مجموعة من الكتب في الإحصاء والإحتمالات

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Regression Models for Categorical and Limited Dependent Variables



J Scott Long, "Regression Models for Categorical and Limited Dependent Variables"
Sage Publications, Inc | 1997 | ISBN: 0803973748 | 328 pages | PDF | 11б5 MB

THE APPROACH

"J. Scott Long’s approach is one that I highly commend. There is a decided emphasis on the application and interpretation of the specific statistical techniques. Long works from the premise that the major difficulty with the analysis of limited and categorical dependent variables (LCDVs) is the complexity of interpreting nonlinear models, and he provides tools for interpretation that can be widely applied across the different techniques."

--Robert L. Kaufman, Sociology, Ohio State University

"A thorough and comprehensive introduction to analyzing categorical and limited dependent variables from a traditional regression perspective that provides unusually clear discussions concerning estimation, identification, and the multiplicity of models available to the researcher to analyze such data."

--Scott Hershberger, Psychology, University of Kansas

THE ORGANIZATION

"The thing that impresses me the most about this book is how organized it is. The chapters are in excellent logical sequence. There is a useful repetition of important concepts (e.g., estimation, hypothesis testing) from chapter to chapter. J. Scott Long has done a terrific job of organizing like things from disparate literatures, such as the scaler measures of fit in Chapter 4."

--Herbert L. Smith, Sociology, University of Pennsylvania

"A major strength of the book is the way that it is organized. The chapter about each technique is written in a highly organized and parallel format. First the statistical basis and assumptions for the particular model are developed, then estimation issues are considered, then issues of testing and interpretation are considered, then variations and extensions are explored."

--Robert L. Kaufman, Sociology, Ohio State University

FOR THE COURSE

"I have been teaching a course on categorical data analysis to sociology graduate students for close to 20 years, but I have never found a book with which I was happy. J. Scott Long’s book, on the other hand, is nearly ideal for my objectives and preferences, and I expect that many other social scientists will feel the same way. I will definitely adopt it the next time I teach the course. It deals with the right topics in the most desirable sequence and it is clearly written."

--Paul D. Allison, Sociology, University of Pennsylvania

Class-tested at two major universities and written by an award-winning teacher, J. Scott Long’s book gives readers unified treatment of the most useful models for categorical and limited dependent variables (CLDVs). Throughout the book, the links among models are made explicit, and common methods of derivation, interpretation, and testing are applied. In addition, Long explains how models relate to linear regression models whenever possible. In order for the reader to see how these models can be applied, Long illustrates each model with data from a variety of applications, ranging from attitudes toward working mothers to scientific productivity.

The book begins with a review of the linear regression model and an introduction to maximum likelihood estimation. It then covers the logit and probit models for binary outcomes--providing details on each of the ways in which these models can be interpreted, reviews standard statistical tests associated with maximum likelihood estimation, and considers a variety of measures for assessing the fit of a model. Long extends the binary logit and probit models to ordered outcomes, presents the multinomial and conditioned logit models for nominal outcomes, and considers models with censored and truncated dependent variables with a focus on the tobit model. He also describes models for sample selection bias and presents models for count outcomes by beginning with the Poisson regression model and showing how this model leads to the negative binomial model and zero inflated count models. He concludes by comparing and contrasting the models from earlier chapters and discussing the links between these models and models not discussed in the book, such as loglinear and event history models. Helpful exercises are included in the book with brief answers included in the appendix so that readers can practice the techniques as they read about them.


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[zidaan]

Applied Linear Statistical Models



Michael H Kutner, John Neter, Christopher J. Nachtsheim, William Wasserman, "Applied Linear Statistical Models"
McGraw-Hill Higher Education; 5 ed | 2004 | ISBN:0071122214
| 1424 pages | Djvu | 9,7 MB

Applied Linear Statistical Models 5e is the long established leading authoritative text and reference on statistical modeling. For students in most any discipline where statistical analysis or interpretation is used, ALSM serves as the standard work. The text includes brief introductory and review material, and then proceeds through regression and modeling for the first half, and through ANOVA and Experimental Design in the second half. All topics are presented in a precise and clear style supported with solved examples, numbered formulae, graphic illustrations, and "Notes" to provide depth and statistical accuracy and precision. Applications used within the text and the hallmark problems, exercises, and projects are drawn from virtually all disciplines and fields providing motivation for students in virtually any college. The Fifth edition provides an increased use of computing and graphical analysis throughout, without sacrificing concepts or rigor. In general, the 5e uses larger data sets in examples and exercises, and where methods can be automated within software without loss of understanding, it is so done.

