Robust Interference

About this book

This reference work covers the many aspects of Robust Inference. Much of what is contained in the chapters, written by leading experts in the field, has not been part of previous surveys of this area. Robust Inference has been an active area of research for the last two decades. Especially during recent years it has been extended in different directions covering a wide variety of models. This volume will be valuable for both graduate students and researchers using statistical methods.

Contents

Distance Methods. Robust inference in multivariate linear regression using difference of two convex functions as the discrepancy measure (Z.D. Bai, C.R. Rao, Y.H. Wu). Minimum distance estimation: The approach using density-based distances (A. Basu, I.R. Harris, S. Basu). Methods Based on Influence Functions. Robust inference: The approach based on influence functions (M. Markatou, E. Ronchetti). Practical applications of bounded-influence tests (S. Heritier, M.-P. Victoria-Feser). Outliers and High Breakdown Methods. Introduction to positive-breakdown methods (P.J. Rousseeuw). Outlier indentification and robust methods (U. Gather, C. Becker). Methods Based on Ranks. Rank-based analysis of linear models (T.P. Hettmansperger, J.W. McKean, S.J. Sheather). Rank tests for linear models (R. Koenker). Some extensions in the robust estimation of parameters of exponential and double exponential distributions in the presence of multiple outliers (A. Childs, N. Balakrishnan). Time Series Problems. Outliers, unit roots and robust estimation of nonstationary time series (G.S. Maddala, Y. Yin). Autocorrelation-robust inference (P.M. Robinson, C. Velasco). A practitioner's guide to robust covariance matrix estimation (W.J. den Haan, A. Levin). Panel Data, Censored Data, and Contaminated Data. Approaches to the robust estimation of mixed models (A.H. Welsh, A.M. Richardson). Nonparametric maximum likelihood methods (S.R. Cosslett). A guide to censored quantile regressions (B. Fitzenberger). What can be learned about population parameters when the data are contaminated (J.L. Horowitz, C.F. Manski). General Issues. Asymptotic representations and interrelations of robust estimators and their applications (J. Jureckova, P.K. Sen). Small sample asymptotics: Applications in robustness (C.A. Field, M.A. Tingley). On the fundamentals of data robustness (G. Maguluri, K. Singh). Statistical analysis with incomplete data: A selective review (M.G. Akritas, M.P. LaValley). Contamination level and sensitivity of robust tests (J.A. Visek). Finite sample robustness of tests: An overview (T. Kariya, P. Kim). Future directions (G.S. Maddala, C.R. Rao).

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