Identifying a minimal class of models for high-dimensional data

Daniel Nevo; Ya'acov Ritov

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arXiv · 1504.00494

Identifying a minimal class of models for high-dimensional data

Daniel Nevo·Ya'acov Ritov

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Abstract

Model selection consistency in the high-dimensional regression setting can be achieved only if strong assumptions are fulfilled. We therefore suggest to pursue a different goal, which we call a minimal class of models. The minimal class of models includes models that are similar in their prediction accuracy but not necessarily in their elements. We suggest a random search algorithm to reveal candidate models. The algorithm implements simulated annealing while using a score for each predictor that we suggest to derive using a combination of the Lasso and the Elastic Net. The utility of using a minimal class of models is demonstrated in the analysis of two datasets.

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Daniel Nevo, Ya'acov Ritov. 2015-04-02. Identifying a minimal class of models for high-dimensional data. https://arxiv.org/abs/1504.00494

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