(英-Math.)Wiley Series in Probability and Statistics系列

1. Advanced Calculus with Applications in Statistics
2.A History of Probability and Statistics and Their Applications before 1750
3.Markov Decision Processes: Discrete Stochastic Dynamic Programming
4.Probability and Statistical Inference
5.Continuous Univariate Distributions, Vol. 1
6.Continuous Univariate Distributions, Vol. 2
7.The Theory of Measures and Integration
8.Robust Statistics: Theory and Methods
9.Finite Mixture Models
10.Generalized, Linear, and Mixed Models
11.Statistics of Extremes: Theory and Applications
12.Modes of Parametric Statistical Inference
13.Univariate Discrete Distributions
14.Contemporary Bayesian Econometrics and Statistics
15.Approximation Theorems of Mathematical Statistics
16.Image Processing and Jump Regression Analysis
17.Operational Risk : Modeling Analytics
18.Design and Analysis of Experiments, Introduction to Experimental Design
19.Introductory Biostatistics for the Health Sciences: Modern Applications Including Bootstrap
20.Linear Models in Statistics
21.Statistics for Research
22.Applied Logistic Regression
23.Operational Subjective Statistical Methods: A Mathematical, Philosophical, and Historical Introduction
24.Probability and Measure, 2nd Edition
25.Theory of Preliminary Test and Stein-Type Estimation with Applications
26.The EM Algorithm and Extensions
27.The Theory of Response-Adaptive Randomization in Clinical Trials
28.Models for Probability and Statistical Inference: Theory and Applications
29.Applied Life Data Analysis
30.Structural Equation Modelling: A Bayesian Approach
31.Bootstrap Methods: A Guide for Practitioners and Researchers
32.Nonparametric Analysis of Univariate Heavy-Tailed Data: Research and Practice
33.Applied Linear Regression, 3rd edition
34.Theory of Probability: A Critical Introductory Treatment
35.Financial Derivatives in Theory and Practice
36.Quantitative Methods in Population Health: Extensions of Ordinary Regression
37.Statistical Methods for Survival Data Analysis
38.Applied Bayesian Modelling
39.Spatial Statistics, 2004-08
40.Approximate Dynamic Programming: Solving the Curses of Dimensionality
41.Variance Components
42.Time Series: Applications to Finance
43.Generalized Least Squares
44.Statistical Analysis With Missing Data
45.Long-Memory Time Series: Theory and Methods
46.Statistical Models and Methods for Lifetime Data
47.Uncertainty Analysis with High Dimensional Dependence Modelling
48.Simulation and the Monte Carlo Method
49.A Matrix Handbook for Statisticians
50.Meta Analysis: A Guide to Calibrating and Combining Statistical Evidence
51.Precedence-Type Tests and Applications
52.Statistical Meta-Analysis with Applications
53.Management of Data in Clinical Trials
54.Periodically Correlated Random Sequences: Spectral Theory and Practice
55.Design and Analysis of Experiments, Advanced Experimental Design
56.Methods and Applications of Linear Models : Regression and the Analysis of Variance
57.Combinatorial Methods in Discrete Distributions
58.Nonparametric Regression Methods for Longitudinal Data Analysis: Mixed-Effects Modeling Approaches
59.Response Surfaces, Mixtures, and Ridge Analyses
60.Variations on Split Plot and Split Block Experiment Designs
61.Recent Advances in Quantitative Methods in Cancer and Human Health Risk Assessment
62.The Construction of Optimal Stated Choice Experiments: Theory and Methods
63.Nonparametric Density Estimation: The L1 View
64.Applied MANOVA and Discriminant Analysis
65.Survey Errors and Survey Costs
66.Statistical Advances in the Biomedical Sciences: Clinical Trials, Epidemiology, Survival Analysis, and Bioinformatics
67.Latent Curve Models: A Structural Equation Perspective
68.Regression Diagnostics: Identifying Influential Data and Sources of Collinearity
69.Reliability and Risk: A Bayesian Perspective
70.Environmental Statistics
71.Bayes Linear Statistics, Theory & Methods
72.Introductory Stochastic Analysis for Finance and Insurance
73.Bayesian Models for Categorical Data
74.Bayesian Statistical Modelling
75.Weibull Models
76.Analysis of Financial Time Series
77.Linear Model Theory: Univariate, Multivariate, and Mixed Models
78.An Introduction to Categorical Data Analysis
79.Bayesian Statistics and Marketing
80.Statistical Shape Analysis
81.Nonparametric Statistics with Applications to Science and Engineering
82.Longitudinal Data Analysis
83.Regression Models for Time Series Analysis
84.Introduction to Nonparametric Regression
85.Statistical Modeling by Wavelets
86.Case Studies in Reliability and Maintenance
87.The Geometry of Random Fields
88.Biostatistics : A Methodology For the Health Sciences
89.Planning, Construction, and Statistical Analysis of Comparative Experiments

