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Model-assisted estimation in high-dimensional settings for survey data.

Mehdi Dagdoug1, Camelia Goga1, David Haziza2

  • 1Laboratoire de Mathématiques de Besançon, Université de Bourgogne Franche-Comté, Besançon, France.

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|February 23, 2023
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Summary
This summary is machine-generated.

Model-assisted estimators efficiently use auxiliary data for surveys. This study evaluates their performance, including linear regression and penalized estimators, in high-dimensional settings using smart meter data.

Keywords:
Design consistencyLassoXGBoostelastic netrandom forestridge regression

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Area of Science:

  • Statistics
  • Survey Methodology
  • Data Science

Background:

  • Model-assisted estimators leverage auxiliary information for improved survey efficiency.
  • These methods are robust to model misspecification, retaining design-based properties like consistency.
  • High-dimensional data presents unique challenges for traditional estimation techniques.

Purpose of the Study:

  • To examine model-assisted estimators from a design-based perspective in a high-dimensional context.
  • To assess the performance of linear regression and penalized estimators.
  • To evaluate bias and efficiency using real-world high-dimensional data.

Main Methods:

  • Design-based examination of model-assisted estimators.
  • Inclusion of linear regression and penalized regression models.
  • Extensive simulation study using smart meter data.

Main Results:

  • Performance assessment of various model-assisted estimators.
  • Evaluation of bias and efficiency in a high-dimensional dataset.
  • Comparison of linear regression and penalized estimators.

Conclusions:

  • Model-assisted estimators offer a robust approach for high-dimensional survey data.
  • Penalized estimators show promise in managing high-dimensional auxiliary information.
  • The study provides insights into practical applications using smart meter data.