65.Survey Errors and Survey Costs

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Survey Errors and Survey Costs (Wiley Series in Probability and Statistics)
By Robert M. Groves
Publisher:   Wiley-Interscience
Number Of Pages:   620
Publication Date:   1989-07
ISBN-10 / ASIN:   0471611719
ISBN-13 / EAN:   9780471611714
Binding:   Hardcover
Book Description:
Presents a comprehensive treatment of survey errors and the costs and benefits of alternative survey designs, integrating statistical and social science perspectives on survey design. Considers coverage error, nonresponse error, sampling error, and measurement error, including the effects of interviewers and respondents, the wording of the questionnaire, and mode of data collection. A review of the social science and statistical literatures on survey errors is included, and the relationships between the different types of errors are explored. Presents cost models designed to reduce the various types of errors.

66.Statistical Advances in the Biomedical Sciences: Clinical Trials, Epidemiology, ....

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Statistical Advances in the Biomedical Sciences: Clinical Trials, Epidemiology, Survival Analysis, and Bioinformatics (Wiley Series in Probability and Statistics)
By Atanu Biswas, Sujay Datta, Jason P. Fine, Mark R. Segal
Publisher: Wiley-Interscience
Number Of Pages: 582
Publication Date: 2008-01-02
ISBN-10 / ASIN: 0471947539
ISBN-13 / EAN: 9780471947530
Binding: Hardcover
Book Description:
This book compiles 30 carefully prepared and peer-reviewed articles form eminent researchers in the modern-day biomedical sciences. With sections on Clinical Trials, Epidemiology, Survival Analysis, and Bioinformatics, it provides more specific details on concepts, methods, and algorithms.
Provides a wide view of contemporary methodological developments and technical innovations with a focus on applications. A related website offers datasets used or referred to in the book and suggests links to the commercial or freely downloadable software packages referred to in the various chapters.

67.Latent Curve Models: A Structural Equation Perspective

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Latent Curve Models: A Structural Equation Perspective (Wiley Series in Probability and Statistics)
By Kenneth A. Bollen, Patrick J. Curran
Publisher:   Wiley-Interscience
Number Of Pages:   312
Publication Date:   2005-12-23
ISBN-10 / ASIN:   047145592X
ISBN-13 / EAN:   9780471455929
Binding:   Hardcover
Product Description:
This volume represents a comprehensive treatment of a model sometimes referred to as latent curve or growth curve models. Latent Curve Models analyzes LTMs from the perspective of structural equation modeling (SEM) with latent variables. Although the authors discuss simple regression-based procedures that are helpful in the early stages of LTM, most of the presentation will use SEMs as a driving tool throughout the text.

68.Regression Diagnostics: Identifying Influential Data and Sources of Collinearity

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Regression Diagnostics : Identifying Influential Data and Sources of Collinearity (Wiley Series in Probability and Statistics)
size: approx. 12.5 MB
Provides practicing statisticians and econometricians with new tools for assessing quality and reliability of regression estimates. Diagnostic techniques are developed that aid in the systematic location of data points that are unusual or inordinately influential, and measure the presence and intensity of collinear relations among the regression data and help to identify variables involved in each and pinpoint estimated coefficients potentially most adversely affected. Emphasizes diagnostics and includes suggestions for remedial action.