From the Publisher
Applied Linear Regression Models assumes the use of computers. Thus, while the basic mathematical steps are given, the text does not dwell on computational details. This allows instructors to eliminate complex formulas and focus on basic principles.
Multiple linear regression analysis discussion starts the text.
Polynomial regression in now woven into the discussion of multiple linear regression.
Qualitative predictor variables now follows discussion of multiple regression model building and diagnostics.
There is an expanded discussion of diagnostics and remedial measures.
New topics added include: robust tests for constancy of the error variance, smoothing techniques to explore the shape of the regression function, robust regression and nonparametric regression techniques, bootstrapping methods for evaluating the precision of sample estimates for complex situations, and estimation of the variance and standard derivation functions to obtain weights for weighted least squares.
Chapter 14 has been revised and expanded to include introduction to polytomous logistic regression, Poisson regression, and generalized linear models.
A disk containing data sets for all examples, problems, exercises, and projects as well as data in Appendix C is packaged with each text.
Applied Linear Regression Models contains several new case studies at strategic places to aid understanding of the methods discussed.
A check in the margin of the problems section indicates the Student Solutions Manual provides immediate and final answers for self-checking.
The expanded use of graphs includes scatter plot matrices, three-dimensional rotating plots, and conditional effects plots.
The comprehensive use of computer and graphic plots helps focus the text on analysis and models.
A chapter on binary dependent variables reflects the trend to the increasing importance of logistic regression models for binary dependent variables in many areas of application.
Model building is thoroughly examined to allow students to see how the model-building process integrates many of the elements considered in earlier chapters.
The discussion of regression diagnostics includes the DFBETAS, DFFITS, and PRESS measures.


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[zidaan]

Nonlinear Regression


Nonlinear Regression (Wiley Series in Probability and Statistics






Nonlinear Regression (Wiley Series in Probability and Statistics)
By George A. F. Seber
Publisher: John Wiley & Sons
Number Of Pages: 800
Publication Date: 1989-02
ISBN-10 / ASIN: 0471617601
ISBN-13 / EAN: 9780471617600
Binding: Hardcover



This text/reference provides a broad survey of aspects of model-building and statistical inference. Presents an accessible synthesis of current theoretical literature, requiring only familiarity with linearregression methods. The three chapters on central computational questions comprise a self-contained introduction to unconstrained optimization. Includes many illustrative practical examples.

Summary: It is a bible of nonlinear regression book
Rating: 5

This is a very good book for people who would like to learn nonlinear regression in deep. Comparing with Bates and Watts book, this book provides very clear nonlinear regression theories. For all statisticians who focus on nonlinear regression, they must have this book.

Summary: one of three excellent books on nonlinear regression
Rating: 5

In 2001 I reviewed for amazon the texts by Gallant and the one by Bates and Watts. This text was written by Seber and Wild, two accomplished statisticians and experienced authors. This volume is of the same high caliber as those texts and deserves mention. It is a longer text that overlaps on many topics with the other two books, deliberately neglects some areas that were well covered by Gallant (Gallant’s book came out in 1987 and this one in 1989) and hits some topics not covered by either of the other two books.
Bootstrap methods are neglected probably because the value of the bootstrap for standard error estimation in nonlinear models was not yet appreciated in 1989.

Chapters 1 and 2 provide good introductory material similar to the other texts. Chapter 1 deals with the models (linear and nonlinear) and Chapter 2 provides the basic estimation techniques. In addition to the standard material on least squares, generalized least squares and maximum likelihood, the authors also cover quasi-likelihood, linear approximations, robust estimation and Bayesian methods. Box – Cox transformations and the issue of variance heterogeneity are also treated in Chapter 2.

As they remark in the preface, they avoid much of the econometric theory and asymptotic theory that is well covered in Gallant’s book.

Chapter 3 deals with important practical issues including the convergence properties of the iterative procedures (important for nonlinear models but a non-issue in linear models), ill-conditioning and identifiability (important issues for both linear and nonlinear models).

Chapter 4 deals with curvature issues and covers much of the original work of Bates and Watts with many references to those authors. Oddly though, there is no mention of the Bates and Watts text. Both books were published by Wiley around the same time with Bates and Watts appearing in 1988 and Seber and Wild in 1989. Perhaps the Seber and Wild book went to the publisher before the Bates and Watts book came out (their preface has a May 1988 date).

Important and interesting topics covered in this book but not the others include models with time dependent errors, detailed treatment of growth models, compartmental models, multiphase and spline regresions and error-in-variables models. They also devote a whole chapter to software issues (very interesting and practical but probably mostly outdated).

