Maximum Likelihood Estimation for Sample Surveys

In recent years, likelihood methods have proven to be useful in methodological and practical advances in various disciplines, including the social sciences and engineering. Likelihood Methods in Sample Surveys presents an overview of likelihood methods for the analysis of sample survey data, providing all necessary background material on likelihood inference. The book provides complete coverage on a range of data types, including multilevel and aggregate data. Illustrated by numerous worked examples and software implementation, it also addresses advanced topics such as combining data, non-response, and informative sampling.

“"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. … 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. … well organized … [and] extremely well edited …" -Andrew Robinson, Australian & New Zealand Journal of Statistics , 2013”

"This book makes a strong contribution to the model-based approach. … This book is the first thorough, self-contained development of the likelihood theory on sample survey data. … The authors demonstrate application of their maximum likelihood method in many important estimation problems. … 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. … the book has something for just about every applied statistician and practitioner whose work is related to sampling survey design and analysis. … elegant presentation of the theory and clarity of writing make it easy to read. … a valuable theoretical contribution to the area of survey sampling and provides a thoughtful basis for further applied research. … 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. … 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. … well organized … [and] extremely well edited …"
—Andrew Robinson, Australian & New Zealand Journal of Statistics, 2013

Raymond L. Chambers, David G. Steel, Suojin Wang, Alan Welsh

In recent years, likelihood methods have proven to be useful in methodological and practical advances in various disciplines, including the social sciences and engineering. Likelihood Methods in Sample Surveys presents an overview of likelihood methods for the analysis of sample survey data, providing all necessary background material on likelihood inference. The book provides complete coverage on a range of data types, including multilevel and aggregate data. Illustrated by numerous worked examples and software implementation, it also addresses advanced topics such as combining data, non-response, and informative sampling.

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