69.Reliability and Risk: A Bayesian Perspective

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Reliability and Risk: A Bayesian Perspective (Wiley Series in Probability and Statistics)
By Nozer D. Singpurwalla
Publisher: Wiley
Number Of Pages: 396
Publication Date: 2006-10-06
Sales Rank: 540916
ISBN / ASIN: 0470855029
EAN: 9780470855027
Binding: Hardcover
Book Description:
We all like to know how reliable and how risky certain situations are, and our increasing reliance on technology has led to the need for more precise assessments than ever before. Such precision has resulted in efforts both to sharpen the notions of risk and reliability, and to quantify them. Quantification is required for normative decision-making, especially decisions pertaining to our safety and wellbeing. Increasingly in recent years Bayesian methods have become key to such quantifications.
Reliability and Risk provides a comprehensive overview of the mathematical and statistical aspects of risk and reliability analysis, from a Bayesian perspective. This book sets out to change the way in which we think about reliability and survival analysis by casting them in the broader context of decision-making. This is achieved by:
Providing a broad coverage of the diverse aspects of reliability, including: multivariate failure models, dynamic reliability, event history analysis, non-parametric Bayes, competing risks, co-operative and competing systems, and signature analysis.
Covering the essentials of Bayesian statistics and exchangeability, enabling readers who are unfamiliar with Bayesian inference to benefit from the book.
Introducing the notion of “composite reliability”, or the collective reliability of a population of items.
Discussing the relationship between notions of reliability and survival analysis and econometrics and financial risk.
Reliability and Risk can most profitably be used by practitioners and research workers in reliability and survivability as a source of information, reference, and open problems. It can also form the basis of a graduate level course in reliability and risk analysis for students in statistics, biostatistics, engineering (industrial, nuclear, systems), operations research, and other mathematically oriented scientists, wherein the instructor could supplement the material with examples and problems.

70.Environmental Statistics

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Environmental Statistics (Wiley Series in Probability and Statistics)
By Vic Barnett
Publisher:   John Wiley & Sons
Number Of Pages:   316
Publication Date:   2004-01-26
Sales Rank:   351125
ISBN / ASIN:   0471489719
EAN:   9780471489719
Binding:   Hardcover
Book Description:
In modern society, we are ever more aware of the environmental issues we face, whether these relate to global warming, depletion of rivers and oceans, despoliation of forests, pollution of land, poor air quality, environmental health issues, etc. At the most fundamental level it is necessary to monitor what is happening in the environment – collecting data to describe the changing scene. More importantly, it is crucial to formally describe the environment with sound and validated models, and to analyse and interpret the data we obtain in order to take action.
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. It serves as both a textbook for students of environmental statistics, as well as a comprehensive source of reference for anyone working in statistical investigation of environmental issues.
Provides broad coverage of the methodology used in the statistical investigation of environmental issues.
Covers a wide range of key topics, including sampling, methods for extreme data, outliers and robustness, relationship models and methods, time series, spatial analysis, and environmental standards.
Includes many detailed practical and worked examples that illustrate the applications of statistical methods in environmental issues.
Authored by a leading authority on environmental statistics.

71.Bayes Linear Statistics, Theory & Methods

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By Michael Goldstein, David Wooff
Publisher: Wiley
Number Of Pages: 536
Publication Date: 2007-06-15
Sales Rank: 896924
ISBN / ASIN: 0470015624
EAN: 9780470015629
Binding: Hardcover
Book Description:
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. The methodology differs from the full Bayesian methodology in that it establishes simpler approaches to belief specification and analysis based around expectation judgements. Bayes Linear Statistics presents an authoritative account of this approach, explaining the foundations, theory, methodology, and practicalities of this important field.
The text provides a thorough coverage of Bayes linear analysis, from the development of the basic language to the collection of algebraic results needed for efficient implementation, with detailed practical examples.
The book covers:
The importance of partial prior specifications for complex problems where it is difficult to supply a meaningful full prior probability specification.
Simple ways to use partial prior specifications to adjust beliefs, given observations.
Interpretative and diagnostic tools to display the implications of collections of belief statements, and to make stringent comparisons between expected and actual observations.
General approaches to statistical modelling based upon partial exchangeability judgements.
Bayes linear graphical models to represent and display partial belief specifications, organize computations, and display the results of analyses.
Bayes Linear Statistics is essential reading for all statisticians concerned with the theory and practice of Bayesian methods. There is an accompanying website hosting free software and guides to the calculations within the book.

