free statistical eBook

Andrea Bacciotti, Lionel Rosier "Liapunov Functions and Stability in Control Theory "
Springer | 1999-12-31 | ISBN:3540213325 | 238 pages | PDF | 1,7 Mb


This book presents a modern and self-contained treatment of the Liapunov method for stability analysis, in the framework of mathematical nonlinear control theory. A Particular focus is on the problem of the existence of Liapunov functions (converse Liapunov theorems) and their regularity, whose interest is especially motivated by applications to automatic control. Many recent results in this area have been collected and presented in a systematic way. Some of them are given in extended, unified versions and with new, simpler proofs. In the 2nd edition of this successful book several new sections were added and old sections have been improved, e.g., about the Zubovs method, Liapunov functions for discontinuous systems and cascaded systems. Many new examples, explanations and figures were added making this book accessible and well readable for engineers as well as mathematicians.

Aris Spanos “Probability Theory and Statistical Inference: Econometric Modeling with Observational Data "
Cambridge University Press | 1999-10-13 | ISBN: 0521413540 | 844 pages | PDF | 5,2 Mb

This major new textbook is intended for students taking introductory courses in probability theory and statistical inference. The primary objective of this book is to establish the framework for the empirical modeling of observational (nonexperimental) data. The text is extremely student friendly, with pathways designed for semester usage, and although aimed primarily at students at second-year undergraduate level and above studying econometrics and economics, Probability Theory and Statistical Inference will also be useful for students in other disciplines that make extensive use of observational data, including finance, biology, sociology and psychology.

Linear Mixed Models: A Practical Guide Using Statistical Software
by Brady West, Kathleen B. Welch, Andrzej T Galecki


Publisher: Chapman & Hall/CRC
Number Of Pages: 376
Publication Date: 2006-11-22
ISBN-10 / ASIN: 1584884800
ISBN-13 / EAN: 9781584884804
Binding: Hardcover


Book Description:

Simplifying the often confusing array of software programsconceptsdatasoftware packages: SAS, SPSS, Stata, R/S-plus, and HLM. The authors introduce basic theoretical concepts, present a heuristic approach to fitting LMMs based on both general and hierarchical model specifications, develop the model-building process step-by-step, and demonstrate the estimation, testing, and interpretation of fixed-effect parameters and covariance parameters associated with random effects. These concepts are illustrated through examples using real-world data sets that enable comparisons of model fitting options and results across the software procedures. The book also gives an overview of important options and features available in each procedure. Making popular software procedures for fitting LMMs easy-to-use, this valuable resource shows how to perform LMM analyses and provides a clear explanation of mixed modeling techniques and theories.. This easy-to-navigate reference details the use of procedures for fitting LMMs in five popular statistical , notation, software implementation, model interpretation, and visualization of clustered and longitudinal for fitting linear mixed models (LMMs), Linear Mixed Models: A Practical Guide Using Statistical Software provides a basic introduction to primary

Anirban DasGupta, "Asymptotic Theory of Statistics and Probability"
Springer | ISBN:0387759700 | March 28, 2008 | 696 pages | PDF | 4 MB

This book is an encyclopedic treatment of classic as well as contemporary large sample theory, dealing with both statistical problems and probabilistic issues and tools. It is written in an extremely lucid style, with an emphasis on the conceptual discussion of the importance of a problem and the impact and relevance of the theorems. The book has 34 chapters over a wide range of topics, nearly 600 exercises for practice and instruction, and another 300 worked out examples. It also includes a large compendium of 300 useful inequalities on probability, linear algebra, and analysis that are collected together from numerous sources, as an invaluable reference for researchers in statistics, probability, and mathematics.
It can be used as a graduate text, as a versatile research reference, as a source for independent reading on a wide assembly of topics, and as a window to learning the latest developments in contemporary topics. The book is unique in its detailed coverage of fundamental topics such as central limit theorems in numerous setups, likelihood based methods, goodness of fit, higher order asymptotics, as well as of the most modern topics such as the bootstrap, dependent data, Bayesian asymptotics, nonparametric density estimation, mixture models, and multiple testing and false discovery. It provides extensive bibliographic references on all topics that include very recent publications.

