Advanced Mathematical and Computational Tools in Metrology and Testing: Amctm VIII



Advanced Mathematical and Computational Tools in Metrology and Testing: Amctm VIII (Series on Advances in Mathematics for Applied Sciences) By Franco Pavese
Publisher: World Scientific Publishing Company 2009-04-15 | 424 Pages | ISBN: 9812839518 | PDF | 10.4 MB

he main theme of the AMCTM 2008 conference, reinforced by the establishment of IMEKO TC21, was to provide a central opportunity for the metrology and testing community worldwide to engage with applied mathematicians, statisticians and software engineers working in the relevant fields.

This review volume consists of reviewed papers prepared on the basis of the oral and poster presentations of the Conference participants. It covers all the general matters of advanced statistical modeling (e.g. uncertainty evaluation, experimental design, optimization, data analysis and applications, multiple measurands, correlation, etc.), metrology software (e.g. engineering aspects, requirements or specification, risk assessment, software development, software examination, software tools for data analysis, visualization, experiment control, best practice, standards, etc.), numerical methods (e.g. numerical data analysis, numerical simulations, inverse problems, uncertainty evaluation of numerical algorithms, applications, etc.), and data fusion techniques and design and analysis of inter-laboratory comparisons.


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Statistical Power with Missing Data: A Structural Equation Modeling Approach



Ian Press – Statistical Power with Missing Data: A Structural Equation Modeling Approach
Taylor and Francis (2009-08-20) | ISBN: 0805863699, 0805863702 | 408 pages | PDF | 4,3 MB
Statistical power analysis has revolutionized the ways in which we conduct and evaluate research. Similar developments in the statistical analysis of incomplete (missing) data are gaining more widespread applications. This volume brings statistical power and incomplete data together under a common framework, in a way that is readily accessible to those with only an introductory familiarity with structural equation modeling. It answers many practical questions such as:

how missing data affects the statistical power in a study
how much power is likely with different amounts and types of missing data
how to increase the power of a design in the presence of missing data, and
how to identify the most powerful design in the presence of missing data.

Points of Reflection encourage readers to stop and test their understanding of the material. Try Me sections test one’s ability to apply the material. Troubleshooting Tips help to prevent commonly encountered problems. Exercises reinforce content and Additional Readings provide sources for delving more deeply into selected topics. Numerous examples demonstrate the book’s application to a variety of disciplines. Each issue is accompanied by its potential strengths and shortcomings and examples using a variety of software packages (SAS, SPSS, Stata, LISREL, AMOS, and MPlus). Syntax is provided using a single software program to promote continuity but in each case, parallel syntax using the other packages is presented in appendixes. Routines, data sets, syntax files, and links to student versions of software packages are found at www.psypress.com/davey. The worked examples in Part 2 also provide results from a wider set of estimated models. These tables, and accompanying syntax, can be used to estimate statistical power or required sample size for similar problems under a wide range of conditions.

Class-tested at Temple, Virginia Tech, and Miami University of Ohio, this brief text is an ideal supplement for graduate courses in applied statistics, statistics II, intermediate or advanced statistics, experimental design, structural equation modeling, power analysis, and research methods taught in departments of psychology, human development, education, sociology, nursing, social work, gerontology and other social and health sciences. The book’s applied approach will also appeal to researchers in these areas. Sections covering Fundamentals, Applications, and Extensions are designed to take readers from first steps to mastery.

