Book Description

Modern Analysis provides coverage of real and abstract analysis, offering a sensible introduction to functional analysis as well as a thorough discussion of measure theory, Lebesgue integration, and related topics. This significant study clearly and distinctively presents the teaching and research literature of graduate analysis:oProviding a fundamental, modern approach to measure theoryoInvestigating advanced material on the Bochner integral, geometric theory, and major theorems in Fourier Analysis Rn, including the theory of singular integrals and Milhin's theorem - material that does not appear in textbooksoOffering exceptionally concise and cardinal versions of all the main theorems about characteristic functionsoContaining an original examination of sufficient statistics, based on the general theory of Radon measuresWith an ambitious scope, this resource unifies various topics into one volume succinctly and completely. The contents span basic measure theory in an abstract and concrete form, material on classic linear functional analysis, probability, and some major results used in the theory of partial differential equations. Two different proofs of the central limit theorem are examined as well as a straightforward approach to conditional probability and expectation.Modern Analysis provides ample and well-constructed exercises and examples. Introductory topology is included to help the reader understand such items as the Riesz theorem, detailing its proofs and statements. This work will help readers apply measure theory to probability theory, guiding them to understand the theorems rather than merely follow directions. ... Read more

Book Description

Most texts on experimental design fall into one of two distinct categories. There are theoretical works with few applications and minimal discussion on design, and there are methods books with limited or no discussion of the underlying theory. Furthermore, most of these tend to either treat the analysis of each design separately with little attempt to unify procedures, or they will integrate the analysis for the designs into one general technique.A First Course in the Design of Experiments: A Linear Models Approach stands apart. It presents theory and methods, emphasizes both the design selection for an experiment and the analysis of data, and integrates the analysis for the various designs with the general theory for linear models. The authors begin with a general introduction then lead students through the theoretical results, the various design models, and the analytical concepts that will enable them to analyze virtually any design. Rife with examples and exercises, the text also encourages using computers to analyze data. The authors use the SAS software package throughout the book, but also demonstrate how any regression program can be used for analysis.With its balanced presentation of theory, methods, and applicationsand its highly readable style, A First Course in the Design of Experiments proves ideal as a text for a beginning graduate or upper-level undergraduate course in the design and analysis of experiments. ... Read more

Reviews (1)

A comprehensive book on Design of Experiments
I found this book very informative and well written. I started lecturing on the subject a year ago, before this book was published. I found a copy of this book in my University library and used it as a reference during the second half of my course. It contains a lot of material that cannot be found elsewhere and a wealth of examples. The first few Chapters are devoted to Regression which makes the book self-contained. There is an Appendix on Linear Algebra intended for those who have had little exposure to this important branch of Mathematics. For those proficient in SAS this book would be most useful.Perhaps the greatest asset of the book is the inclusion of topics I personally had difficulty finding elsewhere. There are other good books on the subject, notably by Montgomery (published by J.Wiley) and Kuehl (published by Duxbury) but for those interested in the Design of Experiments having this book as a reference at least would be most valuable. I gave this book 4 stars because of the appalling type of word-processing used. In this day and age I find it astonishing that some technical books appear to have been typed on a very old typewriter. Surely CRC Press aren't that poor. ... Read more

Book Description

The Monte Carlo method is a numerical method of solving mathematical problems through random sampling. As a universal numerical technique, the method became possible only with the advent of computers, and its application continues to expand with each new computer generation. A Primer for the Monte Carlo Method demonstrates how practical problems in science, industry, and trade can be solved using this method. The book features the main schemes of the Monte Carlo method and presents various examples of its application, including queueing, quality and reliability estimations, neutron transport, astrophysics, and numerical analysis. The only prerequisite to using the book is an understanding of elementary calculus. ... Read more

Reviews (5)

Proceed with Caution
This is a fine enough introduction to Monte Carlo scenarios, but only for the highly technically proficient reader. The mathematics is quite sophisticated and probably out of reach for those who want to understand Monte Carlo methods rather than apply them. It is also a very thin book for a very high price.

Emphasis on PRIMER
It is generally a very elementary book, and does not go into more advanced methods (for example, Sobol's low discrepancy series). It does give a number of very good examples of problems that can be tackled by Monte Carlo Methods. If you want a few pages on what Monte Carlo Methods can do,along with a little of bit decent theory, you may want to try this book. However, I am not sure that a person serious enough to spend the money on this book will be enlightened.