Design and Analysis of Experiments (Springer Texts in Statistics)
Summary:

Angela M. Dean, Daniel Voss, «Design and Analysis of Experiments»
Springer | ISBN 0387985611 | 2000 Year | PDF | 3,28 Mb | 740 Pages
The design and analysis of experiments is an essential part of investigation and discovery in science, of process and product improvement in manufacturing, and of comparison of competing protocols or treatments in the applied sciences. This book offers a step by step guide to the experimental planning process and the ensuing analysis of normally distributed data. Design and Analysis of Experiments emphasizes the practical considerations governing the design of an experiment based on the objectives of the study and a solid statistical foundation for the analysis. Almost all data sets in the book have been obtained from real experiments, either run by students in statistics and the applied sciences, or published in the scientific literature. Details of the planning stage of numerous different experiments are discussed. The statistical analysis of experimental data is based on estimable functions and is developed with some care. Design and Analysis of Experiments starts with basic principles and techniques of experimental design and analysis of experiments. It provides a checklist for the planning of experiments, and explains the estimation of treatment contrasts and analysis of variance. These basics are then applied in a wide variety of settings. Designs covered include completely randomized designs, complete and incomplete block designs, row-column designs, single replicate designs with confounding, fractional factorial designs, response surface designs, and designs involving nested factors and factors with random effects, including split-plot designs. The book is accessible to all readers who have a good basic knowledge of expected values, confidence intervals and hypothesis tests. It is ideal for use in the classroom at both the senior undergraduate and the graduate level. A guide to the use of the SAS System computer

Table of Contents
Preface
1 Principles and Techniques
2 Planning Experiments
3 Designs with One Source of Variation
4 Inferences for Contrasts and Treatment Means
5 Checking Model Assumptions
6 Experiments with Two Crossed Treatment Factors
7 Several Crossed Treatment Factors
8 Polynomial Regression
9 Analysis of Covariance
10 Complete Block Designs
11 Incomplete Block Designs
12 Designs with Two Blocking Factors
13 Confounded Two-Level Factorial Experiments
14 Confounding in General Factorial Experiments
15 Fractional Factorial Experiments
16 Response Surface Methodology
17 Random Effects and Variance Components
18 Nested Models
19 Split-Plot Designs
A. Tables
Bibliography
Index of Authors
Index of Experiments
Index of Subjects

Size: 3.28 MB

Design and Analysis of Experiments Solutions Manual 6th edition
by Douglas C. Montgomery

Publisher: Wiley
Number Of Pages: 660
Publication Date: 2004-12-27
ISBN-10 / ASIN: 047148735X
ISBN-13 / EAN: 9780471487357
Binding: Hardcover


Product Description:

Now in its 6th edition, this bestselling professional reference has helped over 100,000 engineers and scientists with the success of their experiments.

Douglas Montgomery arms readers with the most effective approach for learning how to design, conduct, and analyze experiments that optimize performance in products and processes. He shows how to use statistically designed experiments to obtain information for characterization and optimization of systems, improve manufacturing processes, and design and develop new processes and products. You will also learn how to evaluate material alternatives in product design, improve the field performance, reliability, and manufacturing aspects of products, and conduct experiments effectively and efficiently.

Discover how to improve the quality and efficiency of working systems with this highly-acclaimed book. This 6th Edition:
Places a strong focus on the use of the computer, providing output from two software products: Minitab and DesignExpert.
Presents timely, new examples as well as expanded coverage on adding runs to a fractional factorial to de-alias effects.
Includes detailed discussions on how computers are currently used in the analysis and design of experiments.
Offers new material on a number of important topics, including follow-up experimentation and split-plot design.
Focuses even more sharply on factorial and fractional factorial design.



Summary: A good book of DOE but...
Rating: 4

This is the sixth edition and the book has a lot of typos, please, somebody has to correct them before the next edition.


Summary: Pretty Good Experimental Design Textbook
Rating: 5

I really liked reading and learning about Experimental Analysis and Design from this book. I think it is pretty well paced, and quite well illustrated with many examples. I addition, the author also provides an online space with further expansion to the ideas presented in the book, as well as digital resources to all the data used in solved examples, as well as question problems. I strongly recommend this book for anyone with moderate statistical backgrouns, wishing to learn about experimental design and analysis.


