Variance Estimation
Exercises
13 Chapter 6: Balanced Sampling 6.1 Introduction 6.2 Balanced Sampling: Definition 6.3 Balanced Sampling and Linear Programming 6.4 Balanced Sampling by Systematic Sampling 6.5 Methode of Deville, Grosbras, and Roth 6.6 Cube Method 6.7 Variance Approximation 6.8 Variance Estimation 6.9 Special Cases of Balanced Sampling 6.10 Practical Aspects of Balanced Sampling Exercise
14 Chapter 7: Cluster and Two‐stage Sampling 7.1 Cluster Sampling 7.2 Two‐stage Sampling 7.3 Multi‐stage Designs 7.4 Selecting Primary Units with Replacement 7.5 Two‐phase Designs 7.6 Intersection of Two Independent Samples Exercises
15 Chapter 8: Other Topics on Sampling 8.1 Spatial Sampling 8.2 Coordination in Repeated Surveys 8.3 Multiple Survey Frames 8.4 Indirect Sampling 8.5 Capture–Recapture
16 Chapter 9: Estimation with a Quantitative Auxiliary Variable 9.1 The Problem 9.2 Ratio Estimator 9.3 The Difference Estimator 9.4 Estimation by Regression 9.5 The Optimal Regression Estimator 9.6 Discussion of the Three Estimation Methods
17 Chapter 10: Post‐Stratification and Calibration on Marginal Totals 10.1 Introduction 10.2 Post‐Stratification 10.3 The Post‐Stratified Estimator in Simple Designs 10.4 Estimation by Calibration on Marginal Totals 10.5 Example
18 Chapter 11: Multiple Regression Estimation 11.1 Introduction 11.2 Multiple Regression Estimator 11.3 Alternative Forms of the Estimator 11.4 Calibration of the Multiple Regression Estimator 11.5 Variance of the Multiple Regression Estimator 11.6 Choice of Weights 11.7 Special Cases 11.8 Extension to Regression Estimation
19 Chapter 12: Calibration Estimation 12.1 Calibrated Methods 12.2 Distances and Calibration Functions 12.3 Solving Calibration Equations 12.4 Calibrating on Households and Individuals 12.5 Generalized Calibration 12.6 Calibration in Practice 12.7 An Example
20 Chapter 13: Model‐Based approach 13.1 Model Approach 13.2 The Model 13.3 Homoscedastic Constant Model 13.4 Heteroscedastic Model 1 Without Intercept 13.5 Heteroscedastic Model 2 Without Intercept 13.6 Univariate Homoscedastic Linear Model 13.7 Stratified Population 13.8 Simplified Versions of the Optimal Estimator 13.9 Completed Heteroscedasticity Model 13.10 Discussion 13.11 An Approach that is Both Model‐ and Design‐based
21 Chapter 14: Estimation of Complex Parameters 14.1 Estimation of a Function of Totals 14.2 Variance Estimation 14.3 Covariance Estimation 14.4 Implicit Function Estimation 14.5
