2013 Fall

Date: 2013-09-20 Time: 15:30-16:30 Location: BURN 1205 Abstract: In this talk, a new and consistent statistic is proposed to test whether two discrete random variables are independent. The test is based on a statistic of the Cramér–von Mises type constructed from the so-called empirical checkerboard copula. The test can be used even for sparse contingency tables or tables whose dimension changes with the sample size. Because the limiting distribution of the test statistic is not tractable, a valid bootstrap procedure for the computation of p-values will be discussed.

Date: 2013-09-13 Time: 15:30-16:30 Location: BURN 1205 Abstract: A new density estimation method in a Bayesian nonparametric framework is presented when recorded data are not coming directly from the distribution of interest, but from a length biased version. From a Bayesian perspective, efforts to computationally evaluate posterior quantities conditionally on length biased data were hindered by the inability to circumvent the problem of a normalizing constant. In this talk a novel Bayesian nonparametric approach to the length bias sampling problem is presented which circumvents the issue of the normalizing constant.