Summary: Decent Statistics Text
Rating: 4

I haven't used this much for a class yet but it is definitely better and less rigorous than other statistics books, which easily become dry and boring.


Summary: highly regarded text on topics important in both engineering and agriculture and many other areas of scientific research
Rating: 5

Doug Montgomery got excellent statistical training at VPI. He also has a wealth of practical experience from his consulting work. His books on regression, statistical design and response surfaces are all well written and understandable to engineers. This 4th edition published in 1997 still contains all the useful information on factorial and fractional factorial designs. Chapter 14 is a wonderful up-to-date chapter that covers important process optimization topics not often covered in traditional design of experiments books. This includes response surface methods, mixture experiments, evolutionary operation and Taguchi methods. It does not require high level mathematics.

See Experiments by Wu and Hamada if you want a high mathematical level of presentation.


Summary: EXCELLENCE
Rating: 5

Design and Analysis of Experiments
Dr. Montgomery has done his usual excellent job in presenting difficult material in an understandable manner. In addition to its valuable walk through theory, the text is filled with well-executed and informative examples. The homework exercises are the best in his series of books!

10.8 MB
PDF

Probability Distributions Involving Gaussian Random Variables: A Handbook for Engineers and Scientists (The International Series in Engineering and Computer Science)
By Marvin K. Simon


Publisher: Springer
Number Of Pages: 224
Publication Date: 2002-05-01
ISBN-10 / ASIN: 1402070586
ISBN-13 / EAN: 9781402070587
Binding: Hardcover


Product Description:

This handbook is an essential reference for both professional researchers and academicians in the field of digital communications, including modulation, detection, multi-dimensional signal processing, image processing, pattern recognition, and antenna array processing. The distributions and associated statistics apply to mobile and cellular communications, vehicular technology, radar systems, spread spectrum systems, and optical communication systems. It will also be of interest to those involved in probabilistic and stochastic analysis and modeling. As a handbook, the results are presented in their final form without derivation or discussion. Also included at the end of the book are numerous curves illustrating the behavior of a variety of the probability measures presented in mathematical form. The book represents the best and most comprehensive compilation of such material currently available in the literature. Probability Distributions Involving Gaussian Random Variables: A Handbook for Engineers and Scientists brings together a vast and comprehensive collection of mathematical material in one location, as well as offering a variety of new results interpreted in a form that is particularly useful to engineers and scientists.



Summary: needs a CD version
Rating: 3

The Gaussian distribution is the heart of statistics. Small wonder, then, that over the years, many probability distributions have emerged in the literature, where these involve underlying random variables with Gaussian distributions.

One problem is that often these higher level distributions cannot be expressed analytically. Just like the problem with a basic Gaussian. So often, the distributions must be expressed in tabular form, as given by the book. In related wise, the book usefully furnishes graphs that let you understand distributions more readily.

All this is good. But the numerical nature of much of the book would lend itself more conveniently to it being presented in a CD. These days, when a reader is confronted with a table of values in a book, is she expected to type these in by hand into her computer?

4.5 MB Rar'd PDF

Analyzing Linguistic Data: A Practical Introduction to Statistics using R
By R. H. Baayen


Publisher: Cambridge University Press
Number Of Pages: 368
Publication Date: 2008-03-17
ISBN-10 / ASIN: 0521709180
ISBN-13 / EAN: 9780521709187
Binding: Paperback


Product Description:

Statistical analysis is a useful skill for linguists and psycholinguists, allowing them to understand the quantitative structure of their data. This textbook provides a straightforward introduction to the statistical analysis of language. Designed for linguists with a non-mathematical background, it clearly introduces the basic principles and methods of statistical analysis, using 'R', the leading computational statistics programme. The reader is guided step-by-step through a range of real data sets, allowing them to analyse acoustic data, construct grammatical trees for a variety of languages, quantify register variation in corpus linguistics, and measure experimental data using state-of-the-art models. The visualization of data plays a key role, both in the initial stages of data exploration and later on when the reader is encouraged to criticize various models. Containing over 40 exercises with model answers, this book will be welcomed by all linguists wishing to learn more about working with and presenting quantitative data.