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Experts in Uncertainty: Opinion and Subjective Probability in Science



Roger M. Cooke, "Experts in Uncertainty: Opinion and Subjective Probability in Science"
Oxford University Press, USA | ISBN: 0195064658 | 1991 | 336 pages | PDF | 14,4 MB

Product Description

This book is an extensive survey and critical examination of the literature on the use of expert opinion in scientific inquiry and policy making. The elicitation, representation, and use of expert opinion is increasingly important for two reasons: advancing technology leads to more and more complex decision problems, and technologists are turning in greater numbers to "expert systems" and other similar artifacts of artificial intelligence. Cooke here considers how expert opinion is being used today, how an expert's uncertainty is or should be represented, how people do or should reason with uncertainty, how the quality and usefulness of expert opinion can be assessed, and how the views of several experts might be combined. He argues for the importance of developing practical models with a transparent mathematic foundation for the use of expert opinion in science, and presents three tested models, termed "classical," "Bayesian," and "psychological scaling." Detailed case studies illustrate how they can be applied to a diversity of real problems in engineering and planning.

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Modern Multivariate Statistical Techniques: Regression, Classification, and Manifold Learning by Izenman



Modern Multivariate Statistical Techniques: Regression, Classification, and Manifold Learning (Springer Texts in Statistics) by Alan Julian Izenman
Publisher: Springer | August 28, 2008 | ISBN: 0387781889 | Pages: 734 | PDF | 22.33 MB

Remarkable advances in computation and data storage and the ready availability of huge data sets have been the keys to the growth of the new disciplines of data mining and machine learning, while the enormous success of the Human Genome Project has opened up the field of bioinformatics.

These exciting developments, which led to the introduction of many innovative statistical tools for high-dimensional data analysis, are described here in detail. The author takes a broad perspective; for the first time in a book on multivariate analysis, nonlinear methods are discussed in detail as well as linear methods. Techniques covered range from traditional multivariate methods, such as multiple regression, principal components, canonical variates, linear discriminant analysis, factor analysis, clustering, multidimensional scaling, and correspondence analysis, to the newer methods of density estimation, projection pursuit, neural networks, multivariate reduced-rank regression, nonlinear manifold learning, bagging, boosting, random forests, independent component analysis, support vector machines, and classification and regression trees. Another unique feature of this book is the discussion of database management systems.

This book is appropriate for advanced undergraduate students, graduate students, and researchers in statistics, computer science, artificial intelligence, psychology, cognitive sciences, business, medicine, bioinformatics, and engineering. Familiarity with multivariable calculus, linear algebra, and probability and statistics is required. The book presents a carefully-integrated mixture of theory and applications, and of classical and modern multivariate statistical techniques, including Bayesian methods. There are over 60 interesting data sets used as examples in the book, over 200 exercises, and many color illustrations and photographs.

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The Principles of Experimental Research




The Principles of Experimental Research
Publisher: Butterworth-Heinemann | Pages: 432 | 2005-12-15 | ISBN: 0750679263 | PDF | 14 MB

The need to understand how to design and set up an investigative experiment is nearly universal to all students in engineering, applied technology and science, as well as many of the social sciences. Many schools offer courses in this fundamental skill and this book is meant to offer an easily accessible introduction to the essential tools needed, including an understanding of logical processes, how to use measurement, the do's and don'ts of designing experiments so as to achieve reproducible results and the basic mathematical underpinnings of how data should be analyzed and interpreted. The subject is also taught as part of courses on Engineering statistics, Quality Control in Manufacturing, and Senior Design Project, in which conducting experimental research is usually integral to the project in question.

* Covers such essential fundamentals as "definitions," "quantification," and standardization of test materials

* Shows students and professionals alike how to plan an experiment-from how to frame a proper Hypothesis to designing an experiment to accurately reflect the nature of the problem to "designing with factors."

* Includes a separate section on the use of Statistics in Experimental Research, including overview of probability and statistics, as well as Randomization, Replication and Sampling, as well as proper ways to draw statistical inferences from experimental data.

