publication . Preprint . 2017

Parameter Selection Algorithm For Continuous Variables

Tavallali, Peyman; Razavi, Marianne; Brady, Sean;
Open Access English
  • Published: 19 Jan 2017
Abstract
In this article, we propose a new algorithm for supervised learning methods, by which one can both capture the non-linearity in data and also find the best subset model. To produce an enhanced subset of the original variables, an ideal selection method should have the potential of adding a supplementary level of regression analysis that would capture complex relationships in the data via mathematical transformation of the predictors and exploration of synergistic effects of combined variables. The method that we present here has the potential to produce an optimal subset of variables, rendering the overall process of model selection to be more efficient. The cor...
Subjects
free text keywords: Statistics - Applications, Statistics - Methodology, Statistics - Machine Learning
Funded by
NIH| THE FRAMINGHAM HEART STUDY-268025195
Project
  • Funder: National Institutes of Health (NIH)
  • Project Code: N01HC025195-005
  • Funding stream: DIVISION OF EPIDEMIOLOGY AND CLINICAL APPLICATIONS
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3.1. Values for ς. In fact, the independence limit ς can be characterized with the VIF concept. As mentioned, V IFj = Cjj , however, this formula can also be written as

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