A good introduction to random number simulation.
I enjoyed (and enjoying) this book. However, I gave it a three due to it being $54.00 for 100 pages. A little overpriced.

An excelent primer
A easy to follow introduction to various concept related to (pseudo / quasi) random numbers, and their applications.

Interesting, broad-gauge, practically oriented
Prof. Sobol's neatly presented treatment of the principles underlying static, or Monte Carlo, simulations is an excellent introduction to the topic and will be appreciated by two distinct audiences. First, students early in their study of simulation can approach the topic aided by Sobol's clarity and frequent use of simple, concrete examples. Second, engineers (and other mathematically adept readers) who generally do not rely on probabilistic techniques for analysis can grasp the basics quickly and be reassured of the mathematical rigor which drives the technique. The most valuable part of the book is the concise development of the generation of pseudorandom numbers. Additionally, Sobol's little book gives some interesting insights into Russian practice, contributions and implementations of the Monte Carlo technique. ... Read more

Book Description

Epidemiologic studies provide research strategies for investigating public health and scientific questions relating to the factors that cause and prevent ailments in human populations. Statistics in Epidemiology: Methods, Techniques and Applications presents a comprehensive review of the wide range of principles, methods and techniques underlying prospective, retrospective and cross-sectional approaches to epidemiologic studies. Written for epidemiologists and other researchers without extensive backgrounds in statistics, this new book provides a clear and concise description of the statistical tools used in epidemiology. Emphasis is given to the application of these statistical tools, and examples are provided to illustrate direct methods for applying common statistical techniques in order to obtain solutions to problems. Statistics in Epidemiology: Methods, Techniques and Applications goes beyond the elementary material found in basic epidemiology and biostatistics books and provides a detailed account of techniques: ... Read more

Reviews (2)

The Most Modern Book on Epidemiology Methods
I am somewhat surprised with the critique of the previous reader who has made some thoughtless negative comments on this book without any basis to back up his arguments. I have found this book to be extremely useful in my work at the Food and Drug Administration in Washington D.C. It contains so many excellent features that make it a highly recommended text for students and practicing quantitative epidemiologists. Instead of enumerating its strengths and virtues in my own words, I'll simply quote excerpts from some reviews that have appeared in some top journals in the field

Journal of the American Statistical Association: " This book is an excellent review of fundamental and advanced statistical methods for the analysis of epidemiologic data. The authors cover the derivations of important epidemiologic indices with substantial mathematical detail and applications to various study designs. For instance, they define risk differences, risk ratios, and odds ratios for use in prospective or cohort studies: odds ratios for retrospective or case-control studies (both matched and not); and a variety of prevalence measures for both cross-sectional studies and for stratified analyses. In each case, point and interval estimates are defined. Other topics include multivariate models such as logistic regression and proportional hazards regression, incidence indices for person-time data, and attributable risk... The authors include 14 appendixes that support the mathematical rigor in the text, which is already highly mathematically detailed. Also included are 50 exercises that are oriented toward the chapter topics and complement the numerous examples in the text... One highlight is the extensive current reference list following each chapter and appendix, complemented by an author index. Another strength is the care that the authors have taken to give appropriate software choices for each technique cited. These software sections in the chapters could be strengthened in future revisions by including actual listing of code for and output from the software packages that are described and applied to the appropriate examples... the mathematical thoroughness with which the measures are covered earns this text a high recommendation as a reference text for the practicing quantitative epidemiologist's desktop."

