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## Details

Genre/Form: | Electronic books |
---|---|

Additional Physical Format: | Print version: Chambers, Raymond L. Maximum Likelihood Estimation for Sample Surveys. Hoboken : CRC Press, ©2012 |

Material Type: | Document, Internet resource |

Document Type: | Internet Resource, Computer File |

All Authors / Contributors: |
Raymond L Chambers |

ISBN: | 9781420011357 1420011359 |

OCLC Number: | 793193210 |

Description: | 1 online resource (374 pages). |

Contents: | Front Cover; Dedication; Contents; Preface; 1. Introduction; 2. Maximum likelihood theory for sample surveys; 3. Alternative likelihood-based methods for sample survey data; 4. Populations with independent units; 5. Regression models; 6. Clustered populations; 7. Informative nonresponse; 8. Maximum likelihood in other complicated situations; Notation. |

Series Title: | Chapman & Hall/CRC Monographs on Statistics & Applied Probability. |

### Abstract:

## Reviews

*Editorial reviews*

Publisher Synopsis

Sample surveys provide data used by researchers in a large range of disciplines to analyze important relationships using well-established and widely used likelihood methods. The methods used to select samples often result in the sample differing in important ways from the target population and standard application of likelihood methods can lead to biased and inefficient estimates. Maximum Likelihood Estimation for Sample Surveys presents an overview of likelihood methods for the analysis of sample survey data that account for the selection methods used, and includes all necessary background material on likelihood inference. It covers a range of data types, including multilevel data, and is illustrated by many worked examples using tractable and widely used models. It also discusses more advanced topics, such as combining data, non-response, and informative sampling.The book presents and develops a likelihood approach for fitting models to sample survey data. It explores and explains how the approach works in tractable though widely used models for which we can make considerable analytic progress. For less tractable models numerical methods are ultimately needed to compute the score and information functions and to compute the maximum likelihood estimates of the model parameters. For these models, the book shows what has to be done conceptually to develop analyses to the point that numerical methods can be applied. Designed for statisticians who are interested in the general theory of statistics, Maximum Likelihood Estimation for Sample Surveys is also aimed at statisticians focused on fitting models to sample survey data, as well as researchers who study relationships among variables and whose sources of data include surveys. IntroductionNature and role of sample surveysSample designsSurvey data, estimation and analysisWhy analysts of survey data should be interested in maximum likelihood estimationWhy statisticians should be interested in the analysis of survey dataA sample survey exampleMaximum likelihood estimation for infinite populationsBibliographic notesMaximum likelihood theory for sample surveysIntroductionMaximum likelihood using survey dataIllustrative examples with complete responseDealing with nonresponseIllustrative examples with nonresponseBibliographic notesAlternative likelihood-based methods for sample survey dataIntroductionPseudo-likelihoodSample likelihoodAnalytic comparisons of maximum likelihood, pseudolikelihood and sample likelihood estimationThe role of sample inclusion probabilities in analytic analysisBayesian analysisBibliographic notesPopulations with independent unitsIntroductionThe score and information functions for independent unitsBivariate Gaussian populationsMultivariate Gaussian populationsNon-Gaussian auxiliary variablesStratified populationsMultinomial populationsHeterogeneous multinomial logistic populationsBibliographic notesRegression modelsIntroductionA Gaussian exampleParameterization in the Gaussian modelOther methods of estimationNon-Gaussian modelsDifferent auxiliary variable distributionsGeneralized linear modelsSemiparametric and nonparametric methodsBibliographic notesClustered populationsIntroductionA Gaussian group dependent modelA Gaussian group dependent regression modelExtending the Gaussian group dependent regression modelBinary group dependent modelsGrouping modelsBibliographic notesInformative nonresponseIntroductionNonresponse in innovation surveysRegression with item nonresponseRegression with arbitrary nonresponseImputation versus estimationBibliographic notesMaximum likelihood in other complicated situationsIntroductionLikelihood analysis under informative selectionSecondary analysis of sample survey dataCombining summary population information with likelihood analysisLikelihood analysis with probabilistically linked dataBibliographic notes "This book makes a strong contribution to the model-based approach. â ¦ This book is the first thorough, self-contained development of the likelihood theory on sample survey data. â ¦ The authors demonstrate application of their maximum likelihood method in many important estimation problems. â ¦ the maximum likelihood approach presented in this book allows for further scientific discoveries and further new results when dealing with complex statistical data."