72.Introductory Stochastic Analysis for Finance and Insurance

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By X. Sheldon Lin, Society of Actuaries
Publisher: Wiley-Interscience
Number Of Pages: 248
Publication Date: 2006-03-17
Sales Rank: 471280
ISBN / ASIN: 0471716421
EAN: 9780471716426
Binding: Hardcover
Book Description:
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. The author presents the theories of stochastic processes and stochastic calculus and provides the necessary tools for modeling and pricing in finance and insurance. Practical in focus, the book's emphasis is on application, intuition, and computation, rather than theory.
Consequently, the text is of interest to graduate students, researchers, and practitioners interested in these areas. While the text is self-contained, an introductory course in probability theory is beneficial to prospective readers.
This book evolved from the author's experience as an instructor and has been thoroughly classroom-tested. Following an introduction, the author sets forth the fundamental information and tools needed by researchers and practitioners working in the financial and insurance industries:
* Overview of Probability Theory
* Discrete-Time stochastic processes
* Continuous-time stochastic processes
* Stochastic calculus: basic topics
The final two chapters, Stochastic Calculus: Advanced Topics and Applications in Insurance, are devoted to more advanced topics. Readers learn the Feynman-Kac formula, the Girsanov's theorem, and complex barrier hitting times distributions. Finally, readers discover how stochastic analysis and principles are applied in practice through two insurance examples: valuation of equity-linked annuities under a stochastic interest rate environment and calculation of reserves for universal life insurance.
Throughout the text, figures and tables are used to help simplify complex theory and pro-cesses. An extensive bibliography opens up additional avenues of research to specialized topics.
Ideal for upper-level undergraduate and graduate students, this text is recommended for one-semester courses in stochastic finance and calculus. It is also recommended as a study guide for professionals taking Causality Actuarial Society (CAS) and Society of Actuaries (SOA) actuarial examinations.

73.Bayesian Models for Categorical Data

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By Peter Congdon
Publisher:   John Wiley & Sons, Inc
Number Of Pages:   425
Publication Date:   2005
Sales Rank:   399574
ISBN / ASIN:   0470092378
EAN:   9780470092378
Binding:   Hardcover
Book Description:
The use of Bayesian methods for the analysis of data has grown substantially in areas as diverse as applied statistics, psychology, economics and medical science. Bayesian Methods for Categorical Data sets out to demystify modern Bayesian methods, making them accessible to students and researchers alike. Emphasizing the use of statistical computing and applied data analysis, this book provides a comprehensive introduction to Bayesian methods of categorical outcomes.
* Reviews recent Bayesian methodology for categorical outcomes (binary, count and multinomial data).
* Considers missing data models techniques and non-standard models (ZIP and negative binomial).
* Evaluates time series and spatio-temporal models for discrete data.
* Features discussion of univariate and multivariate techniques.
* Provides a set of downloadable worked examples with documented WinBUGS code, available from an ftp site.
The author's previous 2 bestselling titles provided a comprehensive introduction to the theory and application of Bayesian models. Bayesian Models for Categorical Data continues to build upon this foundation by developing their application to categorical, or discrete data - one of the most common types of data available. The author's clear and logical approach makes the book accessible to a wide range of students and practitioners, including those dealing with categorical data in medicine, sociology, psychology and epidemiology.

