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Descriptions du produit

Revue de presse

"I enjoyed reading this well written book. I recommend it highly tostatisticians." ( Journal of Statistical Computation &Simulation, July 2004)

a well written and well documented text formissing data analysis... (Statistical Methods in MedicalResearch, Vol.14, No.1, 2005)

"An update to this authoritative book is indeed welcome."(Journal of the American Statistical Association, December2004)

this is an excellent book. It is well written andinspiring (Statistics in Medicine, 2004;23)

"...this second edition offers a thoroughly up–to–date,reorganized survey of of current methods for handling missing dataproblems..." (Zentralblatt Math, Vol.1011, No.11, 203)

"...well written and very readable...a comprehensive, updatetreatment of an important topic by two of the leading researchersin the field. In summary, I highly recommend this book..."(Technometrics, Vol. 45, No. 4, November 2003)

Présentation de l'éditeur

Praise for the First Edition of Statistical Analysis with MissingData

"An important contribution to the applied statisticsliterature.... I give the book high marks for unifying and makingaccessible much of the past and current work in this importantarea."
William E. Strawderman, Rutgers University

"This book...provide[s] interesting real–life examples,stimulating end–of–chapter exercises, and up–to–date references. Itshould be on every applied statistician s bookshelf."
The Statistician

"The book should be studied in the statistical methodsdepartment in every statistical agency."
Journal of Official Statistics

Statistical analysis of data sets with missing values is apervasive problem for which standard methods are of limited value.The first edition of Statistical Analysis with Missing Data hasbeen a standard reference on missing–data methods. Now, reflectingextensive developments in Bayesian methods for simulating posteriordistributions, this Second Edition by two acknowledged experts onthe subject offers a thoroughly up–to–date, reorganized survey ofcurrent methodology for handling missing–data problems.

Blending theory and application, authors Roderick Little andDonald Rubin review historical approaches to the subject anddescribe rigorous yet simple methods for multivariate analysis withmissing values. They then provide a coherent theory for analysis ofproblems based on likelihoods derived from statistical models forthe data and the missing–data mechanism and apply the theory to awide range of important missing–data problems.

The new edition now enlarges its coverage to include:

  • Expanded coverage of Bayesian methodology, both theoretical andcomputational, and of multiple imputation
  • Analysis of data with missing values where inferences are basedon likelihoods derived from formal statistical models for thedata–generating and missing–data mechanisms
  • Applications of the approach in a variety of contexts includingregression, factor analysis, contingency table analysis, timeseries, and sample survey inference
  • Extensive references, examples, and exercises

Amstat News asked three review editors to rate their topfive favorite books in the September 2003 issue. StatisticalAnalysis With Missing Data was among those chosen.

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Dans ce livre (En savoir plus)
Première phrase
Standard statistical methods have been developed to analyze rectangular data sets. Lire la première page
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Couverture | Copyright | Table des matières | Extrait | Index | Quatrième de couverture
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52 internautes sur 53 ont trouvé ce commentaire utile 
Cautious and applicable 6 février 2004
Par wiredweird - Publié sur
Format: Relié Achat vérifié
I'm working with data sets where up to 15% of measurements are unusable. If I'm going to get any results at all, I have to get them despite the lost values.
This book provides a huge library of techniques for working around the holes, as well as techniques for filling them in. This is not a cut-and-paste text for programmers - it gives the basic theory and algorithms for each technique. Still, the presentation is quite readable and fairly easy to put into practice.
The book's emphasis is on imputation - filling in values so that analysis can move forward. This is something to approach with real caution, though. The imputed (synthesized) values must not perturb the analysis, so the imputation must differ according to the analysis being performed. The authors present a variety of imputation techniques, as well as bootstrap, jacknife, and other techniques for measuring the quality of the results.
The authors also dedicate chapters to approaches that work only with available data, and to cases where missing data can not simply be ignored.
This is the most thorough and practical guide I know to handling missing data. In an ideal world, experiments would all produce usable results and surveys would all have every question answered. When you have to deal with reality, though, this is the book.
32 internautes sur 32 ont trouvé ce commentaire utile 
second edition of a great book 18 août 2007
Par Michael R. Chernick - Publié sur
Format: Relié
I have previously given great praise to this book under the pen name of statman13. To add to my previous reviews I should say that Little and Rubin continue to be the top researchers in this field and Don Rubin often consults with the FDA, the pharmaceutical industry and statistical review boards. He is an eloquent speaker and writer as is also his co-author Rod Little. The development of the model classifications MAR, MCAR and MNAR (or nonignorable missingness)is due to Rubin and is quite common these days in the thinking of researchers involved with missing data in their analyses. In the cases where the missing mechanism is not ignorable pattern mixture models, that Little had a major role in developing, are given. All this wonderful work is spelled out in this book. This second edition has added much discussion of Bayesian methods using the current computational advantages of Gibbs sampling. Also some specific techniques have software implementation in SAS or SPlus and this is pointe dout by the authors when it comes up. I think that rather than searching through the index to find where sofware is mentioned it would be nice to have a section of the book devoted to it. oddly the software tool SOLAS that Rubin had a part in aiding the development does not appear to be mentioned in the book. Perhaps the authors will expand upon the discussion of software in the next edition. Also new to this edition is more detailed coverage of multiple imputation. Resampling techniques are also discussed in the context of getting sensible estimates of the standard deviation of the estimated parameters in the face of imputing some of the data.
Must have book for missing data analysis 27 mai 2010
Par W. YIP - Publié sur
Format: Relié Achat vérifié
This is a classic and should be part of your library if you are a serious statistician.
4 internautes sur 7 ont trouvé ce commentaire utile 
Classic Text on Missing Data 14 octobre 2000
Par Un client - Publié sur
Format: Relié
This is the standard reference for statistics of missing data. Anyone working in the field will find it indispensable. The new edition is updated to cover a number of recent developments in the field.
3 internautes sur 8 ont trouvé ce commentaire utile 
This book is updated. 3 novembre 2002
Par Un client - Publié sur
Format: Relié
I love this book as one of fundamental books on missing data problem.
For EM algorithm, we can refer other books.
However, we always need to take missing data mechanisms into account, when we do analyze incomplete data.
Now this honorable book has its second edition.
It is fully revised and updated.
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