Good for a graduate statistics course or for a research reference source. Has lots of material and references but lacks homework problems.

Summary: Good Text: Rigorous and Theoretical but Clear
Rating: 4

I am a Chemist Phd working in the oil refining industry and often use statistical tools to model plant process and laboratory data. this text is not for practical uses but is to be considered a very rigorous and in dephtintroduction to nonlinear regression theory. Even if quite long and detailed is still clear and not difficult to understand. if you have time to devote to the principles and not jump to solutions this is a very good advanced text

Summary: excellent coverage by accomplished authors
Rating: 5

I have recently reviewed for amazon the texts by Gallant and the one by Bates and Watts. This text was written by Seber and Wild, two accomplished statisticians and experienced authors. This volume is of the same high caliber as those texts and deserves mention. It is a longer text that overlaps on many topics with the other two books, deliberately neglects some areas that were well covered by Gallant (Gallant’s book came out in 1987 and this one in 1989) and hits some topics not covered by either of the other two books.

Bootstrap methods are neglected probably because the value of the bootstrap for standard error estimation in nonlinear models was not yet appreciated in 1989.

Chapters 1 and 2 provide good introductory material similar to the other texts. Chapter 1 deals with the models (linear and nonlinear) and Chapter 2 provides the basic estimation techniques. In addition to the standard material on least squares, generalized least squares and maximum likelihood, the authors also cover quasi-likelihood, linear approximations, robust estimation and Bayesian methods. Box – Cox transformations and the issue of variance heterogeneity are also treated in Chapter 2.

As they remark in the preface, they avoid much of the econometric theory and asymptotic theory that is well covered in Gallant’s book.

Chapter 3 deals with important practical issues including the convergence properties of the iterative procedures (important for nonlinear models but a non-issue in linear models), ill-conditioning and identifiability (important issues for both linear and nonlinear models).

Chapter 4 deals with curvature issues and covers much of the original work of Bates and Watts with many references to those authors. Oddly though, there is no mention of the Bates and Watts text. Both books were published by Wiley around the same time with Bates and Watts appearing in 1988 and Seber and Wild in 1989. Perhaps the Seber and Wild book went to the publisher before the Bates and Watts book came out (their preface has a May 1988 date).

Important and interesting topics covered in this book but not the others include models with time dependent errors, detailed treatment of growth models, compartmental models, multiphase and spline regresions and error-in-variables models. They also devote a whole chapter to software issues (very interesting and practical but probably mostly outdated).

Good for a graduate statistics course or for a research reference source. Has lots of material and references but lacks homework problems.

Summary: Excelent book on nonlinear regression!
Rating: 5

This book covers the whole theory of nonlinear regression. I think it is essential both for students of statistics and for scientists, not only as a study book but also as a reference book. I recommend it to those who already have had an introductory course on the subject and need to go deeper into it.


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[zidaan]

Bayesian Theory



By José M. Bernardo, Adrian F. M. Smith,
Publisher: Wiley
Number Of Pages: 586
Publication Date: 1994-01-15
Sales Rank: 1239899
ISBN / ASIN: 0471924164
EAN: 9780471924166
Binding: Hardcover
Manufacturer: Wiley
Studio: Wiley
Average Rating: 4

Recent books in the Wiley Series in Probability and Mathematical Statistics Editors Vic Barnett J. Stuart Hunter Adrian F.M. Smith Geoffrey S. Watson Ralph A. Bradley Joseph B. Kadane Stephen M. Stigler Nicholas I. Fisher David G. Kendall Jozef L. Teugels Optimal Design of Experiments Friedrich Pukelsheim, Universität Augsburg, Augsburg, Germany Optimal Design of Experiments presents the first complete theoretical development of optimal design for the linear model, a unified exposition that embraces a wide variety of design problems. It describes the statistical theory involved in designing experiments, and applies it to typical special cases. The design problems originating from statistics are solved using tools from linear algebra and convex analysis. The material is presented in a very clear, careful and organized way. Rather than assaulting traditional ways of thinking about optimal design, this book pulls together formerly separate entities to create a common framework for diverse design problems that share a common goal. Statisticians, mathematicians, engineers, and operations research specialists will find this book stimulating, challenging, and an asset to their work. 1993 Statistics for Spatial Data, Revised Edition Noel Cressie, Iowa State University, USA Designed for the scientific and engineering professional eager to exploit its enormous potential, Statistics for Spatial Data is a primer to the theory as well as the nuts-and-bolts of this influential technique. Focusing on the three areas of geostatistical data, lattice data, and point patterns, the book sheds light on the link between data and model, and reveals how spatial statistical models can be used to solve a host of problems in science and engineering. The previous edition was hailed by Mathematical Reviews as “an excellent book which…will become a basic reference”. Revised to reflect state-of-the-art developments, this edition also features many detailed examples, numerous illustrations, and over 1000 references. The first fully comprehensive introduction, Statistics for Spatial Data is an essential guide for professionals in biology, earth sciences, civil, electrical and agricultural engineering, geography, epidemiology, and ecology. 1993