Techonometrics: "...The book focuses explicitly on epidemiological studies - cohort studies, case-control studies, cross-sectional studies. Its methods likewise are concentrated on the important issues in analysis of epidemiological data - stratified analysts, matching, studies with person-time data, attributable risk. Other positive features are the absence .of any content devoted to derivations in spite of the extensive and explicit presentation of all the relevant equations as the main emphasis in each of the chapters and the inclusion of a numerical example for every one of the computational procedures that is shown... SAS, BMDP, and SPSS procedures are mentioned for much of the methodology and reference is also made to several personal computer products that can be used also. Exceptional literature reference lists follow each of the 11 chapters... the book consists of 14 appendixes which provide background and derivations for some of the algebraic equations that are stated in the text. Examples of topics are odds ratios, maximum likelihood estimation, probability distributions, and logistic regression. Some of the appendixes have reference lists too..." International Statistical Review " This volume is designed as a core handbook for a course in statistics applied to epidemiology. In the first seven chapters the authors describe extensively the 'classical' estimation procedures for risk differences, risk ratios and odds ratios, and relate them to the most common study designs used in epidemiology. The presentation is accurate and proceeds slowly from crude to stratified estimation, comparing at every step alternative procedures for the calculation of point estimates and confidence intervals. In Chapter 8 the logistic, proportional hazards and Poisson regression models are described briefly... Chapters 9 and 10 deal with person-time data, again from the 'classical' point of view... The last chapter is dedicated to the public health interpretation of risk factors as measured by the risk difference, or attributable risk. Several examples are presented throughout the book; they are all taken from real data ...there are several appendices covering a wide range of topics... This volume will be welcomed by many clinical epidemiologists for its clear structure and extensive listing of the methods so often cited in the literature.

An expensive paperweight
Sahai and Khurshid have essentially authored a book with NO purpose. Beginning epidemiologists will find their mathematical proofs impractical and arcane, while biostatisticians will no doubt find them bush-league and simple. Anyone with a practical question about statistics in epi might just as easily find an answer through random-number phone dialing as opening this so-called textbook. ... Read more

Book Description

The Pacific Symposium on Biocomputing (PSB 2004) is an international, multidisciplinary conference for the presentation and discussion of current research on the theory and application of computational methods in problems of biological significance. The rigorously peer-reviewed papers and presentations are collected in this archival proceedings volume.

PSB is a forum for the presentation of work on databases, algorithms, interfaces, visualization, modeling and other computational methods, as applied to biological problems, with emphasis on applications in data-rich areas of molecular biology. PSB 2004 brings together top researchers from the US, the Asia-Pacific region and the rest of the world to exchange research findings and address open issues in all aspects of computational biology. ... Read more

Book Description

SPSS in Practice is an invaluable guide to the statistical analysis of data using the latest (10th) version of SPSS. The concise text is highly illustrated with screen shots that take you step-by-step through data entry, the choice of the appropriate test and the implementation and interpretation of the test selected.This new edition provides many more illustrative screenshots than before, expanded guidance on what method of analysis to use and more explanation of the output, interpretation of results and of the editing of graphical output. Also new to this edition is an overview of the fundamental principles of multivariate analysis. This edition of SPSS in Practice is also compatible with the forthcoming version 11 of this application. ... Read more

Book Description

Biostatistics for Epidemiologists is a unique book that provides a collection of methods that can be used to analyze data in most epidemiological studies. It examines the theoretical background of the methods described and discusses general principles that apply to the analysis of epidemiological data. Specific topics addressed include statistical interference in epidemiological research, important methods used for analyzing epidemiological data, multivariate models, dose-response analysis, analysis of the interaction between causes of disease, meta-analysis, and computer programs. Biostatistics for Epidemiologists will be a useful guide for all epidemiologists and public health professionals who rely on biostatistical data in their work. ... Read more

Book Description

The Pacific Symposium on Biocomputing (PSB 2004) is an international, multidisciplinary conference for the presentation and discussion of current research on the theory and application of computational methods in problems of biological significance. The rigorously peer-reviewed papers and presentations are collected in this archival proceedings volume.

PSB is a forum for the presentation of work on databases, algorithms, interfaces, visualization, modeling and other computational methods, as applied to biological problems, with emphasis on applications in data-rich areas of molecular biology. PSB 2004 brings together top researchers from the US, the Asia-Pacific region and the rest of the world to exchange research findings and address open issues in all aspects of computational biology. ... Read more

Book Description

These conferences are held every five years. This book contains papers by several prominent biostatisticians which were inspired by the Second Seattle Symposium in Biostatistics.

