[概率统计] Wiley Series in Probability and Statistics系列 119 books
似乎有几本没有上来 各位使用过程中发现哪个没有 我再补充呵呵
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
90.Statistical Size Distributions in Economics and Actuarial Sciences
91.Modern Experimental Design
92.Comparative Statistical Inference
93.Methods of Multivariate Analysis
94.Robust Statistics
95.Order Statistics
96.Fourier Analysis of Time Series : An Introduction
97.Spatial Statistics 1981-04
98.Clinical Trials : A Methodologic Perspective
99.Numerical Issues in Statistical Computing for the Social Scientist
100.Nonlinear Regression
101.Preparing for the Worst : Incorporating Downside Risk in Stock Market Investments
102.Nonlinear Regression Analysis and Its Applications
103.Mixed Models : Theory and Applications
104.Discrete Distributions : Applications in the Health Sciences
105.Design and Analysis of Clinical Trials : Concepts and Methodologies
106.Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives
107.Flowgraph Models for Multistate Time-–to-Event Data
108.Randomization in Clinical Trials: Theory and Practice
109.Regression With Social Data: Modeling Continuous and Limited Response Variables
110.Regression Analysis by Example
111.Categorical Data Analysis
112.Statistical Methods for Reliability Data
113.Elements of Stochastic Processes Wit
114.Random Graphs for Statistical Pattern Recognition
115.Construction and Assessment of Classification Rules
116.The Statistical Analysis of Failure Time Data
117.Statistical Analysis of Finite Mixture Distributions
118.Modern Applied U-Statistics
119.Applied Multiway Data Analysis
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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.
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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.
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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
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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.
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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.
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Robust Statistics: Theory and Methods (Wiley Series in Probability and Statistics)
By Ricardo A. Maronna, Douglas R. Martin, Victor 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.
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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.
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