Robust Rank-Based and Nonparametric Methods

1 Rank-Based Analysis of Linear Models and Beyond: A Review.- 2 Robust Signed-Rank Variable Selection in Linear Regression.- 3 Generalized Rank-Based Estimates for Linear Models with Cluster Correlated Data.- 4 Iterated Reweighted Rank-Based Estimates for GEE Models.- 5 On the Asymptotic Distribution of a Weighted Least Absolute Deviation Estimate for a Bifurcating Autoregressive Process.- 6 Applications of Robust Regression to "Big" Data Problems.- 7 Rank-Based Inference for Multivariate Data in Factorial Designs.- 8 Two-Sample Rank-Sum Test for Order Restricted Randomized Designs.- 9 On a Partially Sequential Ranked Set Sampling Paradigm.- 10 A New Scale-Invariant Nonparametric Test for Two-Sample Bivariate Location Problem with Application.- 11 Influence Functions and Efficiencies of k-Step Hettmansperger-Randles Estimators for Multivariate Location and Regression.- 12 New Nonparametric Tests for Comparing Multivariate Scales Using Data Depth.- 13 Multivariate Autoregressive Time Series Using Schweppe Weighted Wilcoxon Estimates.- 14 Median Stable Distributions.- 15 Confidence Intervals for Mean Difference between Two Delta-distributions.

Dr. Regina Liu is currently Distinguished Professor of Statistics at Rutgers University, USA. She received her Ph.D. from Columbia University at New York. She has published extensively in a broad range of research areas, including nonparametric statistics, data depth, robust statistics, resampling techniques, text mining, fusion learning, statistical quality control, and aviation risk management. She has served on the editorial board of several statistical journals, including The Annals of Statistics, Journal of American Statistical Association, and Journal of Multivariate Analysis. She is the recipient of the 2011 Stieltjes Professor, Thomas Stieltjes Institute for Mathematics, the Netherlands. She has been elected fellow of American Statistical Association, Institute of Mathematical Statistics, and International Statistical Institute.

Dr. Joseph McKean is Professor of Statistics at Western Michigan University. He received hisPhD in Statistics in 1975 from the Pennsylvania State University under the direction of Professor T.P. Hettmansperger. He has held several visiting research professorships at University of New South Wales. In 1999, he was elected as a fellow of the American Statistical Association. In 1994, he received the Distinguished Faculty Scholar Award from Western Michigan University. He served as Chair of the Nonparametric Section of the American Statistical Association during 2002. Dr. McKean has served on the editorial board of several statistical journals, including the Journal of the American Statistical Association, the Journal of Statistical Computation and Simulation, and the Journal of Nonparametric Statistics.
Dr. McKean has published extensively on robust rank-based procedures for linear models. These include papers on the theory for robust estimation and testing, the geometry of robust procedures, and the small sample properties of robust inference. He has worked with general robust estimates, bounded inuence estimates, and