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http://rapidshare.com/files/69920922/generalized_linear_and_mixed_models_mcculloch.rar
Generalized, Linear, and Mixed Models (Wiley Series in Probability and Statistics)
By Charles E. McCulloch, Shayle R. Searle,
Publisher: Wiley-Interscience
Number Of Pages: 358
Publication Date: 2001-01-01
Sales Rank: 312567
ISBN / ASIN: 047119364X
EAN: 9780471193647
Binding: Hardcover
Book Description:
Wiley Series in Probability and Statistics
A modern perspective on mixed models
The availability of powerful computing methods in recent decades has thrust linear and nonlinear mixed models into the mainstream of statistical application. This volume offers a modern perspective on generalized, linear, and mixed models, presenting a unified and accessible treatment of the newest statistical methods for analyzing correlated, nonnormally distributed data.
As a follow-up to Searle's classic, Linear Models, and Variance Components by Searle, Casella, and McCulloch, this new work progresses from the basic one-way classification to generalized linear mixed models. A variety of statistical methods are explained and illustrated, with an emphasis on maximum likelihood and restricted maximum likelihood. An invaluable resource for applied statisticians and industrial practitioners, as well as students interested in the latest results, Generalized, Linear, and Mixed Models features:
* A review of the basics of linear models and linear mixed models
* Descriptions of models for nonnormal data, including generalized linear and nonlinear models
* Analysis and illustration of techniques for a variety of real data sets
* Information on the accommodation of longitudinal data using these models
* Coverage of the prediction of realized values of random effects
* A discussion of the impact of computing issues on mixed models

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Statistics of Extremes: Theory and Applications
Author: Jan Beirlant, Jef Caers, Johan Segers, Yuri Goegebeur
Publisher: Wiley, John & Sons, Incorporated
Series: Probability and Statistics Series
ISBN: 0471976474
Summary
Research in the statistical analysis of extreme values has flourished over the past decade: new probability models, inference and data analysis techniques have been introduced; and new application areas have been explored. Statistics of Extremes comprehensively covers a wide range of models and application areas, including risk and insurance: a major area of interest and relevance to extreme value theory. Case studies are introduced providing a good balance of theory and application of each model discussed, incorporating many illustrated examples and plots of data. The last part of the book covers some interesting advanced topics, including time series, regression, multivariate and Bayesian modelling of extremes, the use of which has huge potential.
Table of Contents
1 Why extreme value theory? 11 2 The probabilistic side of extreme value theory 45 3 Away from the maximum 83 4 Tail estimation under pareto-type models 99 5 Tail estimation for all domains of attraction 131 6 Case studies 177 7 Regression analysis 209 8 Multivariate extreme value theory 251 9 Statistics of multivariate extremes 297 10 Extremes of stationary time series 369 11 Bayesian methodology in extreme value statistics 429

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Modes of Parametric Statistical Inference (Wiley Series in Probability and Statistics)
By Seymour Geisser,&nbspWesley M. Johnson,  
Publisher:   Wiley-Interscience
Number Of Pages:   192
Publication Date:   2006-01-17
Sales Rank:   847789
ISBN / ASIN:   0471667269
EAN:   9780471667261
Binding:   Hardcover
Book Description:
A fascinating investigation into the foundations of statistical inference
This publication examines the distinct philosophical foundations of different statistical modes of parametric inference. Unlike many other texts that focus on methodology and applications, this book focuses on a rather unique combination of theoretical and foundational aspects that underlie the field of statistical inference. Readers gain a deeper understanding of the evolution and underlying logic of each mode as well as each mode's strengths and weaknesses.
The book begins with fascinating highlights from the history of statistical inference. Readers are given historical examples of statistical reasoning used to address practical problems that arose throughout the centuries. Next, the book goes on to scrutinize four major modes of statistical inference:
* Frequentist
* Likelihood
* Fiducial
* Bayesian
The author provides readers with specific examples and counterexamples of situations and datasets where the modes yield both similar and dissimilar results, including a violation of the likelihood principle in which Bayesian and likelihood methods differ from frequentist methods. Each example is followed by a detailed discussion of why the results may have varied from one mode to another, helping the reader to gain a greater understanding of each mode and how it works. Moreover, the author provides considerable mathematical detail on certain points to highlight key aspects of theoretical development.
The author's writing style and use of examples make the text clear and engaging. This book is fundamental reading for graduate-level students in statistics as well as anyone with an interest in the foundations of statistics and the principles underlying statistical inference, including students in mathematics and the philosophy of science. Readers with a background in theoretical statistics will find the text both accessible and absorbing.