1. Advanced Calculus with Applications in Statistics

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Advanced Calculus with Applications in Statistics (Wiley Series in Probability and Statistics)
By André I. Khuri
Publisher:   Wiley-Interscience
Number Of Pages:   673
Publication Date:   2002-11-18
Sales Rank:   316939
ISBN / ASIN:   0471391042
EAN:   9780471391043
Binding:   Hardcover
Manufacturer:   Wiley-Interscience
Studio:   Wiley-Interscience
Average Rating:   
Total Reviews:   
Review
"This is an exceptional book, which I would recommend for anyone beginning a career in statistical research." (Journal of the American Statistical Association, September 2004)
Book Description
Successful track record
No competition
Unique blend of mathematics and statistics
Emphasis on applications
The publisher, John Wiley & Sons
Designed to help motivate the learning of advanced calculus by demonstrating its relevance in the field of statistics. Features detailed coverage of optimization techniques and their applications in statistics. Introduces approximation theory. Each chapter contains a significant amount of examples and exercises as well as additional reading lists. --This text refers to an out of print or unavailable edition of this title.
From the Back Cover
Praise for the First Edition
"An enticing approach to the subject. . . . Students contemplating a career in statistics will acquire a valuable understanding of the underlying structure of statistical theory. . . statisticians should consider purchasing it as an additional reference on advanced calculus."
–Journal of the American Statistical Association
"This book is indeed a pleasure to read. It is simple to understand what the author is attempting to accomplish, and to follow him as he proceeds. . . . I would highly recommend the book for one’s personal collection or suggest your librarian purchase a copy."
–Journal of the Operational Research Society
Knowledge of advanced calculus has become imperative to the understanding of the recent advances in statistical methodology. The First Edition of Advanced Calculus with Applications in Statistics has served as a reliable resource for both practicing statisticians and students alike. In light of the tremendous growth of the field of statistics since the book’s publication, André Khuri has reexamined his popular work and substantially expanded it to provide the most up-to-date and comprehensive coverage of the subject.
Retaining the original’s much-appreciated application-oriented approach, Advanced Calculus with Applications in Statistics, Second Edition supplies a rigorous introduction to the central themes of advanced calculus suitable for both statisticians and mathematicians alike. The Second Edition adds significant new material on:
Basic topological concepts
Orthogonal polynomials
Fourier series
Approximation of integrals
Solutions to selected exercises
The volume’s user-friendly text is notable for its end-of-chapter applications, designed to be flexible enough for both statisticians and mathematicians. Its well thought-out solutions to exercises encourage independent study and reinforce mastery of the content. Any statistician, mathematician, or student wishing to master advanced calculus and its applications in statistics will find this new edition a welcome resource.
About the Author
ANDRÉ I. KHURI, PhD, is a Professor in the Department of Statistics at the University of Florida, Gainesville.
Preface.
1. An Introduction to Set Theory.
2. Basic Concepts in Linear Algebra.
3. Limits and Continuity of Functions.
4. Differentiation.
5. Infinite Sequences and Series.
6. Integration.
7. Multidimensional Calculus.
8. Optimization in Statistics.
9. Approximation of Functions.
10. Orthogonal Polynomials.
11. Fourier Series.
12. Approximation of Integrals.
Appendix. Solutions to Selected Exercises.
General Bibliography.
Index.
2.A History of Probability and Statistics and Their Applications before 1750

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A History of Probability and Statistics and Their Applications before 1750 (Wiley Series in Probability and Statistics)
By Anders Hald
Publisher:   Wiley-Interscience
Number Of Pages:   608
Publication Date:   1990-01
ISBN-10 / ASIN:   0471502308
ISBN-13 / EAN:   9780471502302
Binding:   Hardcover
Product Description:
Evoking the life and works of the great natural philosophers who contributed to the development of probability theory and statistics, this bestseller—now available in paperback--also describes the contemporaneous development and interaction of probability theory (and games of chance), statistics (particularly in astronomy and demography), and life insurance mathematics. To read and enjoy this intellectual history, you need know but little statistics or mathematics.