Review:
For rich, subjectivist true believers only
Bernardo and Smith(BS)have written a book that assumes that Frank Ramsey, Bruno De Finetti,and Leonard Savage solved all of the major problems concerning the foundations of probability and decision theory in the period between 1931,the year Ramsey’s major essay on probability was published,and 1954,the year that Savage published his book.All that remains is a mopping up effort at minor,residual anomalies.The basic point made by BS is that all probabilities are precise,single number, point estimates or that they can be treated “as if” they were.Unfortunately,this is not the case.The subjectivist approach is applicable only in those situations where the purely deductive,mathematical laws of probability(the addition and multiplication rules for conjunction and disjunction)apply.This requires that a)there exists a complete sample space of all possible outcomes representing the choice problem before any probability is calculated;b)a complete preference ordering of all possible outcomes exists for the problem or c)a single,unique probability distribution is defined for the problem.Under these conditions,the probability calculus serves as a consistency and coherence check for the rational decision maker who is willing to bet on one side or another of all propositions.The subjectivist approach is a special theory with limited applicability.It is this failure to recognize that the subjective approach is a limiting case, that conflates the concepts of probability,logical probability,inductive probability,and degree of belief with mathematical probability, that is the source of much of the criticism of the subjectivist approach.There are many assertions made throughout the book that are highly dubious and/or unsupported.The rest of the review will be devoted to correcting these assertions.First,it is not the case that the Allais paradox choices are mistaken.It is strange to see it argued that such choices are similar to”…individuals(who)can often be shown to perform badly at deduction or long division”(BS,P.97).The real problem is that many/some decision makers have nonlinear probability preferences,as opposed to the linear probability preferences axiomatised by the subjectivists.The BS claim is similar to the claim made by many proponents of Euclidean geometry in the 18th and 19th centuries that non Euclidean geometries were erroneous and/or could not exist.Second,it is not the case that the Raiffa(1961) and Roberts(1963)replies to Ellsberg provide”…clear and convincing rejoinders to the Ellsberg criticisms”(BS,P.9.Both Raiffa and Roberts,like Savage in his belated reply toAllais ,simply restructured and changed the problem on which they commented.Third,the claim that the Ellsberg problems and/or examples(the two color and three color urn ball problems)are”…optical or magical illusions…” makes no sense.Fourth,the claim that “The logical(emphasis added)view is entirely lacking in operational content.” (BS,p.100),has no support at all.It is impossible to even talk about scientific theories unless an underlying logical conceptualization of probability is already in place beforehand.Fifth,the claim that John Maynard Keynes changed his view in 1931 and accepted the primacy of the subjectivist interpretation of F.Ramsey is erroneous.Keynes accepted Ramsey’s dutch book argument claim only if the deductive,purely mathematical laws of probability(”…the calculus of probability…”) were completely operational.Keynes completely rejected Ramsey’s assertions that habits and memory alone were the only foundations for induction and analogy.Sixth,BS are completely and totally ignorant about Keynes’s establishment of the interval estimate approach to probability in this century.It is a widespread misbelief on the part of many economists,philosophers,psychologists,etc.,that only partial, ordinal rankings,that could be made only part of the time,represents the main outcome of Keynes’s 16 years of study of probability.Nothing could be further from the truth.In fact,this misbelief is due to the acceptance by most scholars of the conclusions arrived at in the horrible mess made of Keynes’s book by Ramsey in both his 1922 and 1926 reviews,respectively.Ramsey’s unsupported claims about Keynes’s strange nonnumerical probabilities and mysterious logical relations are just that,unsupported.Most Keynesian probabilities have an upper and a lower bound or limit. It is in chapters 15 and 17 of Keynes’s 1921 A Treatise on Probability(TP) that BS can find Keynes’s “approximation” approach worked out in great detail.A number of problems are worked out by Keynes on pp.161-163 and pp.186-194 of the TP.All of these problems can now be solved using easier integer-mixed integer linear programming techniques.Keynes’s approach is fully operational.Seventh,the claim that Keynes’s logical approach provides “…no operational guidance as to how to choose…”(BS,p.99)makes it crystal clear to this reviewer that BS have never read Keynes’s TP.It is a great tragedy that books can be written on probability by authors that are grossly ignorant of basic literature.