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Univariate Discrete Distributions (Wiley Series in Probability and Statistics)
By Norman L. Johnson, Adrienne W. Kemp, Samuel Kotz
Publisher:   Wiley-Interscience
Number Of Pages:   672
Publication Date:   2005-08-30
ISBN-10 / ASIN:   0471272469
ISBN-13 / EAN:   9780471272465
Binding:   Hardcover
Book Description:
Discover the latest advances in discrete distributions theory
The Third Edition of the critically acclaimed Univariate Discrete Distributions provides a self-contained, systematic treatment of the theory, derivation, and application of probability distributions for count data. Generalized zeta-function and q-series distributions have been added and are covered in detail. New families of distributions, including Lagrangian-type distributions, are integrated into this thoroughly revised and updated text. Additional applications of univariate discrete distributions are explored to demonstrate the flexibility of this powerful method.
A thorough survey of recent statistical literature draws attention to many new distributions and results for the classical distributions. Approximately 450 new references along with several new sections are introduced to reflect the current literature and knowledge of discrete distributions.
Beginning with mathematical, probability, and statistical fundamentals, the authors provide clear coverage of the key topics in the field, including:
* Families of discrete distributions
* Binomial distribution
* Poisson distribution
* Negative binomial distribution
* Hypergeometric distributions
* Logarithmic and Lagrangian distributions
* Mixture distributions
* Stopped-sum distributions
* Matching, occupancy, runs, and q-series distributions
* Parametric regression models and miscellanea
Emphasis continues to be placed on the increasing relevance of Bayesian inference to discrete distribution, especially with regard to the binomial and Poisson distributions. New derivations of discrete distributions via stochastic processes and random walks are introduced without unnecessarily complex discussions of stochastic processes. Throughout the Third Edition, extensive information has been added to reflect the new role of computer-based applications.
With its thorough coverage and balanced presentation of theory and application, this is an excellent and essential reference for statisticians and mathematicians.

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Contemporary Bayesian Econometrics and Statistics (Wiley Series in Probability and Statistics)
By John Geweke
Publisher: Wiley-Interscience
Number Of Pages: 320
Publication Date: 2005-09-14
ISBN-10 / ASIN: 0471679321
ISBN-13 / EAN: 9780471679325
Binding: Hardcover
Book Description:
Tools to improve decision making in an imperfect world
This publication provides readers with a thorough understanding of Bayesian analysis that is grounded in the theory of inference and optimal decision making. Contemporary Bayesian Econometrics and Statistics provides readers with state-of-the-art simulation methods and models that are used to solve complex real-world problems. Armed with a strong foundation in both theory and practical problem-solving tools, readers discover how to optimize decision making when faced with problems that involve limited or imperfect data.
The book begins by examining the theoretical and mathematical foundations of Bayesian statistics to help readers understand how and why it is used in problem solving. The author then describes how modern simulation methods make Bayesian approaches practical using widely available mathematical applications software. In addition, the author details how models can be applied to specific problems, including:
Linear models and policy choices
Modeling with latent variables and missing data
Time series models and prediction
Comparison and evaluation of models
The publication has been developed and fine- tuned through a decade of classroom experience, and readers will find the author's approach very engaging and accessible. There are nearly 200 examples and exercises to help readers see how effective use of Bayesian statistics enables them to make optimal decisions. MATLAB® and R computer programs are integrated throughout the book. An accompanying Web site provides readers with computer code for many examples and datasets.
This publication is tailored for research professionals who use econometrics and similar statistical methods in their work. With its emphasis on practical problem solving and extensive use of examples and exercises, this is also an excellent textbook for graduate-level students in a broad range of fields, including economics, statistics, the social sciences, business, and public policy.

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Approximation Theorems of Mathematical Statistics (Wiley Series in Probability and Statistics)
By Robert J. Serfling
Publisher:   Wiley-Interscience
Number Of Pages:   392
Publication Date:   1980-11
ISBN-10 / ASIN:   0471024031
ISBN-13 / EAN:   9780471024033
Binding:   Hardcover
Product Description:
This paperback reprint of one of the best in the field covers a broad range of limit theorems useful in mathematical statistics, along with methods of proof and techniques of application. The manipulation of "probability" theorems to obtain "statistical" theorems is emphasized.

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Image Processing and Jump Regression Analysis (Wiley Series in Probability and Statistics)
By Peihua Qiu
Publisher:   Wiley-Interscience
Number Of Pages:   344
Publication Date:   2005-01-28
ISBN-10 / ASIN:   0471420999
ISBN-13 / EAN:   9780471420996
Binding:   Hardcover
Book Description:
Image Processing and Jump Regression Analysis builds a bridge between the worlds of computer graphics and statistics by addressing both the connections and the differences between these two disciplines. The author provides a systematic breakdown of the methodology behind nonparametric jump regression analysis by outlining procedures that are easy to use, simple to compute, and have proven statistical theory behind them. Key topics include conventional smoothing procedures, estimation of jump regression curves, edge detection in image processing, and edge-preserving image restoration, to name a few. With mathematical proofs kept to a minimum, this book is uniquely accessible as a primary text in nonparametric jump regression analysis and image processing as well as a reference on image processing or curve/surface estimation.