4661 KB rar pdf

Economic Forecasting
By Nicholas Carnot, Vincent Koen, Bruno Tissot


Publisher: Palgrave Macmillan
Number Of Pages: 384
Publication Date: 2005-10-21
ISBN-10 / ASIN: 1403936544
ISBN-13 / EAN: 9781403936547
Binding: Paperback


Book Description:

Economic Forecasting deals with macroeconomic forecasts from a global point of view. The focus is first on a large range of theories as well as empirical methods: business cycle analysis, time series methods, macroeconomic models, medium and long-run projections, fiscal and financial forecasts, and sectoral forecasting. In addition, the book addresses the main issues surrounding the use of forecasts (accuracy, communication challenges) and their policy implications. A synthetic overview of economic data and forecasting institutions is also provided.


type: rar'd pdf file
size: ~3MB

Bayesian Forecasting and Dynamic Models (Springer Series in Statistics)
By Mike West, Jeff Harrison


Publisher: Springer
Number Of Pages: 680
Publication Date: 1999-03-26
ISBN-10 / ASIN: 0387947256
ISBN-13 / EAN: 9780387947259
Binding: Hardcover


Product Description:

The second edition of this book includes revised, updated, and additional material on the structure, theory, and application of classes of dynamic models in Bayesian time series analysis and forecasting. In addition to wide ranging updates to central material, the second edition includes many more exercises and covers new topics at the research and application frontiers of Bayesian forecastings.



Summary: A really good way to master Dinamic linear models
Rating: 5

As a reader with an economical background, mathematical texts are usually hard to be followed. Nevertheless, dinamic models through bayesian forecasting are afordable with this book. Introductory chapters on the bayesian learning algorithm and univariate models rough out the kernel of the issue. Once you dive into the following more complicated chapters you can get lost but the main idea is got. To avoid getting lost, several readings are necessary. Finally, last chapters for non linear models, models with exponential distributions and MCMC methods are really heavy going but a light reading can allow you to get a general overview.

All in all, is a great workbook. The main drawback may be the lack of more practical examples to illustrate the theoretical concepts.

pdf, 4.55MB

Contributions to Probability and Statistics: Applications and Challenges: Proceedings of the International Statistics Workshop
By Peter Brown, Shuangzhe Liu, Dharmendra Sharma


Publisher: World Scientific Publishing Company
Number Of Pages: 324
Publication Date: 2006-10-23
ISBN-10 / ASIN: 9812703918
ISBN-13 / EAN: 9789812703910
Binding: Hardcover



rar'd pdf
11.34 MB

Statistical Design (Springer Texts in Statistics)
By George Casella


Publisher: Springer
Number Of Pages: 312
Publication Date: 2008-04-03
ISBN-10 / ASIN: 0387759646
ISBN-13 / EAN: 9780387759647
Binding: Hardcover


Product Description:

Although statistical design is one of the oldest branches of statistics, its importance is ever increasing, especially in the face of the data flood that often faces statisticians. It is important to recognize the appropriate design, and to understand how to effectively implement it, being aware that the default settings from a computer package can easily provide an incorrect analysis. The goal of this book is to describe the principles that drive good design, paying attention to both the theoretical background and the problems arising from real experimental situations. Designs are motivated through actual experiments, ranging from the timeless agricultural randomized complete block, to microarray experiments, which naturally lead to split plot designs and balanced incomplete blocks.



2860 KB rared pdf

Adaptive Design Theory and Implementation Using SAS and R (Chapman & Hall/Crc Biostatistics)
By Mark Chang


Publisher: Chapman & Hall/CRC
Number Of Pages: 440
Publication Date: 2007-06-27
ISBN-10 / ASIN: 1584889624
ISBN-13 / EAN: 9781584889625
Binding: Hardcover


Book Description:

