moderately large samples. A comparison of the approach with the well known Peaks over Threshold (POT) method from extreme values theory, using both artificial and real data, points to the merit of repeated out of sample fusion (ROSF).