74.Bayesian Statistical Modelling

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By Peter Congdon
Publisher: Wiley
Number Of Pages: 596
Publication Date: 2007-01-17
Sales Rank: 223511
ISBN / ASIN: 0470018755
EAN: 9780470018750
Binding: Hardcover
Book Description:
Bayesian methods combine the evidence from the data at hand with previous quantitative knowledge to analyse practical problems in a wide range of areas. The calculations were previously complex, but it is now possible to routinely apply Bayesian methods due to advances in computing technology and the use of new sampling methods for estimating parameters. Such developments together with the availability of freeware such as WINBUGS and R have facilitated a rapid growth in the use of Bayesian methods, allowing their application in many scientific disciplines, including applied statistics, public health research, medical science, the social sciences and economics.
Following the success of the first edition, this reworked and updated book provides an accessible approach to Bayesian computing and analysis, with an emphasis on the principles of prior selection, identification and the interpretation of real data sets.
The second edition:
Provides an integrated presentation of theory, examples, applications and computer algorithms.
Discusses the role of Markov Chain Monte Carlo methods in computing and estimation.
Includes a wide range of interdisciplinary applications, and a large selection of worked examples from the health and social sciences.
Features a comprehensive range of methodologies and modelling techniques, and examines model fitting in practice using Bayesian principles.
Provides exercises designed to help reinforce the reader’s knowledge and a supplementary website containing data sets and relevant programs.
Bayesian Statistical Modelling is ideal for researchers in applied statistics, medical science, public health and the social sciences, who will benefit greatly from the examples and applications featured. The book will also appeal to graduate students of applied statistics, data analysis and Bayesian methods, and will provide a great source of reference for both researchers and students.

75.Weibull Models

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By D. N. Prabhakar Murthy, Min Xie, Renyan Jiang,
Publisher:   Wiley-Interscience
Number Of Pages:   408
Publication Date:   2003-11-14
Sales Rank:   1177931
ISBN / ASIN:   0471360929
EAN:   9780471360926
Binding:   Hardcover
Book Description:
Get the first book to pull the vast literature on the Weibull into a proper perspective under one cover. Weibull Models
Covers model analysis, parameter estimation, model validation, and application
Classifies the different models and their properties
Intertwines theory and application
Focuses on model identification prior to model parameter estimation

76.Analysis of Financial Time Series

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Publisher: Wiley-Interscience
Number Of Pages: 640
Publication Date: 2005-08-30
Sales Rank: 48165
ISBN / ASIN: 0471690740
EAN: 9780471690740
Binding: Hardcover
Book Description:
Gain the statistical tools and techniques you need to understand today's financial markets with the Second Edition of this critically acclaimed book.
Youll find a comprehensive and systematic introduction to financial econometric models and their applications in modeling and predicting financial time series data. This edition continues to emphasize empirical financial data and focuses on real-world examples. Youll master key aspects of financial time series, including volatility modeling, neural network applications, market microstructure and high-frequency financial data, continuous-time models and Ito's Lemma, Value at Risk, multiple returns analysis, financial factor models, and econometric modeling via computation-intensive methods.
This is an ideal textbook for MBA students and a key reference for researchers and professionals in business and finance. Order your copy today.

77.Linear Model Theory: Univariate, Multivariate, and Mixed Models

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By Keith E. Muller, Paul W. Stewart,
Publisher:   Wiley-Interscience
Number Of Pages:   410
Publication Date:   2006-08-04
Sales Rank:   1071874
ISBN / ASIN:   0471214884
EAN:   9780471214885
Binding:   Hardcover
Book Description:
A precise and accessible presentation of linear model theory, illustrated with data examples
Statisticians often use linear models for data analysis and for developing new statistical methods. Most books on the subject have historically discussed univariate, multivariate, and mixed linear models separately, whereas Linear Model Theory: Univariate, Multivariate, and Mixed Models presents a unified treatment in order to make clear the distinctions among the three classes of models.
Linear Model Theory: Univariate, Multivariate, and Mixed Models begins with six chapters devoted to providing brief and clear mathematical statements of models, procedures, and notation. Data examples motivate and illustrate the models. Chapters 7-10 address distribution theory of multivariate Gaussian variables and quadratic forms. Chapters 11-19 detail methods for estimation, hypothesis testing, and confidence intervals. The final chapters, 20-23, concentrate on choosing a sample size. Substantial sets of excercises of varying difficulty serve instructors for their classes, as well as help students to test their own knowledge.
The reader needs a basic knowledge of statistics, probability, and inference, as well as a solid background in matrix theory and applied univariate linear models from a matrix perspective. Topics covered include:
A review of matrix algebra for linear models
The general linear univariate model
The general linear multivariate model
Generalizations of the multivariate linear model
The linear mixed model
Multivariate distribution theory
Estimation in linear models
Tests in Gaussian linear models
Choosing a sample size in Gaussian linear models
Filling the need for a text that provides the necessary theoretical foundations for applying a wide range of methods in real situations, Linear Model Theory: Univariate, Multivariate, and Mixed Models centers on linear models of interval scale responses with finite second moments. Models with complex predictors, complex responses, or both, motivate the presentation.