Adaptive design has become an important tool in modern pharmaceutical research and development. Compared to a classic trial design with static features, an adaptive design allows for the modification of the characteristics of ongoing trials based on cumulative information. Adaptive designs increase the probability of success, reduce costs and the time to market, and promote accurate drug delivery to patients.
Reflecting the state of the art in adaptive design approaches, Adaptive Design Theory and Implementation Using SAS and R provides a concise, unified presentation of adaptive design theories, uses SAS and R for the design and simulation of adaptive trials, and illustrates how to master different adaptive designs through real-world examples. The book focuses on simple two-stage adaptive designs with sample size re-estimation before moving on to explore more challenging designs and issues that include drop-loser, adaptive dose-funding, biomarker-adaptive, multiple-endpoint adaptive, response-adaptive randomization, and Bayesian adaptive designs. In many of the chapters, the author compares methods and provides practical examples of the designs, including those used in oncology, cardiovascular, and inflammation trials.
Equipped with the knowledge of adaptive design presented in this book, you will be able to improve the efficiency of your trial design, thereby reducing the time and cost of drug development.



Summary: Comprehensive, concise, unified presentation written by a hands-on statistician with years of adaptive design experiences
Rating: 5

There are explosions of adaptive design papers in past several years. This book alone has included about 400 references. It is very confusing to most new researchers in this field. This book use a unified approach to treat the major hypothesis test based adaptive design methods, i.e., view different methods as some forms of stagewise p-values combinations for test statistics. Chapter 1 provides overview of adaptive designs. Chapter 2 provides background for various clinical trials including superior, non-inferiority, equivalence and dose-response trials. The unified approach is presented in chapter 3 for stopping boundary determination, adjusted p-value, early futility and efficacy stopping, expected sample-size and clinical trial duration, conditional power, and futility index. All the formulations for these operating characteristics are presented in multiple-integration forms. In the next several chapters, all the integrations for the operating characteristics are carried out for particular combinations of p-values - lead to particular statistical methods for adaptive designs. In the most cases, the book avoid to using approaches from the original papers when the ideas were first proposed to avoid confusions and reduce the amount of material to be included. Chapter 7 presented another way (conditional error approach) to look at the common and different characteristics among different methods. Almost all methods for adaptive design can be reviewed as the conditional error approach. The difference is that each method uses a different conditional error function. In the chapter, different conditional error function and conditional power formulations are summarized. Chapter 8 discusses the recursive conditional error method so that it can be used for a K-stage adaptive design. Chapters 9 to 14 discuss different types of adaptive trials using the statistical methods that have been discussed in the previous chapters. These trials include sample-size reestimation, drop-loser design, biomarker adaptive design, response-adaptive randomization, adaptive treatment switch, and multiple endpoint issues. Chapters 15 and 16 discuss Bayesian adaptive approach for clinical trials. Chapter 17 talks about implementation issues. Chapter 18 is for readers who are interested in philosophical debates.

If you have not read too much adaptive design research papers, you wouldn't be confused, and you may not appreciate the unified approach in this book.

For most chapters, computer programs (SAS Macro) are provided with illustrated examples from clinical trials. However, it is not the author's intention to teach to how to implement adaptive design using SAS. The main purpose to include computer programs is to provide tools that you can use to design your adaptive trials since the software for adaptive design is very expensive (some reach [..]annual license for single user). It is not a computer book. Hence the algorithm of the computer program is usually not provided your clinical trials. However, each program is written with clear logic flows and is only about a page long. It should not be a challenge to most readers who have coding knowledge. The corresponding R functions are presented in Appendix. Because they are so similar between a SAS Macro and the corresponding R function. It is wise to put one of them in the appendix. The R functions cover the typical adaptive designs. Others can be directly translated from SAS macros without any difficulties.

There are exercises at end of chapters. Some are good, some are OK. This should be enhanced for the revision.

For some reasons, Amozon.com does not include the sample pages from the book. I am the author of book; I think it is helpful to use this feature to provide some insight for the readers. More information can be found online, where you can obtain the table of contents and the electronic computer programs. Rank it 5 starts that could be author's bias.


Summary: A SAS macro library with attached documentation and a few R functions appended
Rating: 1

The book, going by the table of contents, provides a fairly comprehensive overview of the field of adaptive designs in drug development.
After having read it, I am somewhat disappointed. The topics are in fact all there, and the different approaches are presented. There is no real overview on how the different approaches link together though.
I think that other texts like Ting (Dose Finding in Drug Development (Statistics for Biology and Health)) do a much better job at providing the background.