Book Description

Complex stochastic systems comprises a vast area of research, from modelling specific applications to model fitting, estimation procedures, and computing issues. The exponential growth in computing power over the last two decades has revolutionized statistical analysis and led to rapid developments and great progress in this emerging field. In Complex Stochastic Systems, leading researchers address various statistical aspects of the field, illustrated by some very concrete applications.A Primer on Markov Chain Monte Carloby Peter J. Green provides a wide-ranging mixture of the mathematical and statistical ideas, enriched with concrete examples and more than 100 references.Causal Inference from Graphical Models by Steffen L. Lauritzen explores causal concepts in connection with modelling complex stochastic systems, with focus on the effect of interventions in a given system.State Space and Hidden Markov Models by Hans R. Künschshows the variety of applications of this concept to time series in engineering, biology, finance, and geophysics.Monte Carlo Methods on Genetic Structures by Elizabeth A. Thompson investigates special complex systems and gives a concise introduction to the relevant biological methodology.Renormalization of Interacting Diffusions by Frank den Hollander presents recent results on the large space-time behavior of infinite systems of interacting diffusions.Stein's Method for Epidemic Processesby Gesine Reinert investigates the mean field behavior of a general stochastic epidemic with explicit bounds.Individually, these articles provide authoritative, tutorial-style exposition and recent results from various subjects related to complex stochastic systems. Collectively, they link these separate areas of study to form the first comprehensive overview of this rapidly developing field. ... Read more

Book Description

During the last decades, there has been an explosion in computation and information technology. This development comes with an expansion of complex observational studies and clinical trials in a variety of fields such as medicine, biology, epidemiology, sociology, and economics among many others, which involve collection of large amounts of data on subjects or organisms over time. The goal of such studies can be formulated as estimation of a finite dimensional parameter of the population distribution corresponding to the observed time-dependent process. Such estimation problems arise in survival analysis, causal inference and regression analysis.

This book provides a fundamental statistical framework for the analysis of complex longitudinal data. It provides the first comprehensive description of optimal estimation techniques based on time-dependent data structures subject to informative censoring and treatment assignment in so called semiparametric models. Semiparametric models are particularly attractive since they allow the presence of large unmodeled nuisance parameters. These techniques include estimation of regression parameters in the familiar (multivariate) generalized linear regression and multiplicative intensity models. They go beyond standard statistical approaches by incorporating all the observed data to allow for informative censoring, to obtain maximal efficiency, and by developing estimators of causal effects. It can be used to teach masters and Ph.D. students in biostatistics and statistics and is suitable for researchers in statistics with a strong interest in the analysis of complex longitudinal data. ... Read more

Book Description

Until recently, acquiring a background in the basic methodological principles that apply to most types of investigations meant struggling to obtain results through laborious calculations. The advent of statistical software packages has removed much of the tedium and many of the errors of manual calculations and allowed a marked increase in the depth and sophistication of analyses. Although most statistics classes now incorporate some instruction in using a statistics package, most introductory texts do not.Quantitative Investigations in the Biosciences using MINITAB fills this void by providing an introduction to investigative methods that, in addition to outlining statistical principles and describing methods of calculations, also presents essential commands and interprets output from the statistics package MINITAB.The author introduces the three basic elements of investigations-design, analysis, and reporting-using an extremely accessible approach that keeps mathematical detail to a minimum. He groups statistical tests according to the type of problem they are used to examine, such as comparisons, sequential relationships, and associations.Quantitative Investigations in the Biosciences using MINITAB draws techniques and examples from a variety of subjects, ranging from physiology and biochemistry through to ecology, behavioral sciences, medicine, agriculture and horticulture, and complements the mathematical results with formal conclusions for all of the worked examples. It thus provides an ideal handbook for anyone in virtually any field who wants to apply statistical techniques to their investigations. ... Read more

Book Description

Called the "bible of applied statistics," the first edition of the bestselling Handbook of Parametric and Nonparametric Statistical Procedures was unsurpassed in its scope. The Second Edition goes even further - more tests, more examples, more than 250 pages of new material.Thorough - Up-To-DateWith details of more than 100 statistical procedures, the Handbook offers unparalleled coverage of modern statistical methods. You get in-depth discussion of both practical and theoretical issues, many of which are not addressed in conventional statistics books.Practical - User-FriendlyAccessible to novices but valuable to seasoned researchers, the Handbook emphasizes application over theory and presents the procedures in a standardized formatthat makes it easy to access the information you need.If you have toØ Decide what method of analysis to useØ Use a particular test for the first timeØ Distinguish acceptable from unacceptable researchØ Interpret the results ofpublished studiesthe Handbook of Parametric and Nonparametric Statistical Procedures has the background, the answers, and the guidelines to get the job done. ... Read more

Reviews (5)

Useful reference book
Got it as a reference book to help me understand statistical tests. Havent used it much in my daily work, but its very comforting to know that its there. Quite readable, and extremely bulky to handle. Guess this will go on to be a classic in the field ..sad reality is that most of the methods discussed are already in programs like Minitab & S-Plus, so you can do most of the analysis without slogging through the theory.