3.Markov Decision Processes: Discrete Stochastic Dynamic Programming

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Markov Decision Processes: Discrete Stochastic Dynamic Programming (Wiley Series in Probability and Statistics)
By Martin L. Puterman
Publisher:   Wiley-Interscience
Number Of Pages:   680
Publication Date:   2005-03-03
ISBN-10 / ASIN:   0471727822
ISBN-13 / EAN:   9780471727828
Binding:   Paperback
Book Description:
The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists.
"This text is unique in bringing together so many results hitherto found only in part in other texts and papers. . . . The text is fairly self-contained, inclusive of some basic mathematical results needed, and provides a rich diet of examples, applications, and exercises. The bibliographical material at the end of each chapter is excellent, not only from a historical perspective, but because it is valuable for researchers in acquiring a good perspective of the MDP research potential."
-Zentralblatt fur Mathematik
". . . it is of great value to advanced-level students, researchers, and professional practitioners of this field to have now a complete volume (with more than 600 pages) devoted to this topic. . . . Markov Decision Processes: Discrete Stochastic Dynamic Programming represents an up-to-date, unified, and rigorous treatment of theoretical and computational aspects of discrete-time Markov decision processes."
-Journal of the American Statistical Association

4.Probability and Statistical Inference

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Probability and Statistical Inference (Wiley Series in Probability and Statistics)
By Robert Bartoszynski, Magdalena Niewiadomska-Bugaj
Publisher:   Wiley-Interscience
Number Of Pages:   647
Publication Date:   2008-01-02
ISBN-10 / ASIN:   0471696935
ISBN-13 / EAN:   9780471696933
Binding:   Hardcover
Book Description:
Probability and Statistical Inference, Second Edition is a user-friendly book that stresses the comprehension of concepts instead of the simple acquisition of a skill or tool. It provides a mathematical framework that permits students to carry out various procedures using any number of computer software packages as opposed to relying on one particular package. Its unique approach to problems allows readers to integrate the knowledge gained from the text, thus, enhancing a more complete and honest understanding of the topic.

5.Continuous Univariate Distributions, Vol. 1

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Continuous Univariate Distributions, Vol. 1 (Wiley Series in Probability and Statistics)
By Norman L. Johnson, Samuel Kotz, N. Balakrishnan
Publisher:   Wiley-Interscience
Number Of Pages:   761
Publication Date:   1994-10
ISBN-10 / ASIN:   0471584959
ISBN-13 / EAN:   9780471584957
Binding:   Hardcover
6.Continuous Univariate Distributions, Vol. 2

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Continuous Univariate Distributions, Vol. 2 (Wiley Series in Probability and Statistics)
By Norman L. Johnson, Samuel Kotz, N. Balakrishnan
Publisher:   Wiley-Interscience
Number Of Pages:   752
Publication Date:   1995-05-08
ISBN-10 / ASIN:   0471584940
ISBN-13 / EAN:   9780471584940
Binding:   Hardcover

7.The Theory of Measures and Integration

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The Theory of Measures and Integration (Wiley Series in Probability and Statistics)
By Eric M. Vestrup
Publisher:   Wiley-Interscience
Number Of Pages:   594
Publication Date:   2003-09-18
ISBN-10 / ASIN:   0471249777
ISBN-13 / EAN:   9780471249771
Binding:   Hardcover
Product Description:
An accessible, clearly organized survey of the basic topics of measure theory for students and researchers in mathematics, statistics, and physics
In order to fully understand and appreciate advanced probability, analysis, and advanced mathematical statistics, a rudimentary knowledge of measure theory and like subjects must first be obtained. The Theory of Measures and Integration illuminates the fundamental ideas of the subject-fascinating in their own right-for both students and researchers, providing a useful theoretical background as well as a solid foundation for further inquiry.
Eric Vestrup's patient and measured text presents the major results of classical measure and integration theory in a clear and rigorous fashion. Besides offering the mainstream fare, the author also offers detailed discussions of extensions, the structure of Borel and Lebesgue sets, set-theoretic considerations, the Riesz representation theorem, and the Hardy-Littlewood theorem, among other topics, employing a clear presentation style that is both evenly paced and user-friendly. Chapters include:
* Measurable Functions
* The Lp Spaces
* The Radon-Nikodym Theorem
* Products of Two Measure Spaces
* Arbitrary Products of Measure Spaces
Sections conclude with exercises that range in difficulty between easy "finger exercises"and substantial and independent points of interest. These more difficult exercises are accompanied by detailed hints and outlines. They demonstrate optional side paths in the subject as well as alternative ways of presenting the mainstream topics.
In writing his proofs and notation, Vestrup targets the person who wants all of the details shown up front. Ideal for graduate students in mathematics, statistics, and physics, as well as strong undergraduates in these disciplines and practicing researchers, The Theory of Measures and Integration proves both an able primary text for a real analysis sequence with a focus on measure theory and a helpful background text for advanced courses in probability and statistics.