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Statistical Rules of Thumb (Wiley Series in Probability and Statistics)
By Gerald van Belle


Publisher: Wiley-Interscience
Number Of Pages: 272
Publication Date: 2008-09-02
ISBN-10 / ASIN: 0470144483
ISBN-13 / EAN: 9780470144480
Binding: Paperback


Statistical Rules of Thumb, Second Edition compiles simple rules that are widely applicable, robust, and elegant, and each captures key statistical concepts. This handbook provides a framework for considering statistical questions such as sample size and design of experiments. Explaining the justification for each rule, this book conveys the various possibilities that statisticians must think of when designing and conducting a study or analyzing its data. It provides a framework for considering such aspects of statistical work such as: randomness and statistical models; sample size; covariation; epidemiology; environmental studies; designing, conducting, and analyzing studies; words, tables, and graphs; and consulting. New rules of thumb are included such as: Sample size for ratios of means; Very non-significant P-values are very significant; Dichotomize continuous variables for odds ratio analysis; and Correlations need to be substantial to gain advantage in ANCOVA. Some rules have been revised for the new edition, i.e. sample size for relative risk and sample size for percentage change. In addition, the references have been completed updated and expanded.

A related website www.vanbelle.org provides additional rules, author presentations and more.

Amazon.com Review:

Good scientists learn early on not to do everything by the book. Respected statistician Gerald Van Belle has compiled the unpublished wisdom of his profession in the invaluable Statistical Rules of Thumb. Those mathematicians involved in statistical work will applaud its clarity and organization, while other scientists will find their experimental design and analysis vastly improved by its suggestions. Each of the 99 rules has a brief introduction, a simple statement of the rule, illustrations, theoretical underpinnings, and extensions. Topics covered include covariation, design, consultation, epidemiology, and data representation. Van Belle is providing Web support for this ongoing project, so we can expect to see even greater breadth and refinement as it develops. –Rob Lightner


Summary: great idea, great reference book
Rating: 5

Gerald van Belle is a biostatistician and professor at the University of Washington. He has coauthored an excellent text on biostatistics with Lloyd Fisher. In this delightful and clearly written text van Belle provides 99 rules of thumb based on his vast experience as a consultant and researcher in statistics and biostatistics. For a statistician or a student of statistics along with the term “rule of thumb” one thinks of the three sigma rule or the use of range divided by sample size as a quick estimate of standard error of the mean. But this text is much more than a compilation of such simple rules. Professor van Belle organizes the book into topical chapters on sample size determination, covariation, epidemiology, environmental studies, design, conduct and analysis of experiments, tables and graphics, and consulting. Each rule is put into proper context and is justified with mathemetical theorems or empirical evidence.
Some of the rules are more like guidance for proper approaches to problems. For example in reviewing the basics in chapter 1 van Belle discusses the linear model in terms of the key assumptions of independence, equal variance and normality. The rule of thumb in section 1.4 states that assumptions should be considered in the order (1) independence, (2) equal variance and (3) normality. Van Belle explains this order by showing that the inferences are far more sensitive to violations in the independence assumption than in either the assumption of equal variance or the assumption of normality. As a statistician, I am aware of the sensitivity to correlation and the fact that variances need to differ by a factor of nearly four before results are seriously affected. Also when the data do not fit the normal distribution we have the nonparametric alternatives based on ranks. Nevertheless,in practice it is easiest and routine to test normality first, variances second and correlation becomes an afterthought. In some situations this may be okay since we may have good reason to believe that the observations were generated independently. But the rule is a good practical guidance. If you question all three assumptions it makes sense to test them in the order van Belle is suggesting.

Other practical advice of this type include the following rules of thumb:
1. Start with the Poisson to model incidence or prevalence.
2. Begin with the exponential model for time to event data.
3. Begin with two exponentials to compare two survival distributions.
4. Begin with the lognormal distribution in environmental studies.

These rules are not meant to suggest that simple models always work or even that they work in the majority of case. It is just that it is best to start simple and let the analysis and diagnostics tell you when more complicated models are needed.

This book will be a great guide for statistical practitioners and a terrific reference for professional and consulting statisticians. The references suppoting the rules are as valuable as the rules themselves.