The code seems quite useful, but the typesetting is fairly disastrous. Most functions and macros have many parameters, and they are listed in floating text style instead of a tabular layout, making it very hard to read.
The code is typeset in proportional font (where monospace is standard) and does not contain any comments and documentation of particular blocks.

Finally, the text comprises 27 SAS programs and only 6 R programs. The SAS programs are in the corresponding chapters, the R programs are all put at the very end of the book in its own appendix chapter. The R code is of fairly low quality, suggesting that the author is a SAS user and transcribed the code into R.
Example of some typical and not so great R code:
for(i in 1:nStgs) { TSc[i] = 0}

So the benefit of the book might be in the SAS library. It is not in the introduction to adaptive design theory and certainly not in the small R library, making the title somewhat misleading.


Summary: excellent topic, well covered, with software for implementation
Rating: 5

This book just came out but I know a lot about it and about the author before I even got a copy. In November of last year Mark Chang coauthored a book in this Chapman and Hall series that I reviewed with praise because of the importance of the topic and the way it was demonstrated to work in a variety of real problems in pharmaceutical clinical trials. This book is even better as it goes more deeply into the methodology, the controversies and the results from simulation studies. Also it is much more practical because for every case where an application is given a SAS macro is also included to allow the reader to try the methodology for himself. In March of 2007 I actually designed a two-stage adaptive design with sample size reestimation for bioequivalence trials. I met mark at a conference where he presented much of his recent work and he was instrumental in helping me through his first book and his journal articles. This book had already gone to the publisher but he realized that this important design had overlooked. He added it when the copyedited version came to him. The design and the simulations related to it are very close to what I actually used. For those who like to program in R, he provides R code corresponding to each of the SAS macros that he gave. These programs make the new methodology readily available to interested users. The book is very comprehensive in that it covers a wide variety of applications for phase 2, phase 3 and combined phase trials. With the FDAs new initiative to speed up the drug discovery process this book will be an invaluable tool to statisticians in the pharmaceutical industry who would like to learn and apply these methods that along with the group sequential methodsare gaining favor within the FDA.


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Reuven Y. Rubinstein, Dirk P. Kroese “Simulation and the Monte Carlo Method "
Wiley-Interscience | 2007-12-19 | ISBN:0470177942 | 345 pages | PDF | 14 Mb

* The authoritative resource for understanding the power behind Monte Carlo Methods.
* Most ideas are introduced and explained by way of concrete examples, algorithms, and practical experiments
* A new co-author has now been added to enliven the writing style and to provide modern day expertise on new topics
* An extensive range of exercises is provided at the end of each chapter, with more difficult sections and exercises marked accordingly
* Examples of cross-entropy programs, written in MATLAB, are given in an appendix

How to Perform Statistical Tolerance Analysis (ASQC Basic References in Quality Control: Statistical Techniques, Vol 11) (Asqc Basic References in Quality Control : Statistical Techniques, Vol 11)
By Neil D. Cox


Publisher: Amer Society for Quality
Number Of Pages: 55
Publication Date: 1986-10-01
ISBN-10 / ASIN: 0873890108
ISBN-13 / EAN: 9780873890106
Binding: Paperback


Book Description:

This book describes a practical tool to help problem solvers and system designers in a wide variety of settings. The fact that tolerance analysis is known by various other names in various settings - propagation of error, moment propagation, and uncertainty analysis - attests to the wide applicability of this methodology for dealing with random input variables that determine the value of an output variable under consideration.

The author shows the derivation of the method's mathematical foundation, shows potential applications, and gives fully developed numerical results for the applications, which are as diverse as analysis of manufacturing tolerances, scheduling of facilities, inventory loss evaluation, product performance as affected by component performance, and profitability analysis.

If you are already familiar with tolerance analysis under any of its various names, read this book for a clearer understanding, a review of the derivation, and an idea of extended applications. If you are unfamiliar, by all means, read this book.

SAS Programming I : Essentials Course Notes

Publisher: Sas Inst
Number Of Pages:
Publication Date: 2000-12
ISBN-10 / ASIN: 158025649X
ISBN-13 / EAN: 9781580256490
Binding: Paperback

File-Size: 3,14 MB