Pranab Kumar Sen

Mervyn J. Silvapulle

An up-to-date approach to understanding statistical inference Statistical inference is finding useful applications in numerousfields, from sociology and econometrics to biostatistics. Thisvolume enables professionals in these and related fields to masterthe concepts of statistical inference under inequality constraintsand to apply the theory to problems in a variety of areas. Constrained Statistical Inference: Order, Inequality, and ShapeConstraints provides a unified and up-to-date treatment of themethodology. It clearly illustrates concepts with practicalexamples from a variety of fields, focusing on sociology,econometrics, and biostatistics. The authors also discuss a broad range of otherinequality-constrained inference problems that do not fit well inthe contemplated unified framework, providing a meaningful way forreaders to comprehend methodological resolutions. Chapter coverage includes: Population means and isotonic regression Inequality-constrained tests on normal means Tests in general parametric models Likelihood and alternatives Analysis of categorical data Inference on monotone density function, unimodal densityfunction, shape constraints, and DMRL functions Bayesian perspectives, including Stein’s Paradox,shrinkage estimation, and decision theory

Order, Inequality, and Shape Constraints

Constrained Statistical Inference

Reference and Truth

Nitis Mukhopadhyay

Malay Ghosh

The only comprehensive guide to the theory and practice of one oftoday's most important probabilistic techniques The past 15 years have witnessed many significant advances insequential estimation, especially in the areas of three-stage andnonparametric methodology. Yet, until now, there were no referencesdevoted exclusively to this rapidly growing statisticalfield. Sequential Estimation is the first, single-source guide to thetheory and practice of both classical and modern sequentialestimation techniques--including parametric and nonparametricmethods. Researchers in sequential analysis will appreciate theunified, logically integrated treatment of the subject, as well ascoverage of important contemporary procedures not covered in moregeneral sequential analysis texts, such as: * Shrinkage estimation * Empirical and hierarchical Bayes procedures * Multistage sampling and accelerated sampling procedures * Time-sequential estimation * Sequential estimation in finite population sampling * Reliability estimation and capture-recapture methodologiesleading to sequential tagging schemes An indispensable resource for researchers in sequential analysis,Sequential Estimation is an ideal graduate-level text as well.

Sequential Estimation

A study of sequential nonparametric methods emphasizing the unified Martingale approach to the theory, with a detailed explanation of major applications including problems arising in clinical trials, life-testing experimentation, survival analysis, classical sequential analysis and other areas of applied statistics and biostatistics.

Theory and Applications of Sequential Nonparametrics

Essays in Philosophical Analysis

Logic, Induction, and Ontology

Jana Jureckova

Jana Jurecková

Jana Jure&ccaron Kova

A broad and unified methodology for robust statistics—with exciting new applications Robust statistics is one of the fastest growing fields in contemporary statistics. It is also one of the more diverse and sometimes confounding areas, given the many different assessments and interpretations of robustness by theoretical and applied statisticians. This innovative book unifies the many varied, yet related, concepts of robust statistics under a sound theoretical modulation. It seamlessly integrates asymptotics and interrelations, and provides statisticians with an effective system for dealing with the interrelations between the various classes of procedures. Drawing on the expertise of researchers from around the world, and covering over a decade's worth of developments in the field, Robust Statistical Procedures: Asymptotics and Interrelations: Discusses both theory and applications in its two parts, from the fundamentals to robust statistical inference Thoroughly explores the interrelations between diverse classes of procedures, unlike any other book Compares nonparametric procedures with robust statistics, explaining in detail asymptotic representations for various estimators Provides a timesaving list of mathematical tools for the problems under discussion Keeps mathematical abstractions to a minimum, in spite of its largely theoretical content Includes useful problems and exercises at the end of each chapter Offers strategies for more complex models when using robust statistical procedures Self-contained and rounded in approach, this book is invaluable for both applied statisticians and theoretical researchers; for graduate students in mathematical statistics; and for anyone interested in the influence of this methodology.

Asymptotics and Interrelations

Robust Statistical Procedures

A. K. Md. Ehsanes Saleh

A Collection of Three Papers on the Robust Estimation of Location Parameter (nonparametrics).

Edward J. Dudewicz

Detlef Plachky

Recent Results in Estimation Theory and Related Topics

Pranab Kumar Sen

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