Comprehensive, valuable reference
This is an excellent, exceptionally comprehensive, and well-structured reference on a wide range of inferential tests and measures of association, which is ideal for the applied scientist. After a few useful introductory chapters that provide definitions and outline the main concepts involved in inferential statistics, the various tests are covered chapter by chapter. Each chapter contains sections describing:
1. the hypotheses evaluated and relevant background information;
2. examples of the kind of problems that can be addressed using the test
3. the null and alternative hypotheses;
4. computation (including meticulously worked out examples so the reader can follow the precise workings);
5. an extensive set of notes describing the interpretation of the results, the assumptions of the test, the robustness of the test to violations of those assumptions, and comparisons with alternative tests;
6. a set of references.
A set of decision tables are provided to assist the user in selecting the appropriate test, and there are additional extensive discussions in the various chapters to assist further if required. The book also includes an extensive list of look-up tables for significance testing. An additional valuable feature of the book is that in the chapter on the nonparametric test for a difference in medians of two populations (Mann-Whitney U-test) there is included discussions about permutation and randomization tests.

The target audience of the book is the practioner rather than the theoretician. The book aims to assist in the selection of an appropriate test and the interpretation of the test results rather than on a theoretical discussion of the test. The text is exceptionally clearly written, and is highly accessible to non-experts in statistics. There is a minimum of equations, which are supplied only where necessary. While it is not that hard to find a few editorial omissions, the book does seem to have been edited carefully, and I have as yet stumbled across only trivial errors. If I were forced to find any criticism, about the only thing I could say is that at the top of the page the chapter headings are listed purely in terms of test number without listing the test name, which sometimes makes it a little harder to find the test of interest.

I make absolutely no hesitation in recommending this book to anyone who makes use of inferential statistics.

Backbone for statistical analysis
Excellent book for handling all common and uncommon stats tests. Plenty of examples for application, an overview for each. This book is good for those that have had stats and need a reference guide on them later. I would not recommend this for anyone that has not actaully formally studied higher end stats.

Superb handbook
"One stop shopping" for information on statistical testing

A great aid for understanding the use of statistical tests.
From a student's point of view, this book contains numerous aids to assist in understanding the application of many statistical tests. For example, the decision charts on pages 27-30 help to determine what tests are applicable for analysis given the type of data the researcher plans to obtain in the study. I might decide that the use of the Kruskal-Wallis is appropriate. The chart refers me to Test 17, which is on page 396. I can go to that page, and get a complete plain-language explanation of the test and what it measures. Examples are provided for each test to determine appropriateness, and this section is followed by an example with computations. A complete analysis of the data and a discussion of the test ensues. All of the necessary tables for computation are included in appendices. I am a complete novice at statistics, and this book has helped me to understand material in classroom lectures. ... Read more

Book Description

The use of statistics in biology, medicine, engineering, and the sciences has grown dramatically in recent years and having a basic background in the subject has become a near necessity for students and researchers in these fields. Although many introductory statistics books already exist, too often their focus leans towards theory and few help readers gain effective experience in using a standard statistical software package.Designed to be used in a first course for graduate or upper-level undergraduate students, Basic Statistical Methods and Models builds a practical foundation in the use of statistical tools and imparts a clear understanding of their underlying assumptions and limitations. Without getting bogged down in proofs and derivations, thorough discussions help readers understand why the stated methods and results are reasonable. The use of the statistical software Minitab is integrated throughout the book, giving readers valuable experience with computer simulation and problem-solving techniques. The author focuses on applications and the models appropriate to each problem while emphasizing Monte Carlo methods, the Central Limit Theorem, confidence intervals, and power functions.The text assumes that readers have some degree of maturity in mathematics, but it does not require the use of calculus. This, along with its very clear explanations, generous number of exercises, and demonstrations of the extensive uses of statistics in diverse areas applications make Basic Statistical Methods and Models highly accessible to students in a wide range of disciplines. ... Read more

Book Description

Accessible to medicine- and/or public policy-related audiences, as well as most statisticians.
* Emphasis on outliers is discussed by way of detection and treatment.
* Resampling statistics software is incorporated throughout.
* Motivating applications are presented in light of honest theory.
* Plentiful exercises are sprinkled throughout. ... Read more