Outlier Detection and Minimum Distance Applications Maximum likelihood procedures are widely applicable but are highly influenced by the presence of outliers and bad data. In this minisymposium, we survey a range of attacks on this problem. One approach is to modify the influence function in order to improve upon the performance of M-estimators. A second approach is to modify the likelihood and incorporate a Bayesian framework. Finally, the minimum distance approach may be adopted in a semiparametric (eg. mixture of normals model) or nonparametric (penalized minimum distance) framework. Modern data analysis and data mining requires a range of outlier detection and flexible modeling strategies. Bill Szewczyk From Kernels to Mixtures: Modeling High-Dimensional Data David Rocke Outlier Detection With Massive Data Sets George Terrell Penalized Minimum Distance Modeling for Data Analysis Will Wojciechowski A Bayesian MCMC Multivariate Outlier Detection Algorithm