Summary: Great value
Rating: 5

A very welcome book. Packed full of useful information in a very readable form. I ordered it for the one and half page about “Overlapping confidence intervals do not imply nonsignificance”. Have been overjoyed discovering the rest.

Summary: Extremely helpful and very readible
Rating: 5

Sometimes you don’t want to see a lot of “scary math notation” — you just want to know what a concept is and how to apply it. This book meets that desire. It takes all the theoretical math concepts and explains them in very readable and accessible language so that you won’t have post-traumatic flashbacks to your college statistics days.

This book provides concrete and practical advice on how to use statistical methods and how to make sure that you are using them correctly. The very first chapter (which you can read online) talks about sample size and provides simple and equations to use to design an experiment that will give you statistically valid results. That chapter is worth the purchase price itself.

Between the quality of the information, and the ease of reading, this book has got to be one of the best books out there for those who shudder at the mere mention of statistics.

Summary: Practical advice
Rating: 4

This is a useful book for the working statistician or consultant. Many questions arise in practice that are never covered in traditional textbooks, and with experience an applied statistician learns “rules of thumb”. Here is a text that nicely organizes some of the most common questions and problems and design considerations, with solid practical advice. This is not a text for a course (unless a course in consulting), but would serve an applied researcher or statistician well.

Summary: Excellent Book – though not for the uninitiated
Rating: 5

Excellent reference for statisticians. Only two complaints:
1) In many instances, wording is not clear – you have to really pick sentences apart to figure out what the author meant.
2) Reasons for a particular rule sometimes leave you wanting. But at least you’re introduced to the concept and can look elsewhere for assistance in understanding.

Also, the title might lead some non-statisticans to think that they can pick this book up and learn how to plug and chug in all sorts of situations. This is not the case.


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Everyday Probability a Statistics: Health, Elections, Gambling and War
By Michael M. Woolfson

Publisher: World Scientific Publishing Company
Number Of Pages: 223
Publication Date: 2008-07-30
ISBN-10 / ASIN: 1848160313
ISBN-13 / EAN: 9781848160316
Binding: Hardcover

Probability and statistics impinge on the life of the average person in a variety of ways — as is suggested by the title of this book. Very often, information is provided that is factually accurate but intended to present a biased view. This book presents the important results ofprobability and statistics without making heavy mathematical demands on the reader. It should enable an intelligent reader to properly assess statistical information and to understand that the same information can be presented in different ways.

Contents: The Nature of Probability; Combining Probabilities; A Day at the Races; Making Choices and Selections; Non-Intuitive Examples of Probability; Probability and Health; Combining Probabilities, The Craps Game Revealed; The UK National Lottery, Loaded Dice and Crooked Wheels; Block Diagrams; The Normal (or Gaussian) Distribution; Statistics — The Collection and Analysis of Numerical Data; The Poisson Distribution and Death by Horse Kicks; Predicting Voting Patterns; Taking Samples — How Many Fish in the Pond?; Differences — Rats and IQs; Crime is Increasing and Decreasing; My Uncle Joe Smoked 60 a Day; Chance, Luck and Making Decisions.

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Multivariable Model – Building: A Pragmatic Approach to Regression Anaylsis based on Fractional Polynomials for Modelling Continuous Variables (Wiley Series in Probability and Statistics)
By Patrick Royston, Willi Sauerbrei

Publisher: Wiley
Number Of Pages: 322
Publication Date: 2008-07-08
ISBN-10 / ASIN: 0470028424
ISBN-13 / EAN: 9780470028421
Binding: Hardcover


Multivariable regression models are of fundamental importance in all areas of science in which empirical data must be analyzed. This book proposes a systematicapproach to building such models based on standard principles of statistical modeling. The main emphasis is on the fractional polynomial method for modeling the influence of continuous variables in a multivariable context, a topic for which there is no standardapproach . Existing options range from very simple step functions to highly complex adaptive methods such as multivariate splines with many knots and penalisation. This newapproach , developed in part by the authors over the last decade, is a compromise which promotes interpretable, comprehensible and transportable models.


