Bagging for the highly adaptive lasso Open Access

Yu, Haoyong (Spring 2020)

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Prediction is a common goal in the statistic world. Many new estimators are created every year to seek better quality of prediction in various situation. A new estimator called highly adaptive lasso estimator was proved to be competitive with other popular machine learning methods and had theoretical advantages. Furthermore, the prediction performance of this estimator may be furthered by combining with some unique methods. Bagging is a common ensemble method that can be utilized to improve the performance of prediction. Feature bagging is a promising usage of traditional bagging method. We propose a new estimator that we call bagged highly adaptive lasso estimator based on feature bagging approach. We show via simulation and public data analysis that our estimator seems not provide more benefits by additional aggregating bootstrap procedures.

Table of Contents

1.      Introduction 1

2.      Methods 2

2.1   Highly adaptive lasso 2

2.2   Bagging 3

2.3   Bagged highly adaptive LASSO 4

3.      Simulation 4

4.      Data Analysis 9

5.      Conclusion 10

References 12

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