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Huseyin Guler1, Ebru Ozgur Guler1

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Summary

The new Mixed Lasso (M-Lasso) estimator effectively handles big data by simultaneously selecting relevant predictors and estimating parameters, outperforming existing methods in accuracy and model selection. This approach integrates stochastic restrictions into the Lasso framework for improved big data analysis.

Keywords:
Stochastic restrictionslassomixed estimatormodel selectionproduction function

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Area of Science:

  • Statistics
  • Econometrics
  • Machine Learning

Background:

  • Traditional linear regression methods struggle with big data characterized by numerous predictors relative to observations.
  • The Least Absolute Shrinkage and Selection Operator (Lasso) addresses estimation and model selection in big datasets but lacks integration with stochastic restrictions.
  • Existing methods do not incorporate stochastic linear restrictions within a Lasso framework for big data analysis.

Purpose of the Study:

  • To propose a novel Mixed Lasso (M-Lasso) estimator for big datasets.
  • To simultaneously perform model selection and parameter estimation incorporating stochastic linear restrictions.
  • To evaluate the performance of M-Lasso against existing estimators using simulation studies.

Main Methods:

  • Development of the Mixed Lasso (M-Lasso) estimator.
  • Incorporation of stochastic linear restrictions into the Lasso framework.
  • Simulation study comparing M-Lasso with traditional and Lasso-based estimators based on mean squared error and model selection criteria.

Main Results:

  • M-Lasso demonstrates superior performance in terms of mean squared error compared to existing estimators.
  • M-Lasso generally dominates other estimators in model selection performance.
  • Application to a production function shows M-Lasso provides precise, theory-consistent parameter estimates.

Conclusions:

  • M-Lasso is a powerful tool for parameter estimation and model selection in big data settings with stochastic restrictions.
  • The proposed estimator offers improved accuracy and selection capabilities over existing methods.
  • M-Lasso provides reliable estimates aligned with economic theory, particularly in production function analysis.