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Operational Risk : Modeling Analytics (Wiley Series in Probability and Statistics)
By Harry H. Panjer
Publisher: Wiley-Interscience
Number Of Pages: 448
Publication Date: 2006-07-28
ISBN-10 / ASIN: 0471760897
ISBN-13 / EAN: 9780471760894
Binding: Hardcover
Book Description:
Discover how to optimize business strategies from both qualitative and quantitative points of view
Operational Risk: Modeling Analytics is organized around the principle that the analysis of operational risk consists, in part, of the collection of data and the building of mathematical models to describe risk. This book is designed to provide risk analysts with a framework of the mathematical models and methods used in the measurement and modeling of operational risk in both the banking and insurance sectors.
Beginning with a foundation for operational risk modeling and a focus on the modeling process, the book flows logically to discussion of probabilistic tools for operational risk modeling and statistical methods for calibrating models of operational risk. Exercises are included in chapters involving numerical computations for students' practice and reinforcement of concepts.
Written by Harry Panjer, one of the foremost authorities in the world on risk modeling and its effects in business management, this is the first comprehensive book dedicated to the quantitative assessment of operational risk using the tools of probability, statistics, and actuarial science.
In addition to providing great detail of the many probabilistic and statistical methods used in operational risk, this book features:
* Ample exercises to further elucidate the concepts in the text
* Definitive coverage of distribution functions and related concepts
* Models for the size of losses
* Models for frequency of loss
* Aggregate loss modeling
* Extreme value modeling
* Dependency modeling using copulas
* Statistical methods in model selection and calibration
Assuming no previous expertise in either operational risk terminology or in mathematical statistics, the text is designed for beginning graduate-level courses on risk and operational management or enterprise risk management. This book is also useful as a reference for practitioners in both enterprise risk management and risk and operational management.

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http://rapidshare.com/files/99065797/Design_and_Analysis_of_Experiments__Volume_1.rar
Design and Analysis of Experiments, Introduction to Experimental Design (Wiley Series in Probability and Statistics)
By Klaus Hinkelmann, Oscar Kempthorne
Publisher: Wiley-Interscience
Number Of Pages: 631
Publication Date: 2007-12-17
ISBN-10 / ASIN: 0471727563
ISBN-13 / EAN: 9780471727569
Binding: Hardcover
Book Description:
This user-friendly new edition reflects a modern and accessible approach to experimental design and analysis
Design and Analysis of Experiments, Volume 1, Second Edition provides a general introduction to the philosophy, theory, and practice of designing scientific comparative experiments and also details the intricacies that are often encountered throughout the design and analysis processes. With the addition of extensive numerical examples and expanded treatment of key concepts, this book further addresses the needs of practitioners and successfully provides a solid understanding of the relationship between the quality of experimental design and the validity of conclusions.
This Second Edition continues to provide the theoretical basis of the principles of experimental design in conjunction with the statistical framework within which to apply the fundamental concepts. The difference between experimental studies and observational studies is addressed, along with a discussion of the various components of experimental design: the error-control design, the treatment design, and the observation design. A series of error-control designs are presented based on fundamental design principles, such as randomization, local control (blocking), the Latin square principle, the split-unit principle, and the notion of factorial treatment structure. This book also emphasizes the practical aspects of designing and analyzing experiments and features:
Increased coverage of the practical aspects of designing and analyzing experiments, complete with the steps needed to plan and construct an experiment
A case study that explores the various types of interaction between both treatment and blocking factors, and numerical and graphical techniques are provided to analyze and interpret these interactions
Discussion of the important distinctions between two types of blocking factors and their role in the process of drawing statistical inferences from an experiment
A new chapter devoted entirely to repeated measures, highlighting its relationship to split-plot and split-block designs
Numerical examples using SAS® to illustrate the analyses of data from various designs and to construct factorial designs that relate the results to the theoretical derivations
Design and Analysis of Experiments, Volume 1, Second Edition is an ideal textbook for first-year graduate courses in experimental design and also serves as a practical, hands-on reference for statisticians and researchers across a wide array of subject areas, including biological sciences, engineering, medicine, pharmacology, psychology, and business.

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http://rapidshare.com/files/5107684/Chernick_Introductory_Biostatistics_for_the_Health_Sciences.djvu
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Introductory Biostatistics for the Health Sciences: Modern Applications Including Bootstrap (Wiley Series in Probability and Statistics)
By Michael R. Chernick, Robert H. Friis
Publisher:   Wiley-Interscience
Number Of Pages:   424
Publication Date:   2003-03-17
ISBN-10 / ASIN:   047141137X
ISBN-13 / EAN:   9780471411376
Binding:   Hardcover
Product Description:
Accessible to medicine- and/or public policy-related audiences, as well as most statisticians.
Emphasis on outliers is discussed by way of detection and treatment.
Resampling statistics software is incorporated throughout.
Motivating applications are presented in light of honest theory.
Plentiful exercises are sprinkled throughout.
A textbook for an introductory course in statistical methods for undergraduate students in the health sciences who have had high school algebra but not necessarily calculus. A previous statistics course would be helpful but is not necessary.