David Sprott Distinguished Lecture by Art B. Owen

Wednesday, May 14, 2014 4:00 pm - 4:00 pm EDT (GMT -04:00)

Empirical likelihood

Art Owen
Likelihood methods provide one of the most versatile and effective ways to handle data. They give us tests and confidence intervals with very strong optimality measures. But the cost for using them is usually that we have to know a family of distributions generating our data. Very often we have no reason to assume that any of our usual parametric families truly contain the distribution of our data. Without such a distribution, parametric models can be misleading, i.e., wrong.

Empirical likelihood is a method that provides the benefits of a likelihood function without requiring the user to know a parametric family for the data. The data themselves supply their own parametric family at a quick enough rate to make the resulting tests and confidence intervals reliable. This talk will show some examples of how empirical likelihood can be used.

Art B. Owen, Stanford University

Art Owen is a professor of statistics at Stanford University, where he has held a faculty position since 1985. He obtained a BMath in statistics and computer science from the University of Waterloo in 1981, and a PhD in statistics from Stanford in 1987. His research interests include empirical likelihood, computer experiments, Monte Carlo and quasi-Monte Carlo sampling, as well as high dimensional transposable style data sets that come up in bioinformatics and Internet applications. His pioneering work on empirical likelihood has opened up a large research area of its own in statistics, and resulted in a very highly cited book on the subject, which he published in 2001.

David A. Sprott (1930-2013)

Professor David Sprott was the first Chair (1967-1975) of the Department of Statistics and Actuarial Science at the University of Waterloo and first Dean of the Faculty of Mathematics (1967-1972). The David Sprott Distinguished Lecture Series was created in recognition of his tremendous leadership at a formative time of our department, as well as his highly influential research in statistical science.