Review:
A complete introduction to classical Bayesian analysis
[1] It is an excellent book on the classical Bayesian theory. The first author is a famous mathematician, who held several international conferences on Bayesian statistics.
[2] Similar to Berger’s book, it is also built on Statistical Decision Theory. In my opinion, Berger’s is a little better.
[3] The part of Bayesian foundation is heavy, maybe a topos today. But in the bookshelf, we indeed need such work.
[4] Think about the thickness of the bibliography — the reference is awesome!
[5] The history of Bayesian statistics is well overviewed.
[6] To learn more about the Bayesian computation, you need some complement books, such as Liu’s, Tanner’s, Gelman’s, etc.


Review:
Nice but….
Really nice book, but a VERY expensive “bible” if you ask me. $300, what a joke.

Date: 2004-01-23 Rating: 5
Review:
The Standard First Text To Begin Studying Bayesian Methods
This is an extremely nice introduction to Bayesian statistical methods. It takes you from the very basics – even who Thomas Bayes was (who happens to be buried in Bunhill Fields cemetery in London with William Blake (Songs of Innocence and Experience, Jerusalem), Daniel Defoe (Robinson Crusoe), John Bunyan (Pilgrim’s Progress)).
Its chapters are divided into sections forming an Introduction, Foundations, Generalizations, Modeling, Inference, and Remodeling. There is also a section summarizing the basic formulae and alternative non-Bayesian approaches. A rich reference list, subject index, and author index are also provided.
If you are familiar with the math of undergraduate statistics you should not have a problem with the math notation in this book. This really is the standard text you find on most shelves of folks who are familiar with this subject. There are many books to read beyond this one, but this is a fine place to start


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[zidaan]

Introduction to Linear Models and Statistical Inference



By Steven J. Janke, Frederick Tinsley,
Publisher: Wiley-Interscience
Number Of Pages: 600
Publication Date: 2005-07-20
Sales Rank: 1772679
ISBN / ASIN: 0471662593
EAN: 9780471662594
Binding: Hardcover
Manufacturer: Wiley-Interscience
Studio: Wiley-Interscience

A multidisciplinary approach that emphasizes learning by analyzing real-world data sets
This book is the result of the authors’ hands-on classroom experience and is tailored to reflect how students best learn to analyze linear relationships. The text begins with the introduction of four simple examples of actual data sets. These examples are developed and analyzed throughout the text, and more complicated examples of data sets are introduced along the way. Taking a multidisciplinary approach, the book traces the conclusion of the analyses of data sets taken from geology, biology, economics, psychology, education, sociology, and environmental science.
As students learn to analyze the data sets, they master increasingly sophisticated linear modeling techniques, including:
* Simple linear models
* Multivariate models
* Model building
* Analysis of variance (ANOVA)
* Analysis of covariance (ANCOVA)
* Logistic regression
* Total least squares
The basics of statistical analysis are developed and emphasized, particularly in testing the assumptions and drawing inferences from linear models. Exercises are included at the end of each chapter to test students’ skills before moving on to more advanced techniques and models. These exercises are marked to indicate whether calculus, linear algebra, or computer skills are needed.
Unlike other texts in the field, the mathematics underlying the models is carefully explained and accessible to students who may not have any background in calculus or linear algebra. Most chapters include an optional final section on linear algebra for students interested in developing a deeper understanding.
The many data sets that appear in the text are available on the book’s Web site. The MINITAB(r) software program is used to illustrate many of the examples. For students unfamiliar with MINITAB(r), an appendix introduces the key features needed to study linear models.
With its multidisciplinary approach and use of real-world data sets that bring the subject alive, this is an excellent introduction to linear models for students in any of the natural or social sciences.

