Böcker i Wiley Series in Probability and Statistics-serien

Takes a look at the application of random graphs to pattern recognition. This work presents examples of applications of the graphs studied, as well as the theoretical treatment of their properties; and a compilation of topics in discrete mathematics, pattern recognition, and machine learning.

An accessible introduction to the use of regression analysis in the social sciences Regression with Social Data: Modeling Continuous and Limited Response Variables represents the most complete and fully integrated coverage of regression modeling currently available for graduate-level behavioral science students and practitioners.

Expert practical and theoretical coverage of runs and scans This volume presents both theoretical and applied aspects of runs and scans, and illustrates their important role in reliability analysis through various applications from science and engineering.

This text uses simulation as a computational aid in dealing with and creating models of reality. Its main objective is to incorporate simulation as an integral part of the interaction between data and the models which may have approximately generated them.

In this volume, influential statistician Christopher Lloyd presents a comprehensive, self-contained discussion of the special statistical methods used to analyse and report count data, also known as categorical data. The emphasis here is on modern methods, with some attention paid to traditional methods as well.

Fractional factorial plans are of immense practical utility in many fields of investigation (particularly in experimental design, quality control, and quality improvment), and research in this area is progressing at a vigorous pace with an already voluminous and growing amount of research papers published.

The importance of nonparametric methods in modern statistics has grown dramatically since their inception in the mid-1930s. Requiring few or no assumptions about the populations from which data are obtained, they have emerged as the preferred methodology among statisticians and researchers performing data analysis.

Uniquely combining theory, application, and computing, this book explores the spectral approach to time series analysis The use of periodically correlated (or cyclostationary) processes has become increasingly popular in a range of research areas such as meteorology, climate, communications, economics, and machine diagnostics.

A unique, practical guide for industry professionals who need to improve product quality and reliability in repairable systems Owing to its vital role in product quality, reliability has been intensely studied in recent decades. Most of this research, however, addresses systems that are nonrepairable and therefore discarded upon failure.

Considers neoclassical models in light of results that can go wrong with them to bring about better models. This work offers an examination of the LTCM collapse.

Includes a chapter on multiple linear regression in biomedical research, with sections containing the multiple linear regressions model and least squares; the ANOVA table, parameter estimates, and confidence intervals; partial f-tests; polynomial regression; and analysis of covariance.

This text provides a mathematical foundation for prediction theory and time series analysis using the geometry of Hilbert spaces. Emphasis is on foundation and structure, supported by theory, application and exercises to provide reinforcement and to extend discussions.

This third edition of the successful Elements of Applied Stochastic Processes improves on the last edition by condensing the material and organizing it into a more teachable format. With more in-depth coverage of Markov chains and simple Markov process, the authors provide added emphasis to statistical inference in stochastic processes.

This is a revision of the classic book Applied Discriminant Analysis by Carl Huberty. Dr. Huberty has taken on a co-author, Steve Olejnik, who helped to update existing material and write new chapters. New terms, updated discussion of topics that have recently become more important, new computer applications, and new references have been included.

Environmental Statistics provides a broad overview of the statistical methodology used in the study of the environment, written in an accessible style by a leading authority on the subject. * Provides broad coverage of the methodology used in the statistical investigation of environmental issues.

This work covers stochastic order relations, which provide insight into the behaviour of complex stochastic (random) systems and enables the user to collect comparative data. Application areas include queuing systems, actuarial and financial risk, decision making, and stochastic simulation.

A system for statistical computing and dynamic graphics based on the LISP language is described in this book, which shows how to use the system for statistical calculations and graphs. No prior knowledge of LISP is assumed, and examples are included.

Explores the application of bootstrap to problems that place unusual demands on the method. The bootstrap method, introduced by Bradley Efron in 1973, is a nonparametric technique for inferring the distribution of a statistic derived from a sample.

This study deals with the calculations of mathematical expectations, primarily by simulation methods. The authors explore the present state of research and signal the types of problems raised by new methods. Topics discussed include Monte Carlo methods and the simulation of stochastic processes.

This second edition covers new developments in the analysis of statistical time series since the 1st edition was published in 1976. There is a considerable expansion of material, including added discussion of central limit theorems, estimation and generalized least squares.

Combines recent developments in resampling technology (including the bootstrap) with new methods for multiple testing that are easy to use, convenient to report and widely applicable. Software from SAS Institute is available to execute many of the methods and programming is straightforward for other applications.

The first step-by-step guide to conducting successful Chi-squared tests Chi-squared testing is one of the most commonly applied statistical techniques. It provides reliable answers for researchers in a wide range of fields, including engineering, manufacturing, finance, agriculture, and medicine.

Theory of Preliminary Test and Stein-Type Estimation with Applications provides a com-prehensive account of the theory and methods of estimation in a variety of standard models used in applied statistical inference.

Of related interest. Nonlinear Regression Analysis and its Applications Douglas M. Bates and Donald G. Watts ". an extraordinary presentation of concepts and methods concerning the use and analysis of nonlinear regression models. highly recommend[ed].

The purpose of this work is to provide a unified treatment of both the theory and practice of factor analysis and latent variables models.

Developing new mathematical tools to study discrete event systems has been a major focus in the fields of systems theory and operations research. This study discusses the two lines of investigation in DES research that have emerged: logical/qualitative issues and temporal/quantitative analysis.

Explains how Hilbert space techniques cross the boundaries into the foundations of probability and statistics. Focuses on the theory of martingales stochastic integration, interpolation and density estimation. Includes a copious amount of problems and examples.

This study presents the concepts and practice of interpreting single equation dynamic regression models. Emphasis is placed on possible dynamic patterns, such as distributed lag responses of the output series to the input series and the auto-correlation patterns of the regression disturbance.

Introduces the basic principles and ideas of MACSYMA, a computer programming system designed to perform mathematical computations and manipulations in symbolic as well as numerical form.

In this well written book, the authors treat the fundamental question of response-adaptive randomization as dealing with the trade-off between minimizing the expected number of treatment failures and maximizing the power of inferential tests.