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Modern Applied U-Statistics (Wiley Series in Probability and Statistics)
By Jeanne Kowalski, Xin M. Tu


Publisher: Wiley
Number Of Pages: 378
Publication Date: 2007-12-19
ISBN-10 / ASIN: 0471682276
ISBN-13 / EAN: 9780471682271
Binding: Hardcover

A timely and applied approach to the newly discovered methods and applications of U-statistics

Built on years of collaborative research and academic experience, Modern Applied U-Statistics successfully presents a thorough introduction to the theory of U-statistics using in-depth examples andapplications that address contemporary areas of study including biomedical and psychosocial research. Utilizing a “learn by example” approach, this book provides an accessible, yet in-depth, treatment of U-statistics, as well as addresses key concepts in asymptotic theory by integrating translational and cross-disciplinary research.

The authors begin with an introduction of the essential and theoretical foundations of U-statistics such as the notion of convergence in probability and distribution, basic convergence results, stochastic Os, inference theory, generalized estimating equations, as well as the definition and asymptotic properties of U-statistics. With an emphasis on nonparametricapplications when and where applicable, the authors then build upon this established foundation in order to equip readers with the knowledge needed to understand the modern-day extensions of U-statistics that are explored in subsequent chapters. Additional topical coverage includes:

Longitudinal data modeling with missing data

Parametric and distribution-free mixed-effect and structural equation models

A new multi-response based regression framework for non-parametric statistics such as the product moment correlation, Kendall’s tau, and Mann-Whitney-Wilcoxon rank tests

A new class of U-statistic-based estimating equations (UBEE) for dependent responses

Motivating examples, in-depth illustrations of statistical and model-building concepts, and an extensive discussion of longitudinal study designs strengthen the real-world utility and comprehension of this book. An accompanying Web site features SAS? and S-Plus? program codes, softwareapplications , and additional study data. Modern Applied U-Statistics accommodates second- and third-year students of biostatistics at the graduate level and also serves as an excellent self-study for practitioners in the fields of bioinformatics and psychosocial research.


Summary: excellent introduction to U-statistics and their applications
Rating: 4

Jeanne Kowlaski with the help of Xin Tu provides a book that describes both the history and the applications of U-statistics. These statistics provide powerful nonparametric estimates and tests for many univariate and multivariate problems. The book does an excellent job of describing these statistical tools that are not commonly used even by statisticians. However, they are becoming more and more common in both theoretical and applied papers by specialists in nonparametrics.

As the applications grow in size and usefulness these tools will become important to be understood by every statistician.

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Missing Data in Longitudinal Studies: Strategies for Bayesian Modeling and Sensitivity Analysis (Monographs on Statistics and Applied Probability)
By Michael J. Daniels, Joseph W. Hogan

Publisher: Chapman & Hall/CRC
Number Of Pages: 328
Publication Date: 2008-03-11
ISBN-10 / ASIN: 1584886099
ISBN-13 / EAN: 9781584886099
Binding: Hardcover

Drawing from the authorsâ ™ own work and from the most recent developments in the field, Missing Data in Longitudinal Studies: Strategies for Bayesian Modeling and Sensitivity Analysis describes a comprehensive Bayesian approach for drawing inference from incomplete data in longitudinal studies. To illustrate these methods, the authors employ several data sets throughout that cover a range of study designs, variable types, andmissing data issues.
The book first reviews modern approaches to formulate and interpret regression models for longitudinal data. It then discusses key ideas in Bayesian inference, including specifying prior distributions, computing posterior distribution, and assessing model fit. The book carefully describes the assumptions needed to make inferences about a full-data distribution from incompletely observed data. For settings with ignorable dropout, it emphasizes the importance of covariance models for inference about the mean while for nonignorable dropout, the book studies a variety of models in detail. It concludes with three case studies that highlight important features of the Bayesian approach for handling nonignorable missingness.
With suggestions for further reading at the end of most chapters as well as many applications to the health sciences, this resource offers a unified Bayesian approach to handlemissing data in longitudinal studies.