78.An Introduction to Categorical Data Analysis

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Publisher:   Wiley-Interscience
Number Of Pages:   372
Publication Date:   2007-03-23
Sales Rank:   167881
ISBN / ASIN:   0471226181
EAN:   9780471226185
Binding:   Hardcover
Book Description:
The use of statistical methods for categorical data is ever increasing in today's world. An Introduction to Categorical Data Analysis, Second Edition provides an applied introduction to the most important methods for analyzing categorical data. This new edition summarizes methods that have long played a prominent role in data analysis, such as chi-squared tests, and also places special emphasis on logistic regression and other modeling techniques for univariate and correlated multivariate categorical responses.
This Second Edition features:
Two new chapters on the methods for clustered data, with an emphasis on generalized estimating equations (GEE) and random effects models
A unified perspective based on generalized linear models
An emphasis on logistic regression modeling
An appendix that demonstrates the use of SAS® for all methods
An entertaining historical perspective on the development of the methods
Specialized methods for ordinal data, small samples, multicategory data, and matched pairs
More than 100 analyses of real data sets and nearly 300 exercises
Written in an applied, nontechnical style, the book illustrates methods using a wide variety of real data, including medical clinical trials, drug use by teenagers, basketball shooting, horseshoe crab mating, environmental opinions, correlates of happiness, and much more.
An Introduction to Categorical Data Analysis, Second Edition is an invaluable tool for social, behavioral, and biomedical scientists, as well as researchers in public health, marketing, education, biological and agricultural sciences, and industrial quality control.

79.Bayesian Statistics and Marketing

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By Peter E. Rossi
Publisher:   John Wiley & Sons
Number Of Pages:   348
Publication Date:   2005
Sales Rank:   125058
ISBN / ASIN:   0470863676
EAN:   9780470863671
Binding:   Hardcover
Book Description:
The past decade has seen a dramatic increase in the use of Bayesian methods in marketing due, in part, to computational and modelling breakthroughs, making its implementation ideal for many marketing problems. Bayesian analyses can now be conducted over a wide range of marketing problems, from new product introduction to pricing, and with a wide variety of different data sources.
Bayesian Statistics and Marketing describes the basic advantages of the Bayesian approach, detailing the nature of the computational revolution. Examples contained include household and consumer panel data on product purchases and survey data, demand models based on micro-economic theory and random effect models used to pool data among respondents. The book also discusses the theory and practical use of MCMC methods.
Written by the leading experts in the field, this unique book:
Presents a unified treatment of Bayesian methods in marketing, with common notation and algorithms for estimating the models.
Provides a self-contained introduction to Bayesian methods.
Includes case studies drawn from the authors’ recent research to illustrate how Bayesian methods can be extended to apply to many important marketing problems.
Is accompanied by an R package, bayesm, which implements all of the models and methods in the book and includes many datasets. In addition the book’s website hosts datasets and R code for the case studies.
Bayesian Statistics and Marketing provides a platform for researchers in marketing to analyse their data with state-of-the-art methods and develop new models of consumer behaviour. It provides a unified reference for cutting-edge marketing researchers, as well as an invaluable guide to this growing area for both graduate students and professors, alike.