Introduction to Model-Based Survey Sampling with Applications
1st Edition
Published on 12. January 2012
280 pages
978-0-19-162789-7 (ISBN)
Description
This text brings together important ideas on the model-based approach to sample survey, which has been developed over the last twenty years. Suitable for graduate students and professional statisticians, it moves from basic ideas fundamental to sampling to more rigorous mathematical modelling and data analysis and includes exercises and solutions.
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Ray Chambers | Robert Clark
An Introduction to Model-Based Survey Sampling with Applications
Book
01/2012
1st Edition
Oxford University Press
€114.80
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Content
- Cover
- Contents
- PART I: BASICS OF MODEL-BASED SURVEY INFERENCE
- 1. Introduction
- 1.1 Why Sample?
- 1.2 Target Populations and Sampling Frames
- 1.3 Notation
- 1.4 Population Models and Non-Informative Sampling
- 2. The Model-Based Approach
- 2.1 Optimal Prediction
- 3. Homogeneous Populations
- 3.1 Random Sampling Models
- 3.2 A Model for a Homogeneous Population
- 3.3 Empirical Best Prediction and Best Linear Unbiased Prediction of the Population Total
- 3.4 Variance Estimation and Confidence Intervals
- 3.5 Predicting the Value of a Linear Population Parameter
- 3.6 How Large a Sample?
- 3.7 Selecting a Simple Random Sample
- 3.8 A Generalisation of the Homogeneous Model
- 4. Stratified Populations
- 4.1 The Homogeneous Strata Population Model
- 4.2 Optimal Prediction Under Stratification
- 4.3 Stratified Sample Design
- 4.4 Proportional Allocation
- 4.5 Optimal Allocation
- 4.6 Allocation for Proportions
- 4.7 How Large a Sample?
- 4.8 Defining Stratum Boundaries
- 4.9 Model-Based Stratification
- 4.10 Equal Aggregate Size Stratification
- 4.11 Multivariate Stratification
- 4.12 How Many Strata?
- 5. Populations with Regression Structure
- 5.1 Optimal Prediction Under a Proportional Relationship
- 5.2 Optimal Prediction Under a Linear Relationship
- 5.3 Sample Design and Inference Under the Ratio Population Model
- 5.4 Sample Design and Inference Under the Linear Population Model
- 5.5 Combining Regression and Stratification
- 6. Clustered Populations
- 6.1 Sampling from a Clustered Population
- 6.2 Optimal Prediction for a Clustered Population
- 6.3 Optimal Design for Fixed Sample Size
- 6.4 Optimal Design for Fixed Cost
- 6.5 Optimal Design for Fixed Cost including Listing
- 7. The General Linear Population Model
- 7.1 A General Linear Model for a Population
- 7.2 The Correlated General Linear Model
- 7.3 Special Cases of the General Linear Population Model
- 7.4 Model Choice
- 7.5 Optimal Sample Design
- 7.6 Derivation of BLUP Weights
- PART II: ROBUST MODEL-BASED SURVEY METHODS
- 8. Robust Prediction Under Model Misspecification
- 8.1 Robustness and the Homogeneous Population Model
- 8.2 Robustness and the Ratio Population Model
- 8.3 Robustness and the Clustered Population Model
- 8.4 Non-parametric Prediction
- 9. Robust Estimation of the Prediction Variance
- 9.1 Robust Variance Estimation for the Ratio Estimator
- 9.2 Robust Variance Estimation for General Linear Estimators
- 9.3 The Ultimate Cluster Variance Estimator
- 10. Outlier Robust Prediction
- 10.1 Strategies for Outlier Robust Prediction
- 10.2 Robust Parametric Bias Correction
- 10.3 Robust Non-parametric Bias Correction
- 10.4 Outlier Robust Design
- 10.5 Outlier Robust Ratio Estimation: Some Empirical Evidence
- 10.6 Practical Problems with Outlier Robust Estimators
- PART III: APPLICATIONS OF MODEL-BASED SURVEY INFERENCE
- 11. Inference for Non-linear Population Parameters
- 11.1 Differentiable Functions of Population Means
- 11.2 Solutions of Estimating Equations
- 11.3 Population Medians
- 12. Survey Inference via Sub-Sampling
- 12.1 Variance Estimation via Independent Sub-Samples
- 12.2 Variance Estimation via Dependent Sub-Samples
- 12.3 Variance and Interval Estimation via Bootstrapping
- 13. Estimation for Multipurpose Surveys
- 13.1 Calibrated Weighting via Linear Unbiased Weighting
- 13.2 Calibration of Non-parametric Weights
- 13.3 Problems Associated With Calibrated Weights
- 13.4 A Simulation Analysis of Calibrated and Ridged Weighting
- 13.5 The Interaction Between Sample Weighting and Sample Design
- 14. Inference for Domains
- 14.1 Unknown Domain Membership
- 14.2 Using Information about Domain Membership
- 14.3 The Weighted Domain Estimator
- 15. Prediction for Small Areas
- 15.1 Synthetic Methods
- 15.2 Methods Based on Random Area Effects
- 15.3 Estimation of the Prediction MSE of the EBLUP
- 15.4 Direct Prediction for Small Areas
- 15.5 Estimation of Conditional MSE for Small Area Predictors
- 15.6 Simulation-Based Comparison of EBLUP and MBD Prediction
- 15.7 Generalised Linear Mixed Models in Small Area Prediction
- 15.8 Prediction of Small Area Unemployment
- 15.9 Concluding Remarks
- 16. Model-Based Inference for Distributions and Quantiles
- 16.1 Distribution Inference for a Homogeneous Population
- 16.2 Extension to a Stratified Population
- 16.3 Distribution Function Estimation under a Linear Regression Model
- 16.4 Use of Non-parametric Regression Methods for Distribution Function Estimation
- 16.5 Imputation vs. Prediction for a Wages Distribution
- 16.6 Distribution Inference for Clustered Populations
- 17. Using Transformations in Sample Survey Inference
- 17.1 Back Transformation Prediction
- 17.2 Model Calibration Prediction
- 17.3 Smearing Prediction
- 17.4 Outlier Robust Model Calibration and Smearing
- 17.5 Empirical Results I
- 17.6 Robustness to Model Misspecification
- 17.7 Empirical Results II
- 17.8 Effcient Sampling under Transformation and Balanced Weighting
- Bibliography
- Exercises
- Index
- A
- B
- C
- D
- E
- F
- G
- H
- I
- J
- K
- L
- M
- N
- O
- P
- Q
- R
- S
- T
- U
- V
- W
