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Outlier robust model-assisted small area estimation.

Enrico Fabrizi1, Nicola Salvati, Monica Pratesi

  • 1Dipartimento di Scienze Economiche e Sociali, Università Cattolica del S. Cuore, Via Emilia Parmense 84, Piacenza, Italy.

Biometrical Journal. Biometrische Zeitschrift
|October 15, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces design-consistent small area estimators using M-quantile models. The new approach integrates unit-level survey weights for improved accuracy in complex sampling designs.

Keywords:
BootstrapFinite populationsQuantile regressionRobust estimationSampling weights

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Area of Science:

  • Statistics
  • Survey Methodology

Background:

  • M-quantile models offer robust small area estimation without strong parametric assumptions.
  • Existing M-quantile methods lack unit-level survey weight integration, potentially compromising design consistency.

Purpose of the Study:

  • To develop design-consistent small area estimators using M-quantile models.
  • To address the limitation of not using unit-level survey weights in prior M-quantile approaches.

Main Methods:

  • A model-assisted approach was adopted to construct estimators.
  • The new estimators are based on the M-quantile small area model.
  • Analytic and bootstrap variance estimators were developed.

Main Results:

  • The proposed estimators are design-consistent.
  • The methodology accommodates unit-level survey weights.
  • Empirical evaluations were conducted under complex sampling designs.

Conclusions:

  • The model-assisted M-quantile approach enhances small area estimation by incorporating survey weights.
  • This method provides reliable and design-consistent estimates, particularly valuable for complex surveys.