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|Methods and Applications for Statisticians|
Maximum Likelihood Estimation for Sample Surveys
R.L. Chambers, D.G. Steel, Suojin Wang, and A.K. 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. Maximum 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.
Extreme Value Methods with Applications to Finance
Serguei Y. Novak, Middlesex University, London, UK
Also available in eBook.
Statistical Methods for Stochastic Differential Equations
The seventh volume in the SemStat series, Statistical Methods for Stochastic Differential Equations presents current research trends and recent developments in statistical methods for stochastic differential equations. Written to be accessible both to new students and seasoned researchers, each chapter starts with introductions to the topics and builds gradually toward discussing recent research. Chapters are self-contained and written by leading researchers from the field. The book includes applications to finance and econometrics.
Flexible Imputation of Missing Data
Stef van Buuren, TNO Quality of Life, Leiden, The Netherlands
“I’m delighted to see this new book on multiple imputation by Stef van Buuren ...This book represents a “no nonsense” straightforward approach to the application of multiple imputation. I particularly like Stef’s use of graphical displays ... It’s great to have Stef’s book on multiple imputation, and I look forward to seeing more editions as this rapidly developing methodology continues to become even more effective at handling missing data problems in practice.”
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