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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Variable selection for binary spatial regression: Penalized quasi-likelihood approach.

Wenning Feng¹, Abdhi Sarkar², Chae Young Lim³

¹American Express, New York, U.S.A.

|April 11, 2016

Summary

This summary is machine-generated.

This study introduces a penalized quasi-likelihood method for spatial regression with binary outcomes, enabling simultaneous covariate selection and parameter estimation. The approach is validated through simulations and real data, showing promise for spatial data analysis.

Keywords:

Binary response Increasing domain asymptotics LASSO MM algorithm Penalized quasi-likelihood SCAD Spatial regression Variable selection

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Area of Science:

Spatial statistics
Statistical modeling
Biostatistics

Background:

Covariate selection is crucial in spatial regression for accurate modeling.
Standard likelihood methods face challenges with spatially dependent binary data.
Penalized likelihood approaches offer simultaneous variable selection and estimation.

Purpose of the Study:

To develop a penalized quasi-likelihood method for spatial regression with binary outcomes.
To enable simultaneous covariate selection and parameter estimation in spatial models.
To provide an efficient computational algorithm and theoretical validation.

Main Methods:

Development of a penalized quasi-likelihood function incorporating spatial dependence.
Application of increasing domain asymptotics for theoretical analysis.
Validation through extensive simulation studies and real-world data examples.

Main Results:

The proposed method effectively performs simultaneous covariate selection and parameter estimation for spatial binary data.
Theoretical properties like asymptotic normality and consistency are established.
Empirical performance is demonstrated on real datasets, showing applicability.

Conclusions:

The penalized quasi-likelihood approach is a robust and efficient tool for spatial regression with binary data.
The method extends to other distributions within the exponential family, as suggested by preliminary investigations.
This work advances statistical methodologies for analyzing complex spatial dependencies.