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Variable selection for recurrent event data with broken adaptive ridge regression.

Hui Zhao¹, Dayu Sun², Gang Li³

¹School of Mathematics and Statistics & Hubei Key Laboratory of Mathematical Sciences, Central China Normal University, Wuhan, China.

The Canadian Journal of Statistics = Revue Canadienne De Statistique

|October 1, 2020

Summary

This summary is machine-generated.

This study introduces a new broken adaptive ridge regression method for analyzing recurrent event data. The approach improves variable selection and parameter estimation, outperforming existing methods in simulations.

Keywords:

Additive rate model MSC 2010: Primary 62N99 event history study recurrent event data secondary 62P10 variable selection

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Area of Science:

Statistics
Biostatistics
Econometrics

Background:

Recurrent event data analysis is crucial in medical and social sciences.
Existing variable selection methods for recurrent events are limited and may not be optimal.
Penalized procedures for linear models are often generalized, with suboptimal performance.

Purpose of the Study:

To develop a novel method for simultaneous parameter estimation and variable selection in recurrent event data analysis.
To introduce a new penalty function, termed the broken adaptive ridge regression approach.
To address limitations of existing variable selection techniques for recurrent event data.

Main Methods:

Proposing the broken adaptive ridge regression approach with a new penalty function.
Establishing the theoretical 'oracle property' for the new method.
Investigating the method's performance in numerical simulations.

Main Results:

The broken adaptive ridge regression method demonstrates the oracle property.
The proposed method exhibits a clustering or grouping effect with highly correlated covariates.
Numerical studies indicate superior performance compared to existing methods in practical scenarios.

Conclusions:

The broken adaptive ridge regression approach offers an effective solution for variable selection and parameter estimation with recurrent event data.
The method shows promise for real-world applications, particularly in complex datasets with correlated variables.
This work advances the methodology for analyzing recurrent event data, providing a more robust alternative to current techniques.