In this volume of the Handbook of Statistics with the primary focus on bioenvironmental and public health statistics, a rather off-beat appr oach has been taken, wherein biostatistical methods that are relevant to the dissemination of bioenvironmental and public health investigati ons have been thoroughly emphasised, and placed side by side with the fruitful applications. One aspect of statistical methodology that mer its special appraisal is the extent of appropriateness of some standar d statistical tools in such non-standard applications, and much of the deliberation in this volume is geared to alternative non-standard and application oriented methodology that have been developed to suit bet ter bioenvironmental and public health studies.

Handbook of Statistics 18: Bioenvironmental and Public Health Statistics

Science & Math

Sen, Pranab Kumar

Biological Sciences

North-Holland

An up-to-date approach to understanding statistical inference 
Statistical inference is finding useful applications in numerous fields, from sociology and econometrics to biostatistics. This volume enables professionals in these and related fields to master the concepts of statistical inference under inequality constraints and to apply the theory to problems in a variety of areas. Constrained Statistical Inference: Order, Inequality, and Shape Constraints provides a unified and up-to-date treatment of the methodology. It clearly illustrates concepts with practical examples from a variety of fields, focusing on sociology, econometrics, and biostatistics. The authors also discuss a broad range of other inequality-constrained inference problems that do not fit well in the contemplated unified framework, providing a meaningful way for readers to comprehend methodological resolutions. Chapter coverage includes: <ul>
<li>Population means and isotonic regression</li> <li>Inequality-constrained tests on normal means</li> <li>Tests in general parametric models</li> <li>Likelihood and alternatives</li> <li>Analysis of categorical data</li> <li>Inference on monotone density function, unimodal density function, shape constraints, and DMRL functions</li> <li>Bayesian perspectives, including Stein’s Paradox, shrinkage estimation, and decision theory</li> </ul>

Constrained Statistical Inference: Order, Inequality, and Shape Constraints

Silvapulle, Mervyn J.

Mathematics

Wiley-Interscience

xi 440p blue cloth, nice fresh copy with library stamps to edges and library shelfmark, never much used, very good condition

Nonparametric Methods in Multivariate Analysis (Wiley Series in Probability and Statistics)

Puri, Madan Lal

wiley

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

Mukhopadhyay, Nitis

Ghosh, Malay

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.

Robust Statistical Procedures: Asymptotics and Interrelations

Jurečková, Jana