-Imbi Traat, International Statistical Review (2013), 81, 2"The authors masterfully accomplish their goal and present us with an excellent and well-written book on model-based analysis for sample surveys. For the models with a mathematically tractable likelihood function, the authors develop the theory to the point ready for numerical implementation; for the mathematical intractable case, they also establish a conceptual procedure that allows future numerical research and implementation. â ¦ the book has something for just about every applied statistician and practitioner whose work is related to sampling survey design and analysis. â ¦ elegant presentation of the theory and clarity of writing make it easy to read. â ¦ a valuable theoretical contribution to the area of survey sampling and provides a thoughtful basis for further applied research. â ¦ I also recommend this book as a key reference for graduate students in applied statistics and related areas."-Cheng Peng, Mathematical Reviews, May 2013"This sinewy and satisfying book presents a thorough development of the use of likelihood techniques for the analysis of sample survey data, that is, for model-based analysis. â ¦ the authors have taken care to lace the presentation with generous explanations, drawing connections between the content and familiar examples in thoughtful ways, and occasionally providing guidance from their own experience. I particularly enjoyed the use of a stratified population to explain the difference between aggregated and disaggregated estimation. Here, and in similar places, the book shines. â ¦ well organized â ¦ [and] extremely well edited â ¦"-Andrew Robinson, Australian & New Zealand Journal of Statistics, 2013 Introduction Nature and role of sample surveys Sample designs Survey data, estimation and analysis Why analysts of survey data should be interested in maximum likelihood estimation Why statisticians should be interested in the analysis of survey data A sample survey example Maximum likelihood estimation for infinite populations Bibliographic notes Maximum likelihood theory for sample surveys Introduction Maximum likelihood using survey data Illustrative examples with complete response Dealing with nonresponse Illustrative examples with nonresponse Bibliographic notes Alternative likelihood-based methods for sample survey data Introduction Pseudo-likelihood Sample likelihood Analytic comparisons of maximum likelihood, pseudolikelihood and sample likelihood estimation The role of sample inclusion probabilities in analytic analysis Bayesian analysis Bibliographic notes Populations with independent units Introduction The score and information functions for independent units Bivariate Gaussian populations Multivariate Gaussian populations Non-Gaussian auxiliary variables Stratified populations Multinomial populations Heterogeneous multinomial logistic populations Bibliographic notes Regression models Introduction A Gaussian example Parameterization in the Gaussian model Other methods of estimation Non-Gaussian models Different auxiliary variable distributions Generalized linear models Semiparametric and nonparametric methods Bibliographic notes Clustered populations Introduction A Gaussian group dependent model A Gaussian group dependent regression model Extending the Gaussian group dependent regression model Binary group dependent models Grouping models Bibliographic notes Informative nonresponse Introduction Nonresponse in innovation surveys Regression with item nonresponse Regression with arbitrary nonresponse Imputation versus estimation Bibliographic notes Maximum likelihood in other complicated situations Introduction Likelihood analysis under informative selection Secondary analysis of sample survey data Combining summary population information with likelihood analysis Likelihood analysis with probabilistically linked data Bibliographic notes Sample surveys provide data used by researchers in a large range of disciplines to analyze important relationships using well-established and widely used likelihood methods. The methods used to select samples often result in the sample differing in important ways from the target population and standard application of likelihood methods can lead to biased and inefficient estimates. Maximum Likelihood Estimation for Sample Surveys presents an overview of likelihood methods for the analysis of sample survey data that account for the selection methods used, and includes all necessary background material on likelihood inference. It covers a range of data types, including multilevel data, and is illustrated by many worked examples using tractable and widely used models. It also discusses more advanced topics, such as combining data, non-response, and informative sampling. The book presents and develops a likelihood approach for fitting models to sample survey data. It explores and explains how the approach works in tractable though widely used models for which we can make considerable analytic progress.For less tractable models numerical methods are ultimately needed to compute the score and information functions and to compute the maximum likelihood estimates of the model parameters. For these models, the book shows what has to be done conceptually to develop analyses to the point that numerical methods can be applied. Designed for statisticians who are interested in the general theory of statistics, Maximum Likelihood Estimation for Sample Surveys is also aimed at statisticians focused on fitting models to sample survey data, as well as researchers who study relationships among variables and whose sources of data include surveys. Read more...

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