An Introduction to Model Based Survey Sampling with Applications 1st Edition by Ray Chambers, Robert Clark ISBN 019856662X 9780198566625

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ISBN 10: 019856662X 
ISBN 13: 9780198566625
Author: Ray Chambers, Robert Clark

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.

An Introduction to Model Based Survey Sampling with Applications 1st Table of 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 Efficient Sampling under Transformation and Balanced Weighting

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Tags: Ray Chambers, Robert Clark, Introduction, Sampling