A Course On Small Area Estimation And Mixed Models
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A Course on Small Area Estimation and Mixed Models
This advanced textbook explores small area estimation techniques, covers the underlying mathematical and statistical theory and offers hands-on support with their implementation. It presents the theory in a rigorous way and compares and contrasts various statistical methodologies, helping readers understand how to develop new methodologies for small area estimation. It also includes numerous sample applications of small area estimation techniques. The underlying R code is provided in the text and applied to four datasets that mimic data from labor markets and living conditions surveys, where the socioeconomic indicators include the small area estimation of total unemployment, unemployment rates, average annual household incomes and poverty indicators. Given its scope, the book will be useful for master and PhD students, and for official and other applied statisticians.
Small Area Estimation
An accessible introduction to indirect estimation methods, both traditional and model-based. Readers will also find the latest methods for measuring the variability of the estimates as well as the techniques for model validation. Uses a basic area-level linear model to illustrate the methods Presents the various extensions including binary response data through generalized linear models and time series data through linear models that combine cross-sectional and time series features Provides recent applications of SAE including several in U.S. Federal programs Offers a comprehensive discussion of the design issues that impact SAE
Robust Small Area Estimation
In recent years there has been substantial and growing interest in small area estimation (SAE) that is largely driven by practical demands. Here, the term "small area" typically refers to a subpopulation or domain of interest for which a reliable direct estimate, based only on the domain-specific sample, cannot be produced due to small sample size in the domain. Keywords in SAE are “borrowing strength”. Because there are insufficient samples from the small areas to produce reliable direct estimates, statistical methods are sought to utilize other sources of information to do better than the direct estimates. A typical way of borrowing strength is via statistical modelling. On the other hand, there is no “free lunch”. Yes, one can do better by borrowing strength, but there is a cost. This is the main topic discussed in this text. Features A comprehensive account of methods, applications, as well as some open problems related to robust SAE Methods illustrated by worked examples and case studies using real data Discusses some advanced topics including benchmarking, Bayesian approaches, machine learning methods, missing data, and classified mixed model prediction Supplemented with code and data via a website Robust Small Area Estimation: Methods, Applications, and Open Problems is primarily aimed at researchers and graduate students of statistics and data science and would also be suitable for geography and survey methodology researchers. The practical approach should help persuade practitioners, such as those in government agencies, to more readily adopt robust SAE methods. It could be used to teach a graduate-level course to students with a background in mathematical statistics.