A multidisciplinary approach that emphasizes learning by analyzing real-world data sets This book is the result of the authors’ hands-on classroom experience and is tailored to reflect how students best learn to analyze linear relationships. The text begins with the introduction of four simple examples of actualdata sets. These examples are developed and analyzed throughout the text, and more complicated examples of data sets are introduced along the way. Taking a multidisciplinary approach, the book traces the conclusion of the analyses of data sets taken from geology, biology, economics, psychology, education, sociology, and environmental science. As students learn to analyze thedata sets , they master increasingly sophisticated linear modeling techniques, including: Simple linear models Multivariate models Model building Analysis of variance (ANOVA) Analysis of covariance (ANCOVA) Logistic regression Total least squares The basics of statistical analysis are developed and emphasized, particularly in testing the assumptions and drawing inferences from linear models. Exercises are included at the end of each chapter to test students’ skills before moving on to more advanced techniques and models. These exercises are marked to indicate whether calculus, linear algebra, or computer skills are needed. Unlike other texts in the field, the mathematics underlying the models is carefully explained and accessible to students who may not have any background in calculus or linear algebra. Most chapters include anoptional final section on linear algebra for students interested in developing a deeper understanding. The many data sets that appear in the text are available on the book’s Web site. The MINITAB(r) software program is used to illustrate many of the examples. For students unfamiliar with MINITAB(r), an appendix introduces the key features needed to study linear models. With its multidisciplinary approach and use of real-worlddata sets that bring the subject alive, this is an excellent introduction to linear models for students in any of the natural or social sciences


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[zidaan]

A Course in Mathematical Statistics, Second Edition




A Course in Mathematical Statistics, Second Edition
By George G. Roussas
Publisher: Academic Press
Number Of Pages: 572
Publication Date: 1997-02-28
ISBN-10 / ASIN: 0125993153
ISBN-13 / EAN: 9780125993159
Binding: Hardcover

A Course in Mathematical Statistics, Second Edition, contains enough material for a year-long course in probability and statistics for advanced undergraduate or first-year graduate students, or it can be used independently for a one-semester (or even one-quarter) course in probability alone. It bridges the gap between high and intermediate level texts so students without a sophisticated mathematical background can assimilate a fairly broad spectrum of the theorems and results from mathematical statistics. The coverage is extensive, and consists of probability and distribution theory, and statistical inference.
* Contains 25% new material
* Includes the most complete coverage of sufficiency
* Transformation of Random Vectors
* Sufficiency / Completeness / Exponential Families
* Order Statistics
* Elements of Nonparametric Density Estimation
* Analysis of Variance (ANOVA)
* Regression Analysis
* Linear Models

Summary: A decent text.
Rating: 3
This is not a bad text to have in your library but it is a bit rough to follow. Some of the proofs are left out which might otherwise help out understanding.

Summary: cryptic, brief, leaves you wanting much more…
Rating: 1
Unfortunately, this book was used as the textbook in a mathematical statistics class taught by the author at UC Davis, which I was enrolled in. I found the book to be extremely brief, cryptic, and failed to even begin to explain the subject at hand in any kind of depth. I am an engineering student and am accustomed to technical material. This is the kind of textbook that gives the sciences a bad image. Being rigorous does not imply that you must be cryptic. Statistics is an exciting subject and this book utterly fails to inspire, explain, and enlighten.

Summary: An excellent calculus-based statistics book.
Rating: 5
This is one of the best math books I have ever read. It’s level of difficulty falls somewhere between a lower-division and graduate course on statistics. I would recommend it for anyone who wants to understand the mathematical principles leading to the great results that are typically spoonfed to Freshman stat. students. Roughly 40% of the book reviews probability, while the remaining text draws on these chapters while developing the mathematical theory of statistics. I was pleased with the numerous concrete examples, and the numerous excercises which for the most part were similar to the examples. From reading this book I gained an appreciation for the subject, and it’s place in mathematics. I now consider statistics as one of the well-developed crown jewels of mathematics, as opposed to my previous view of it as a superficial branch of math that lacked rigor.


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[zidaan]

Observed Confidence Levels: Theory and Application


Observed Confidence Levels: Theory and Application
By Alan M. Polansky

Publisher: Chapman & Hall/CRC
Number Of Pages: 288
Publication Date: 2007-10-26
ISBN-10 / ASIN: 1584888024
ISBN-13 / EAN: 9781584888024
Binding: Hardcover

Illustrating a simple, novel method for solving an array of statistical problems, Observed Confidence Levels: Theory and Application describes the basic development of observed confidence levels, a methodology that can be applied to a variety of common multiple testing problems in statistical inference. It focuses on the modern nonparametric framework of bootstrap-based estimates, allowing for substantial theoretical development and for relatively simple solutions to numerous interesting problems.
After an introduction, the book develops the theory and application of observed confidence levels for general scalar parameters, vector parameters, and linear models. It then examines nonparametric problems often associated with smoothing methods, including nonparametric density estimation and regression. The author also describes applications in generalized linear models, classical nonparametric statistics, multivariate analysis, and survival analysis as well as compares the method of observedconfidence levels to hypothesis testing, multiple comparisons, and Bayesian posterior probabilities. In addition, the appendix presents some background material on the asymptotic expansion theory used in the book.
Helping you choose the most reliable method for a variety of problems, this book shows how observed confidence levels provide useful information on the relative truth of hypotheses in multiple testing problems.