Summary: great new book on the subject, good at theory and practice
Rating: 5
Issues of missing data in longitudinal studies are very important in the design and analysis of clinical trials. This is such an important statistical topic, that many excellent books have been written about it. One of the earliest and a landmark text was the book by Rubin and Little which was recently updated in the second edition. Mixed linear models for longitudinal data provide an effective method for dealing with several types of missingness as does multiple imputation. Pattern mixture models are also very useful. Molenberghs and Kennard, Verbeke and Molenberghs and Rubin all cover these topic well in their excellent texts.
What then is the advantage of this text by Daniels and Hogan?
1. It is slightly more current than the others
2. It combines theory and application very nicely
3. A series of seven real data sets from real clinical trials and epidemiologic studies are presented up front in Chapter 1 and used throughout to illustrate practical advantages and disadvantages of the various techniques covered in the latter chapters
4. It covers Bayesian modeling and sensitivity analysis in more depth that most of its competitors
Only Molenberghs and Kennard match it in the depth of coverage on theory and applications. But they do not provide the coverage of Bayesian methods the way Daniels and Hogan do.
For these reasons I recommend this book to the practicing biostatisticians working on clinical trials even if the texts listed below are alresdy on their bookshelves.
I) Diggle, P. J., Heagerty, P., Liang, K.-Y. and Zeger, S. L. (2002). “Analysis of Longitudinal Data” 2nd Edition. Oxfrod University Press, Oxford.
II) Fitzmaurice, G. M., Laird, N.M. and Ware, J. H. (2004). “Applied ongitudinal Analysis”. John Wiley & Sons, New York.
III) Little, R. J. A. and Rubin, D. B. (2002) “Statistical Analysis with Missing Data” 2nd Edition, John Wiley & Sons, New York
IV) Molenberghs, G and Kennard, M. G. (2007). “Missing Data in Clinical Studies” John Wiley & Sons, Chichester.
V) Molenberghs, G. and Verbeke, G. (2005). “Models for Discrete Longitudinal Data”. Springer-Verlag, New York.
VI) Pinheiro, J. C. and Bates, D. M. (2000). “Mixed Effects Models in S and S-Plus”. Springer-Verlag, New York.
VII) Rubin, D. B. (1987). “Multiple Imputation for Nonresponse in Surveys” John Wiley & Sons, New York.
VIII) Tsiatis, A. A. (2006). “Semiparametric Theory and Missing Data” Dpringer-Verlag, New York.
IX) Verbeke, G and Molenberghs, G. (1997). “Linear Mixed Models in Practice: A SAS-Oriented Approach” Springer-Verlag, New York.
X) Verbeke, G and Molenberghs, G. (2000). “Linear Mixed Models for Longitudinal Data” Springer-Verlag, New York.



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Density Estimation for Statistics and Data Analysis (Monographs on Statistics and Applied Probability)
By Bernard. W. Silverman

Publisher: Chapman & Hall/CRC
Number Of Pages: 176
Publication Date: 1986-04-01
ISBN-10 / ASIN: 0412246201
ISBN-13 / EAN: 9780412246203
Binding: Hardcover


Although there has been a surge of interest in density estimation in recent years, much of the published research has been concerned with purely technical matters with insufficient emphasis given to the technique’s practical value. Furthermore, the subject has been rather inaccessible to the general statistician. The account presented in this book places emphasis on topics of methodological importance, in the hope that this will facilitate broader practical application of density estimation and also encourage research into relevant theoretical work. The book also provides an introduction to the subject for those with general interests in statistics. The important role of density estimation as a graphical technique is reflected by the inclusion of more than 50 graphs and figures throughout the text. Several contexts in which density estimation can be used are discussed, including the exploration and presentation of data, nonparametric discriminant analysis, cluster analysis, simulation and the bootstrap, bump hunting, projection pursuit, and the estimation of hazard rates and other quantities that depend on the density. This book includes general survey of methods available for density estimation. The Kernel method, both for univariate and multivariate data, is discussed in detail, with particular emphasis on ways of deciding how much to smooth and on computation aspects. Attention is also given to adaptive methods, which smooth to a greater degree in the tails of the distribution, and to methods based on the idea of penalized likelihood.


