As part of his Mahler Lecture tour in Australia, Professor Ivan Corwin from Columbia University will be visiting La Trobe Mathematics and Statistics Department. We invite you to attend this special event.
The Mahler Lectures are a biennial activity organised by the Australian Mathematical Society, and supported by the Australian Mathematical Sciences Institute, in which a prominent mathematician tours Australian universities giving lectures. See austms.org.au/tiki-read_article.php?articleId=417.
The lecture will be given at the joint Mathematics, Statistics and Stochastics Colloquium (part of the Colloquium Series of the Department of Mathematics and Statistics) at La Trobe University.
Ever since its popularisation by R.A. Fisher in the early nineteen hundreds, the method of maximum likelihood estimation has grown to become among the most useful and most used point estimation techniques in statistics and allied fields. Under generous regularity conditions, the maximum likelihood estimator for any particular probability model is easy to obtain and has numerous desirable properties such as consistency and efficiency. Unfortunately, there are some very simple probability models for which the maximum likelihood estimator is not so well behaved and where it is not so simple to state. One such model is the single parameter triangular distribution over the unit interval. We shall discuss some curiosities that arise from the maximum likelihood estimation problem for data arising from such distributions and present some recent results and an open question that is left unknown under such scenarios.
About Professor Ivan Corwin:
Ivan Corwin is currently a Professor of Mathematics at Columbia University. His thesis included (in joint work with Amir and Quastel) the exact solution to the Kardar-Parisi-Zhang stochastic PDE. Subsequently, with Borodin, he introduced and developed the theory of Macdonald processes. Along with other collaborators, he has developed the area of Integrable Probability, including the study of stochastic vertex models and the Markov duality approach. He has also worked on discrete approximation theory to stochastic PDEs. Corwin received his Ph.D. from the Courant Institute in 2011 and has since held positions at Microsoft Research, MIT, Institute Henri Poincare, and now Columbia. He was a Clay Research Fellow and is presently a Packard Fellow and a Fellow of the Institute of Mathematical Statistics. He was the recipient of the Alexanderson Award, Rollo Davidson Prize, Young Scientist Prize of the IUPAP, and gave an invited lecture at the 2014 ICM.