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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Variable selection using a smooth information criterion for distributional regression models.

Meadhbh O'Neill¹, Kevin Burke¹

¹Department of Mathematics and Statistics, University of Limerick, Limerick, Republic of Ireland.

Statistics and Computing

|May 8, 2023

Summary

This summary is machine-generated.

This study introduces a novel smooth information criterion (SIC) for efficient variable selection and parameter estimation in statistical models. The SIC method automatically selects tuning parameters in one step, reducing computational intensity compared to traditional cross-validation methods.

Keywords:

Distributional regression Heteroscedasticity Information criteria Multiparameter regression Penalized maximum likelihood Variable selection

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Area of Science:

Statistical modeling
Computational statistics

Background:

Variable selection and estimation are crucial in modern statistics.
Penalization methods like the least absolute shrinkage and selection operator (LASSO) are popular but require computationally intensive tuning parameter selection.
Existing methods often rely on minimizing cross-validation error or Bayesian information criterion (BIC), which can be slow.

Purpose of the Study:

To develop a computationally efficient procedure for automatic tuning parameter selection in penalized regression models.
To extend this novel approach to the flexible distributional regression framework.
To leverage the relationship between information criteria and penalization for improved model selection.

Main Methods:

Developed a "smooth information criterion" (SIC) for one-step automatic tuning parameter selection.
Extended the SIC procedure to the distributional regression framework, also known as multiparameter regression.
Reformulated distributional regression estimation using penalized likelihood to integrate model selection criteria.

Main Results:

The proposed SIC procedure automatically selects the tuning parameter in a single step, significantly reducing computational load.
The extension to distributional regression allows for simultaneous modeling of multiple distributional parameters (e.g., mean and variance).
This approach simplifies model selection in complex scenarios, particularly those with heteroscedasticity, by avoiding the need to select multiple tuning parameters.

Conclusions:

The smooth information criterion (SIC) offers a computationally advantageous alternative for tuning parameter selection in penalized regression.
The application of SIC to distributional regression enhances flexibility and simplifies model selection in multiparameter settings.
This method streamlines statistical modeling, especially for processes exhibiting heteroscedasticity.