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Statistical Design and Analysis of Experiments, with Applications to Engineering and Science



Statistical Design and Analysis of Experiments, with Applications to Engineering and Science
Wiley-Interscience | 2003-02-14 |ISBN: 0471372161 | 760 pages | PDF | 4 mb

"...can really provide useful information for the intended audience..." (Zentralblatt Math, Vol. 1029, 2004)
“...a practitioner’s guide to statistical methods for designing and analyzing experiments...” (Quarterly of Applied Mathematics, Vol. LXI, No. 3, September 2003)

"...a perfect desktop reference..." (Technometrics, Vol. 45, No. 3, August 2003)

Zentralblatt Math, Vol. 1029, 2004
"...can really provide useful information for the intended audience..."






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Applied Probability and Stochastic Processes




Richard M. Feldman, Ciriaco Valdez-Flores, "Applied Probability and Stochastic Processes"
Publisher:Springer | ISBN: 3642051553 | 2009 | PDF | 397 pages | 5.1 Mb PDF






Product Description:

This book presents applied probability and stochastic processes in an elementary but mathematically precise manner, with numerous examples and exercises to illustrate the range of engineering and science applications of the concepts. The book is designed to give the reader an intuitive understanding of probabilistic reasoning, in addition to an understanding of mathematical concepts and principles. The initial chapters present a summary of probability and statistics and then Poisson processes, Markov chains, Markov processes and queuing processes are introduced. Advanced topics include simulation, inventory theory, replacement theory, Markov decision theory, and the use of matrix geometric procedures in the analysis of queues.

Included in the second edition are appendices at the end of several chapters giving suggestions for the use of Excel in solving the problems of the chapter. Also new in this edition are an introductory chapter on statistics and a chapter on Poisson processes that includes some techniques used in risk assessment. The old chapter on queues has been expanded and broken into two new chapters: one for simple queuing processes and one for queuing networks. Support is provided through the web site http://apsp.tamu.edu where students will have the answers to odd numbered problems and instructors will have access to full solutions and Excel files for homework.






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Linear Model Methodology




Andre I. Khuri, "Linear Model Methodology"
Chapman & Hall/CRC | 2009 | ISBN: 1584884819 | 562 pages | PDF | 2,9 MB

Given the importance of linear models in statistical theory and experimental research, a good understanding of their fundamental principles and theory is essential. Supported by a large number of examples, Linear Model Methodology provides a strong foundation in the theory of linear models and explores the latest developments in data analysis.

After presenting the historical evolution of certain methods and techniques used in linear models, the book reviews vector spaces and linear transformations and discusses the basic concepts and results of matrix algebra that are relevant to the study of linear models. Although mainly focused on classical linear models, the next several chapters also explore recent techniques for solving well-known problems that pertain to the distribution and independence of quadratic forms, the analysis of estimable linear functions and contrasts, and the general treatment of balanced random and mixed-effects models. The author then covers more contemporary topics in linear models, including the adequacy of Satterthwaite’s approximation, unbalanced fixed- and mixed-effects models, heteroscedastic linear models, response surface models with random effects, and linear multiresponse models. The final chapter introduces generalized linear models, which represent an extension of classical linear models.

Linear models provide the groundwork for analysis of variance, regression analysis, response surface methodology, variance components analysis, and more, making it necessary to understand the theory behind linear modeling. Reflecting advances made in the last thirty years, this book offers a rigorous development of the theory underlying linear models.


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Handbook of Statistical Analysis and Data Mining Applications





Robert Nisbet, John Elder IV, Gary Miner, "Handbook of Statistical Analysis and Data Mining Applications"
Academic Press | 2009 | ISBN: 0123747651 | 864 pages | PDF | 32,5 MB

The Handbook of Statistical Analysis and Data Mining Applications is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers (both academic and industrial) through all stages of data analysis, model building and implementation. The Handbook helps one discern the technical and business problem, understand the strengths and weaknesses of modern data mining algorithms, and employ the right statistical methods for practical application. Use this book to address massive and complex datasets with novel statistical approaches and be able to objectively evaluate analyses and solutions. It has clear, intuitive explanations of the principles and tools for solving problems using modern analytic techniques, and discusses their application to real problems, in ways accessible and beneficial to practitioners across industries - from science and engineering, to medicine, academia and commerce. This handbook brings together, in a single resource, all the information a beginner will need to understand the tools and issues in data mining to build successful data mining solutions.


