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This book is a comprehensive guide to simulation methods with explicit recommendations of methods and algorithms. It covers both the technical aspects of the subject, such as the generation of random numbers, non-uniform random variates and stochastic processes, and the use of simulation.

During the last decades long-memory processes have evolved as a vital and important part of time series analysis. This book attempts to give an overview of the theory and methods developed to deal with long-range dependent data as well as describe some applications of these methodologies to real-life time series.

The data analytic methods routinely applied in the analysis of recidivism data in criminology have been expanded to include immune individuals. This book describes the theory and methods of incorporating them.

Incorporates the many tools needed for modeling and pricing in finance and insurance Introductory Stochastic Analysis for Finance and Insurance introduces readers to the topics needed to master and use basic stochastic analysis techniques for mathematical finance.

With the growth of such fields as financial economics, so has the need for a thorough discussion of statistical size distributions.

This broadly based graduate--level textbook covers the major models and statistical tools currently used in the practice of econometrics. It examines the classical, the decision theory, and the Bayesian approaches, and contains material on single equation and simultaneous equation econometric models.

Inference and Prediction in Large Dimensions offers a predominantly theoretical coverage of statistical prediction, with some potential applications discussed, when data and/or parameters belong to a large or infinite dimensional space.

This book introduces the general philosophy of a number of unique topics, including response surface methodology and details least squares for response surface work; factorial designs at two levels; fitting second-order models; adequacy of estimation and the use of transformation; and occurrence and elucidation of ridge systems.

A balanced presentation of the theoretical, practical, and computational aspects of nonlinear regression. This book provides background material on linear regression, including a geometrical development for linear and nonlinear least squares.

In this volume, Srivastava examines both random variables that can be quantitatively measured as well as the latest multivariate methods. Most of the methods presented assume that the data has a normal distribution and that there are no outliers.

Meta Analysis: A Guide to Calibrating and Combining Statistical Evidence acts as a source of basic methods for scientists wanting to combine evidence from different experiments. The authors aim to promote a deeper understanding of the notion of statistical evidence. The book is comprised of two parts - The Handbook, and The Theory.

This book provides an introduction to the bootstrap for readers who have professional interest in its methods but do not have a background in advanced mathematics. It offers reliable, authoritative coverage of the bootstrap's considerable advantages as well as its drawbacks.

An expert introduction to stage-wise adaptive designs in all areas of statistics Stage-Wise Adaptive Designs presents the theory and methodology of stage-wise adaptive design across various areas of study within the field of statistics, from sampling surveys and time series analysis to generalized linear models and decision theory.

Praise for the First Edition "This impressive and eminently readable text... [is] a welcome addition to the statistical literature. " -The Indian Journal of Statistics Revised to reflect the current developments on the topic, Linear Statistical Models, Second Edition provides an up-to-date approach to various statistical model concepts.

Offers a rich collection of techniques. Discusses the foundational aspects and modern day practice. Accessible to anyone with knowledge of advanced calculus. Provides a unified framework to discuss the many different perspectives. Includes numerous practical applications in biostatistics, computer science, engineering and economics. .

The Construction of Optimal Stated Choice Experiments provides an accessible introduction to the construction methods needed to create the best possible designs for use in modeling decision-making. It uniquely covers disciplines from marketing to transportation, environmental resource economics to public welfare analysis.

Bayesian methods combine information available from data with any prior information available from expert knowledge. The Bayes linear approach follows this path, offering a quantitative structure for expressing beliefs, and systematic methods for adjusting these beliefs, given observational data.

This book covers the most common methods for analyzing single, double, three-way and multi-way data. Geared towards applications and the decisions that have to be made to get meaningful analyses, it presents a variety of models illustrated using commercially available software.

This book is a practical guide for experimenters who are faced with selecting optimal treatments based on empirical studies.

Reliability & Risk: A Bayesian Perspective addresses the need for a sound introduction to the mathematical and statistical aspects of reliability analysis from a Bayesian perspective. It features many real examples, taken from the author's vast experience, and lots of applications from reliability engineering.

This book explores data management from study development to final analysis and suggests alternative approaches, with guidelines on optimal approaches under various circumstances. It contains discussions of the various approaches to clinical trials for some of the major diseases. This second edition compares approaches in the U.S.

Mathematical models are used to simulate complex real-world phenomena in many areas of science and technology. Large complex models typically require inputs whose values are not known with certainty.

A well-balanced introduction to probability theory and mathematical statistics Featuring updated material, An Introduction to Probability and Statistics, Third Edition remains a solid overview to probability theory and mathematical statistics.

The purpose of this book is to introduce the theory of U-Statistics and illustrate it with a wide range of timely applications arising in genetics, biomedical, and psychological research.

Not even the most brilliant statistician can instantly recall every rule and concept that forms the daily bread of statistical work. Sensibly organized for quick reference, Statistical Rules of Thumb, Second Edition compiles simple rules that are widely applicable, robust, and elegant, and that capture key statistical concepts.

Fuzzy logic provides a simple way to arrive at a definite conclusion based upon vague, ambiguous, imprecise, noisy, or missing input information. Statistical Methods for Fuzzy Data deftly explains the basics of fuzzy logic and the use of statistical methods for fuzzy data sets.

* First book on robust techniques to be specifically aimed at biostatistics. * Supported by an accompanying website containing data sets, programs written in R and a user guide.

Multivariable regression models are of fundamental importance in all areas of science in which empirical data must be analyzed. This book proposes a systematic approach to building such models based on standard principles of statistical modeling.

This book fills this gap, providing a comprehensive, self-contained introduction to regression modeling used in the analysis of time-to-event data in epidemiological, biostatistical, and other health-related research.