Summary: beautifully written and concise
Rating: 5

I had the good fortune to take a short course from Bernie Silverman on density estimation just after this book came out in 1986. It is one of the clearest treatments of the subject and I found it particularly good on the coverage of optimal kernels.

It is also filled with good practical examples and advice. For instance, the Old Faithful data provides an excellent example of a bimodal distribution where kernel density estimation provides a way to detect the two modes.

The author was also very perceptive in recognizing the value of projection pursuit techniques and bootstrap methods and the way density estimation techniques relate to these methods.

The book has the virtue of being clear and concise.


Summary: excellent text on density estimation
Rating: 5

I had the good fortune to take a short course from Bernie Silverman on density estimation just after this book came out in 1986. It is one of the clearest treatments of the subject and I found it particularly good on the coverage of optimal kernels. It is also filled with good practical examples and advice. For instance, the Old Faithful data provides an excellent example of a bimodal distribution where kernel density estimation provides a way to detect the two modes.

The author was also very perceptive in recognizing the value of projection pursuit techniques and bootstrap methods and the way density estimation techniques relate to these methods.

The book has the virtue of being clear and concise.

Summary: Best book on this subject
Rating: 5

Quite a few books have been written since 1986, but this book is still the best. Very intuitive and very readable. It is written with a mastery of the subject and an excellent style of pedagogy. I remember of the joy and refreshness of reading this book around 1987 and it has served me well on a very important introductory of mordern statistics without having to go through tedious “math” notations and a shining example that statistics can be full of intuitive ideas and beautiful. For people unfamiliar with this book, it deals with probability density estimation using the idea of “local averages”, and so it does not deal with other techniques such as splines. Also it is purely a density estimation book, and does not deal with another important problem, namely regression estimation (on which there are many other books). In summary, this book introduces the ideas and sense of “smoothing”, a large (perhaps a little overblown) area of modern statistics. If you want to learn statistical smoothing, besides from Steve Marron, this one is the way to go.

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Statistical Meta-Analysis with Applications (Wiley Series in Probability and Statistics)
By Joachim Hartung, Guido Knapp, Bimal K. Sinha

Publisher: Wiley-Interscience
Number Of Pages: 248
Publication Date: 2008-08-11
ISBN-10 / ASIN: 0470290897
ISBN-13 / EAN: 9780470290897
Binding: Hardcover



This book combines the authors’ experiences on the topic and brings out a wealth of new information relevant to the study of meta-analysis.
Most of the methods described in this book can be understood and applied with a solid master’s level background in statistics.
Applications ranging from business to education to environment to health sciences in both univariate and multivariate cases are presented alongside and subservient to theory.
The treatment of the common mean of univariate normal populations, tests of homogeneity, one-way random effects model, categorical data, recovery of inter-block information, and combination of polls is entirely new.
A special feature includes the incorporation of detailed discussions on the computational aspects and related software to carry out statistical meta-analysis in practice.