8.Robust Statistics: Theory and Methods

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Robust Statistics: Theory and Methods (Wiley Series in Probability and Statistics)
By Ricardo A. Maronna,&nbspDouglas R. Martin,&nbspVictor J. Yohai,  
Publisher:   Wiley
Number Of Pages:   436
Publication Date:   2006-06-13
Sales Rank:   65112
ISBN / ASIN:   0470010924
EAN:   9780470010921
Binding:   Hardcover
Book Description:
Classical statistical techniques fail to cope well with deviations from a standard distribution. Robust statistical methods take into account these deviations while estimating the parameters of parametric models, thus increasing the accuracy of the inference. Research into robust methods is flourishing, with new methods being developed and different applications considered.
Robust Statistics sets out to explain the use of robust methods and their theoretical justification. It provides an up-to-date overview of the theory and practical application of the robust statistical methods in regression, multivariate analysis, generalized linear models and time series. This unique book:
Enables the reader to select and use the most appropriate robust method for their particular statistical model.
Features computational algorithms for the core methods.
Covers regression methods for data mining applications.
Includes examples with real data and applications using the S-Plus robust statistics library.
Describes the theoretical and operational aspects of robust methods separately, so the reader can choose to focus on one or the other.
Supported by a supplementary website featuring time-limited S-Plus download, along with datasets and S-Plus code to allow the reader to reproduce the examples given in the book.
Robust Statistics aims to stimulate the use of robust methods as a powerful tool to increase the reliability and accuracy of statistical modelling and data analysis. It is ideal for researchers, practitioners and graduate students of statistics, electrical, chemical and biochemical engineering, and computer vision. There is also much to benefit researchers from other sciences, such as biotechnology, who need to use robust statistical methods in their work.

9.Finite Mixture Models

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Finite Mixture Models (Wiley Series in Probability and Statistics)
By Geoffrey McLachlan, David Peel,
Publisher: Wiley-Interscience
Number Of Pages: 456
Publication Date: 2000-10-02
Sales Rank: 648477
ISBN / ASIN: 0471006262
EAN: 9780471006268
Binding: Hardcover
Book Description:
An up-to-date, comprehensive account of major issues in finite mixture modeling
This volume provides an up-to-date account of the theory and applications of modeling via finite mixture distributions. With an emphasis on the applications of mixture models in both mainstream analysis and other areas such as unsupervised pattern recognition, speech recognition, and medical imaging, the book describes the formulations of the finite mixture approach, details its methodology, discusses aspects of its implementation, and illustrates its application in many common statistical contexts.
Major issues discussed in this book include identifiability problems, actual fitting of finite mixtures through use of the EM algorithm, properties of the maximum likelihood estimators so obtained, assessment of the number of components to be used in the mixture, and the applicability of asymptotic theory in providing a basis for the solutions to some of these problems. The author also considers how the EM algorithm can be scaled to handle the fitting of mixture models to very large databases, as in data mining applications. This comprehensive, practical guide:
* Provides more than 800 references-400ublished since 1995
* Includes an appendix listing available mixture software
* Links statistical literature with machine learning and pattern recognition literature
* Contains more than 100 helpful graphs, charts, and tables
Finite Mixture Models is an important resource for both applied and theoretical statisticians as well as for researchers in the many areas in which finite mixture models can be used to analyze data.

10.Generalized, Linear, and Mixed Models

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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

11.Statistics of Extremes: Theory and Applications

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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

12.Modes of Parametric Statistical Inference

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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.

13.Univariate Discrete Distributions

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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.

14.Contemporary Bayesian Econometrics and Statistics

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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.

15.Approximation Theorems of Mathematical Statistics

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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.
16.Image Processing and Jump Regression Analysis

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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.

17.Operational Risk : Modeling Analytics

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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.