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[好书推荐] (原版丛书) Wiley Series in Probability and Statistics 49本(10.19更新)
huntaway
99350 522
2009-09-10

辛苦收集，所以象征性收费，每本1论坛币，还望各位体谅。


可能有部分书籍同论坛上的其他资源重复，本人只是想将这一系列的丛书能够更集中的展示，还请斑竹手下留情，勿以重复发贴处理，先在此谢过！


由于本人目前涉及领域主要针对统计分析以及寿命分析，因此此处给出的书籍有一定的偏向性。


读书品书在于读和品，而不是单纯的收集，因此由衷希望大家下载后能学以致用，共同进步。


当然，下载后回帖是美德！
欢迎你对给出的资源或者本人给出你的宝贵建议或意见。

结合一些贴友的反馈，本人争取收集到更多的该系列的书籍以方便大家使用，一旦本人收集到新的资料，将会有陆续的更新。
现已有部分资料更新上传，请大家留意。

Wiley Series in Probability and Statistics系列
1. Advanced Calculus with Applications in Statistics (157楼)
2.A History of Probability and Statistics and Their Applications before 1750 (158楼)
3.Markov Decision Processes: Discrete Stochastic Dynamic Programming (159楼)
4.Probability and Statistical Inference (1楼)
5.Continuous Univariate Distributions, Vol. 1 (160楼)
6.Continuous Univariate Distributions, Vol. 2 (160楼)
7.The Theory of Measures and Integration  (161楼)
8.Robust Statistics: Theory and Methods (3楼)
9.Finite Mixture Models (162楼)
10.Generalized, Linear, and Mixed Models (4楼)
11.Statistics of Extremes: Theory and Applications  (163楼)
12.Modes of Parametric Statistical Inference  (164楼)
13.Univariate Discrete Distributions (169楼)
14.Contemporary Bayesian Econometrics and Statistics (170楼)
15.Approximation Theorems of Mathematical Statistics
16.Image Processing and Jump Regression Analysis (171楼)
17.Operational Risk : Modeling Analytics (172楼)
18.Design and Analysis of Experiments, Introduction to Experimental Design (5楼)
19.Introductory Biostatistics for the Health Sciences: Modern Applications Including Bootstrap (173楼)
20.Linear Models in Statistics (6楼)
21.Statistics for Research (7楼)
22.Applied Logistic Regression (174楼)
23.Operational Subjective Statistical Methods: A Mathematical, Philosophical, and Historical Introduction (175楼)
24.Probability and Measure, 2nd Edition (9楼)
25.Theory of Preliminary Test and Stein-Type Estimation with Applications (191楼)
26.The EM Algorithm and Extensions (192楼)
27.The Theory of Response-Adaptive Randomization in Clinical Trials (194楼)
28.Models for Probability and Statistical Inference: Theory and Applications (10楼)
29.Applied Life Data Analysis (11楼)
30.Structural Equation Modelling: A Bayesian Approach (195楼)
31.Bootstrap Methods: A Guide for Practitioners and Researchers (196楼)
32.Nonparametric Analysis of Univariate Heavy-Tailed Data: Research and Practice (197楼)
33.Applied Linear Regression, 3rd edition (12楼)
34.Theory of Probability: A Critical Introductory Treatment (198楼)
35.Financial Derivatives in Theory and Practice
36.Quantitative Methods in Population Health: Extensions of Ordinary Regression
37.Statistical Methods for Survival Data Analysis (13楼)
38.Applied Bayesian Modelling
39.Spatial Statistics, 2004-08
40.Approximate Dynamic Programming: Solving the Curses of Dimensionality
41.Variance Components (14楼)
42.Time Series: Applications to Finance
43.Generalized Least Squares (15楼)
44.Statistical Analysis With Missing Data (16楼)
45.Long-Memory Time Series: Theory and Methods
46.Statistical Models and Methods for Lifetime Data (17楼)
47.Uncertainty Analysis with High Dimensional Dependence Modelling
48.Simulation and the Monte Carlo Method (18楼)
49.A Matrix Handbook for Statisticians (19楼)
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 (20楼)
56.Methods and Applications of Linear Models : Regression and the Analysis of Variance (21楼)
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 (22楼)
60.Variations on Split Plot and Split Block Experiment Designs (23楼)
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 (132楼，感谢pangchunyang的无私分享)
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 (132楼，感谢pangchunyang的无私分享)
75.Weibull Models (24楼)
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.Nonlinear regression (25楼)
91.Applied Regression Analysis (26楼)


4.Probability and Statistical Inference
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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xysy
2009-9-10 13:24:27
支持楼主这样行为
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huntaway
2009-9-10 13:25:31
8. Robust Statistics: Theory and Methods
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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huntaway
2009-9-10 13:27:00
10.Generalized, Linear, and Mixed Models
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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(10) Generalized, Linear, and Mixed Models.pdf

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huntaway
2009-9-10 13:28:55
18. Design and Analysis of Experiments, Introduction to Experimental Design
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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huntaway
2009-9-10 13:30:21
20.Linear Models in Statistics
Linear Models in Statistics (Wiley Series in Probability and Statistics)
By Alvin C. Rencher, G. Bruce Schaalje
Publisher: Wiley-Interscience
Number Of Pages: 672
Publication Date: 2008-01-02
ISBN-10 / ASIN: 0471754986
ISBN-13 / EAN: 9780471754985
Binding: Hardcover
Book Description:
The essential introduction to the theory and application of linear models-now in a valuable new edition
Since most advanced statistical tools are generalizations of the linear model, it is neces-sary to first master the linear model in order to move forward to more advanced concepts. The linear model remains the main tool of the applied statistician and is central to the training of any statistician regardless of whether the focus is applied or theoretical. This completely revised and updated new edition successfully develops the basic theory of linear models for regression, analysis of variance, analysis of covariance, and linear mixed models. Recent advances in the methodology related to linear mixed models, generalized linear models, and the Bayesian linear model are also addressed.
Linear Models in Statistics, Second Edition includes full coverage of advanced topics, such as mixed and generalized linear models, Bayesian linear models, two-way models with empty cells, geometry of least squares, vector-matrix calculus, simultaneous inference, and logistic and nonlinear regression. Algebraic, geometrical, frequentist, and Bayesian approaches to both the inference of linear models and the analysis of variance are also illustrated. Through the expansion of relevant material and the inclusion of the latest technological developments in the field, this book provides readers with the theoretical foundation to correctly interpret computer software output as well as effectively use, customize, and understand linear models.
This modern Second Edition features:
New chapters on Bayesian linear models as well as random and mixed linear models
Expanded discussion of two-way models with empty cells
Additional sections on the geometry of least squares
Updated coverage of simultaneous inference
The book is complemented with easy-to-read proofs, real data sets, and an extensive bibliography. A thorough review of the requisite matrix algebra has been addedfor transitional purposes, and numerous theoretical and applied problems have been incorporated with selected answers provided at the end of the book. A related Web site includes additional data sets and SAS(r) code for all numerical examples.
Linear Model in Statistics, Second Edition is a must-have book for courses in statistics, biostatistics, and mathematics at the upper-undergraduate and graduate levels. It is also an invaluable reference for researchers who need to gain a better understanding of regression and analysis of variance.
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