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Basic Statistics: A Primer for the Biomedical Sciences,4th Edition



Olive Jean Dunn, Virginia A. Clark, "Basic Statistics: A Primer for the Biomedical Sciences,4th Edition"
Wiley | 2009 | ISBN: 0470248793 | 272 pages | PDF | 11,1 MB

New Edition of a Classic Guide to Statistical Applications in the Biomedical Sciences

In the last decade, there have been significant changes in the way statistics is incorporated into biostatistical, medical, and public health research. Addressing the need for a modernized treatment of these statistical applications, Basic Statistics, Fourth Edition presents relevant, up-to-date coverage of research methodology using careful explanations of basic statistics and how they are used to address practical problems that arise in the medical and public health settings. Through concise and easy-to-follow presentations, readers will learn to interpret and examine data by applying common statistical tools, such as sampling, random assignment, and survival analysis.

Continuing the tradition of its predecessor, this new edition outlines a thorough discussion of different kinds of studies and guides readers through the important, related decision-making processes such as determining what information is needed and planning the collections process. The book equips readers with the knowledge to carry out these practices by explaining the various types of studies that are commonly conducted in the fields of medical and public health, and how the level of evidence varies depending on the area of research. Data screening and data entry into statistical programs is explained and accompanied by illustrations of statistical analyses and graphs. Additional features of the Fourth Edition include:
A new chapter on data collection that outlines the initial steps in planning biomedical and public health studies
A new chapter on nonparametric statistics that includes a discussion and application of the Sign test, the Wilcoxon Signed Rank test, and the Wilcoxon Rank Sum test and its relationship to the Mann-Whitney U test
An updated introduction to survival analysis that includes the Kaplan Meier method for graphing the survival function and a brief introduction to tests for comparing survival functions
Incorporation of modern statistical software, such as SAS, Stata, SPSS, and Minitab into the presented discussion of data analysis
Updated references at the end of each chapter

Basic Statistics, Fourth Edition is an ideal book for courses on biostatistics, medicine, and public health at the upper-undergraduate and graduate levels. It is also appropriate as a reference for researchers and practitioners who would like to refresh their fundamental understanding of statistical techniques.

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Statistics II for Dummies



Statistics II for Dummies
Publisher: For Dummies | Pages: 408 | 2009-08-31 | ISBN 0470466464 | PDF | 6 MB

Need to expand your statistics knowledge and move on to Statistics II? This friendly, hands-on guide gives you the skills you need to take on multiple regression, analysis of variance (ANOVA), Chi-square tests, nonparametric procedures, and other key topics. Statistics II For Dummies also provides plenty of test-taking strategies as well as real-world applications that make data analysis a snap, whether you're in the classroom or at work.
Begin with the basics — review the highlights of Stats I and expand on simple linear regression, confidence intervals, and hypothesis tests
Start making predictions — master multiple, nonlinear, and logistic regression; check conditions; and interpret results
Analyze variance with ANOVA — break down the ANOVA table, one-way and two-way ANOVA, the F-test, and multiple comparisons
Connect with Chi-square tests — examine two-way tables and test categorical data for independence and goodness-of-fit
Leap ahead with nonparametrics — grasp techniques used when you can't assume your data has a normal distribution


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Maximum Probability Estimators and Related Topics



Jacob Wolfowitz, Lionel Weiss, "Maximum Probability Estimators and Related Topics"
Springer-Verlag | 1974 | ISBN: 0387069704 | 106 pages | Djvu | 1,1 MB




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Bonferroni-type Inequalities with Applications (Probability and its Applications)




Janos Galambos, Italo Simonelli, "Bonferroni-type Inequalities with Applications (Probability and its Applications)"
Springer | 1996 | ISBN: 0387947760 | 269 pages | PDF | 16,8 MB

This book presents a large variety of extensions of the methods of inclusion and exclusion. Both methods for generating and methods for proof of such inequalities are discussed. The inequalities are utilized for finding asymptotic values and for limit theorems. Applications vary from classical probability estimates to modern extreme value theory and combinatorial counting to random subset selection. Applications are given in prime number theory, growth of digits in different algorithms, and in statistics such as estimates of confidence levels of simultaneous interval estimation. The prerequisites include the basic concepts of probability theory and familiarity with combinatorial arguments.


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