Written "By Practitioners for Practitioners"
Non-technical explanations build understanding without jargon and equations
Tutorials in numerous fields of study provide step-by-step instruction on how to use supplied tools to build models using Statistica, SAS and SPSS software
Practical advice from successful real-world implementations
Includes extensive case studies, examples, MS PowerPoint slides and datasets
CD-DVD with valuable fully-working 90-day software included: "Complete Data Miner - QC-Miner - Text Miner" bound with book


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An Introduction to Multivariate Statistical Analysis (Wiley Series in Probability and Statistics)




T. W. Anderson "An Introduction to Multivariate Statistical Analysis (Wiley Series in Probability and Statistics)"
Wiley-Interscience | English | 2003-07-25 | ISBN: 0471360910 | 752 pages | DJVU | 7,8 MB


Perfected over three editions and more than forty years, this field- and classroom-tested reference:
* Uses the method of maximum likelihood to a large extent to ensure reasonable, and in some cases optimal procedures.
* Treats all the basic and important topics in multivariate statistics.
* Adds two new chapters, along with a number of new sections.
* Provides the most methodical, up-to-date information on MV statistics available.


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Linear Models with R (Texts in Statistical Science)




Julian J. Faraway "Linear Models with R (Texts in Statistical Science)"
Chapman & Hall/CRC | English | 2004-07-26 | ISBN: 1584884258 | 240 pages | PDF | 4,2 MB


This textbook focuses on the practice of regression and analysis of variance. Readers will learn which methods are available and the various situations in which they can be applied. Numerous examples clarify the use of the techniques and demonstrate what conclusions can be made. The author places less emphasis on mathematical theory, partly because some prior knowledge is assumed and partly because the issues are better tackled elsewhere. An interesting aspect of this book is the author's emphasis on statistical theory and qualitative aspects of the topic. He highlights the importance of data analysis and stresses its inportance through use of the inclusion of R software.

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The EM Algorithm and Extensions




The EM Algorithm and Extensions
360 pages | Wiley-Interscience; 2 edition (March 14, 200 | 0471201707 | PDF | 16 Mb

The only single-source——now completely updated and revised——to offer a unified treatment of the theory, methodology, and applications of the EM algorithm

Complete with updates that capture developments from the past decade, The EM Algorithm and Extensions, Second Edition successfully provides a basic understanding of the EM algorithm by describing its inception, implementation, and applicability in numerous statistical contexts. In conjunction with the fundamentals of the topic, the authors discuss convergence issues and computation of standard errors, and, in addition, unveil many parallels and connections between the EM algorithm and Markov chain Monte Carlo algorithms. Thorough discussions on the complexities and drawbacks that arise from the basic EM algorithm, such as slow convergence and lack of an in-built procedure to compute the covariance matrix of parameter estimates, are also presented.

While the general philosophy of the First Edition has been maintained, this timely new edition has been updated, revised, and expanded to include:
New chapters on Monte Carlo versions of the EM algorithm and generalizations of the EM algorithm
New results on convergence, including convergence of the EM algorithm in constrained parameter spaces
Expanded discussion of standard error computation methods, such as methods for categorical data and methods based on numerical differentiation
Coverage of the interval EM, which locates all stationary points in a designated region of the parameter space
Exploration of the EM algorithm's relationship with the Gibbs sampler and other Markov chain Monte Carlo methods
Plentiful pedagogical elements—chapter introductions, lists of examples, author and subject indices, computer-drawn graphics, and a related Web site

The EM Algorithm and Extensions, Second Edition serves as an excellent text for graduate-level statistics students and is also a comprehensive resource for theoreticians, practitioners, and researchers in the social and physical sciences who would like to extend their knowledge of the EM algorithm.

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Handbook of Exponential and Related Distributions for Engineers and Scientists





Handbook of Exponential and Related Distributions for Engineers and Scientists
Publisher: Chapman & Hall | 376 pages | November 21, 2005 | ISBN 1584881380 | PDF | 5 MB

The exponential distribution is one of the most important probability distributions in the theory and application of statistics. Despite its importance, however, most texts devote too little attention to it and specialized handbooks include too many mathematical details. In a straightforward and engaging manner, this handbook thoroughly examines the exponential, Gamma, Weibull, and Pareto distributions. It provides a comprehensive study of various estimation and testing methods and features numerous applications drawn from biology, engineering, and sociological studies including real data sets, numerical examples, figures and tables.

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