| Description |
1 online resource |
| Series |
Statistics for social and behavioral sciences |
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Statistics for social and behavioral sciences.
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| Contents |
Intro -- Preface -- Contents -- Acronyms -- 1 Small Area Estimation -- 1.1 Introduction -- 1.2 Mixed Models -- 1.3 The Data Files -- 1.3.1 The LFS Data Files -- 1.3.2 The LCS Data Files -- References -- 2 Design-Based Direct Estimation -- 2.1 Introduction -- 2.2 Survey Sampling Theory -- 2.3 Direct Estimator of the Total and the Mean -- 2.4 Estimator of the Ratio -- 2.5 Other Direct Estimators of the Mean and the Total -- 2.6 Bootstrap Resampling for Variance Estimation -- 2.7 Jackknife Resampling for Variance Estimation |
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2.7.1 Delete-One-Cluster Jackknife for Estimators of Domain Parameters -- 2.8 R Codes for Design-Based Direct Estimators -- 2.8.1 Horvitz-Thompson Direct Estimators of the Total and the Mean -- 2.8.2 Hájek Direct Estimator of the Mean and the Total -- 2.8.3 Jackknife Estimator of Variances -- 2.8.4 Functions for Calculating Direct Estimators -- References -- 3 Design-Based Indirect Estimation -- 3.1 Introduction -- 3.2 Basic Synthetic Estimator -- 3.3 Post-Stratified Estimator -- 3.4 Sample Size Dependent Estimator -- 3.5 Generalized Regression Estimator -- 3.6 Estimators of Unemployment Rates |
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3.7 A Labor Force Survey -- 3.7.1 Weight Calibration and Benchmarking -- 3.7.2 Resampling Methods for the LFS -- 3.8 R Codes for Design-Based Indirect Estimators -- 3.8.1 Basic Synthetic Estimator of the Total -- 3.8.2 Post-stratified Estimator of the Total -- 3.8.3 Generalized Regression Estimator of the Mean -- References -- 4 Prediction Theory -- 4.1 Introduction -- 4.2 The Predictive Approach -- 4.3 Prediction Theory Under the Linear Model -- 4.4 The General Prediction Theorem -- 4.5 BLUPs for Some Simple Models -- 4.6 R Codes for BLUPs -- References -- 5 Linear Models -- 5.1 Introduction |
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6.4 Maximum Likelihood Estimation -- 6.4.1 Description of the Method -- 6.4.2 Maximum Likelihood Estimators for Alternative Parameters -- 6.5 Residual Maximum Likelihood Estimation -- 6.5.1 Description of the Method -- 6.5.2 REML Estimators for Alternative Parameters -- 6.5.3 Further REML Equations for Linear Mixed Models -- 6.6 Henderson 3 Estimation -- 6.6.1 Description of the Method -- 6.6.2 Moments of Henderson 3 Estimators -- 6.7 R Codes for Fitting Linear Mixed Models -- 6.7.1 Library lme4 -- 6.7.2 Library nlme -- References -- 7 Nested Error Regression Models -- 7.1 Introduction |
| Summary |
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 |
| Bibliography |
Includes bibliographical references and index |
| Notes |
Online resource; title from PDF title page (SpringerLink, viewed April 6, 2021) |
| Subject |
Small area statistics.
|
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R (Computer program language)
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R (Lenguaje de programación)
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R (Computer program language)
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Small area statistics
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| Form |
Electronic book
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| Author |
Esteban, María Dolores, author
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Pérez, Agustín, author
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Hobza, Tomáš, author
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| ISBN |
9783030637576 |
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3030637573 |
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