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INTRODUCCIÓN AL MUESTREO BASADO EN MODELOS / EREDUETAN OINARRITUTAKO LAGINKETARI SARRERA / AN INTRODUCTION TO MODEL-BASED SURVEY SAMPLING


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CONTENTS

 

  1. FUNDAMENTAL CONCEPTS
    1. Sample Frames and Auxiliary Information (11)
    2. Non-Informative Sampling (11)
    3. Probability sampling (11)
    4. Basic Assumptions (12)
    5. Population Variables (12)
    6. Finite Population Parameters (12)
    7. Sample Statistics (13)
    8. Sample Error and Sample Error Distribution (13)
    9. Which Distribution? The Repeated Sampling Distribution (13)
    10. Another distribution: The Concept of a Population Model (13)
    11. The Repeated Sampling Distribution vs. the Superpopulation Distribution (14)
    12. How to specify the superpopulation distribution? (14)
  2. THE MODEL-BASED APPROACH
    1. Bias and Variance under the Model-Based Approach (19)
    2. The Homogeneous Population Model (H) (20)
    3. Stratified Homogeneous Population Model (S) (21)
      • Optimal Estimation Under S (21)
      • Sample Design Under S (22)
    4. Populations with Linear Regression Structure (25)
      • Optimal Sample Design Under R (26)
      • Optimal Sample Design Under L (27)
      • Combining Regression and Stratification (27)
    5. Model-based Methods for Hierarchical Populations (28)
      • Optimal Prediction under C and Two Stage Sampling (29)
      • Optimal Sample Design Under C and Two Stage Sampling (32)
    6. Optimal Prediction Under The General Linear Model (32)
  3. ROBUST MODEL-BASED INFERENCE
    1. Misspecification of the Homogenous Model (H) (34)
    2. Robutness under Stratification (36)
    3. Balanced Sampling and Robust Estimation (38)
    4. Outlier Robust Estimation (40)
      • Basic Ideas (40)
      • Robust Bias Calibration (41)
      • Outlier Robust Design (43)
      • A numerical Study (44)
      • Practical Problems (45)
  4. METHODS OF VARIANCE ESTIMATION
    1. Robust Variance Estimation for the Ratio Estimator (47)
    2. Robust Variance Estimation for Linear Estimators (49)
    3. The Ultimate Cluster Variance Estimator (51)
    4. Variance Estimation for Non-Linear Estimators (53)
    5. Replication-Based Methods of Variance Estimation (55)
  5. ESTIMATION FOR MULTIPURPOSE SURVEYS
    1. Calibrated Weighting (60)
    2. Nonparametric Weighting (61)
    3. Calibrating Nonparametric Weights (62)
    4. Problems Associated With Calibrated Weights (63)
    5. Simulation Analysis of Calibrated and Ridged Weighting (64)
    6. Interaction Between Sample Weighting and Sample Design (69)
  6. ESTIMATION FOR DOMAINS AND SMALL AREAS
    1. Model-Based Inference when the Domain Sze Nd is Unknown (75)
    2. Model-Based Inference when the Domain Size is Nd Known (76)
    3. Model-Based Inference Using Auxiliary Information (77)
    4. Small Area Estimation (79)
      • Fixed Effects Models(79)
      • Random Effects Models (80)
      • Generalized Linear Mixed Models (GLMM) in Small Area Estimation(82)
      • Estimation of Mean Square Error(82)
      • An example: Comparing GLm and GLMM-Based Predictors for LAD level Estimates of ILO Unemployment the UK LFS(83)
  7. REFERENCES (83)