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Robust Variable Selection with Exponential Squared Loss for the Spatial Durbin Model.

Zhongyang Liu1, Yunquan Song1, Yi Cheng1

  • 1College of Science, China University of Petroleum, Qingdao 266580, China.

Entropy (Basel, Switzerland)
|February 25, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a robust variable selection method for spatial Durbin models, enhancing accuracy and robustness in spatial econometrics, especially with noisy data.

Keywords:
exponential squared lossrobust variable selectionspatial Durbin model

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Area of Science:

  • Econometrics
  • Spatial Analysis
  • Statistical Modeling

Background:

  • Spatial econometrics is crucial for analyzing data with spatial dependencies.
  • Existing variable selection methods face challenges with complex spatial models.

Purpose of the Study:

  • To propose a robust variable selection method for the spatial Durbin model.
  • To address computational challenges in solving nonconvex optimization problems.

Main Methods:

  • Utilizing exponential squared loss and adaptive lasso for variable selection.
  • Developing a Block Coordinate Descent (BCD) algorithm with DC decomposition for efficient model solving.

Main Results:

  • Established asymptotic and "Oracle" properties of the proposed estimator.
  • Demonstrated superior robustness and accuracy compared to existing methods in simulations, particularly under noise.
  • Successfully applied the method to a real-world housing price dataset.

Conclusions:

  • The proposed method offers a robust and accurate approach to variable selection in spatial Durbin models.
  • The BCD algorithm effectively handles the computational complexities associated with the proposed method.