Books in the Statistics: A Series of Textbooks and Monographs series

Presents the rigorous theory of probability and statistical inference in the style of a tutorial, using worked examples, exercises, numerous figures and tables, and computer simulations to develop and illustrate concepts. This book covers various topics typically addressed in a two-semester course in probability and statistical inference.

This book focuses on statistical methods which impinge more or less directly on the decisions that are made during the course of pharmaceutical and agro-chemical research, considering the four decision-making areas.

Discusses model discrimination based upon incorrect selection probability. This book presents diagnostic statistics and formal hypothesis test procedures to assess a model's fit and stability. It explains the use of computer computations such as the jackknife and bootstrap, and demon.

Offers experts and advanced students with a review of the status of the evolved theory of U-statistics.

This book presents ideas and procedures for regression analysis of survival data in cancer chemotherapy based on regression-based approaches for improvements in the quality of care through the effective location of optimal treatment levels. It is useful for cancer chemotherapists.

Describes the randomization test theory, hypotheses, and the role of random assignment. This work also features material on N-of-1 randomization tests and includes randomization test programs and FORTRAN codes.

A collection of proofs of fundamental theorems. This book covers such areas as estimations and testing in linear regression models under various sets of assumptions, and estimation and testing in simultaneous equations models.

This book presents the basic theory of linear models from a Bayesian viewpoint. It is unique in that time series models such as autoregressive moving average processes are treated as linear models in the same way the general linear model is examined.

This book delineates the history of Lp-norm estimation and examines the nonlinear Lp-norm estimation problem that is a viable alternative to least squares estimation problems. It is intended for both statisticians and applied mathematicians.

This book provides an overview of the commonly used statistical methodology. It is intended to enable professionals such as medical doctors, engineers, business executives, laboratory technicians, school teachers, and others to understand the basics of statistical thought through self study.

This volume introduces the theoretical ideas in probability and statistics by means of examples. The strengths of the BASIC computer language are exploited to illustrate probabilistic and statistical ideas. Topics described by the Committee on the Under-graduate Program in Mathematics are included.

Examines theoretical issues, as well as practical developments in statistical inference related to econometric models and analysis. This work offers discussions on such areas as the function of statistics in aggregation, income inequality, poverty, health, spatial econometrics, panel and survey data, bootstrapping and time series.

Presents classical problems of statistical inference, such as estimation and hypothesis testing, on the basis of measures of entropy and divergence with applications to multinomial and generation populations. On the basis of divergence measures, this book introduces minimum divergence estimators as well as divergence test statistics.

"The publication of this book, I believe, is a milestone. . .Kennedy and Gentle have done an outstanding job of assembling the best techniques from a great variety of sources, establishing a benchmark for the field of statistical computing." ---Mathematics of Computation ." . .a very impressive text. . .highly readable and well illustrated with examples. . . .the reader who intends to take a hand in designing his own regression and multivariate packages will find a storehouse of information." ---Journal of the American Statistical Association ." . .a valuable addition to the literature on statistical computing." ---Mathematical Reviews

Students with a background that is less focused on mathematical theory but more applied in their field have a difficult time with the theoretical gravity of a course in mathematical statistics. This work provides an understanding in the prerequisite material along with a link to the applications of the theory to the student's area of interest.

Describes the developed IRT models and furnishes explanations of algorithms that can be used to estimate the item or ability parameters under various IRT models. This work discusses parameter estimation with multiple groups, parameter estimation for a test with mixed item types, and Markov chain Monte Carlo methods.

A guide to testing statistical hypotheses for readers familiar with the Neyman-Pearson theory of hypothesis testing including the notion of power, the general linear hypothesis (multiple regression) problem, and the special case of analysis of variance.

Presents a selection of articles presented at the Eighth Lukacs Symposium held at the Bowling Green State University, Ohio. This title discusses consistency and accuracy of the sequential bootstrap, hypothesis testing, geometry in multivariate analysis, the classical extreme value model, the analysis of cross-classified data, and econometrics.

This book provides a comprehensive study of the bivariate discrete distributions and details the computer simulation techniques for the distributions. It develops distributions using sampling schemes, explores the role of compounding, and covers Waring distribution for use in accident theory.

This monograph is a compilation of research on the inverse Gaussian distribution. It emphasizes the presentation of the statistical properties, methods, and applications of the two-parameter inverse Gaussian family of distribution. It is useful to statisticians and users of statistical distribution.

Offers a treatment of different kinds of James-Stein and ridge regression estimators from a frequentist and Bayesian point of view. This book explains and compares estimators analytically as well as numerically and includes Mathematica and Maple programs used in numerical comparison.

A handbook of statistical analysis for use in the pharmaceutical industry. It covers areas such as: bioavailability, repeated-measures designs, dose-response, population models, multicenter trials, handling dropouts, survival analysis, and, robust data analysis.

This book develops appreciation of the ingenuity involved in the mathematical treatment of random phenomena, and of the power of the mathematical methods employed in the solution of applied problems. It is intended to students interested in applications of probability to their disciplines.

A textbook for a methods course or reference for an experimenter who is mainly interested in data analyses rather than in the mathematical development of the procedures. It provides the useful statistical techniques, not only for the normal distribution, but for other important distributions.

Details the fundamentals of applied statistics and experimental design. This book presents a unified approach to data handling that emphasizes the analysis of variance, regression analysis and the use of Statistical Analysis System computer programs.

Presents techniques for designing experiments that yield adequate and reliable measurements of one or several responses of interest, fitting and testing the suitability of empirical models used for acquiring information from the experiments, and for utilizing the experimental results to make decisions concerning the system under investigation.

Offers a comprehensive presentation of scientific sampling principles and shows how to design a sample survey and analyze the resulting data. This title demonstrates the validity of theorems and statements without resorting to detailed proofs.

Presents an account of popular approaches to nonparametric regression smoothing. This book discusses boundary corrections for trigonometric series estimators; asymptotics for polynomial regression; testing goodness-of-fit; estimation in partially linear models; and practical aspects, problems and methods for confidence intervals and bands.

Beginning with the historical background of probability theory, this text examines various important aspects of mathematical probability - including random variables, probability distributions, characteristic and generating functions, stochastic convergence, and limit theorems. It provides an introduction to various types of statistical problems.