Advertisement
Published in:
2012 | OriginalPaper | Chapter
A Practical Variable Selection for Linear Models
Authors : Hidehisa Noguchi, Yoshikazu Ojima, Seiichi Yasui
Published in: Frontiers in Statistical Quality Control 10
Publisher: Physica-Verlag HD
Activate our intelligent search to find suitable subject content or patents.
Select sections of text to find matching patents with Artificial Intelligence. powered by
Select sections of text to find additional relevant content using AI-assisted search. powered by
Abstract
In the analysis of experiments, there are many variable selection algorithms for linear models. Most of these approaches select the best model based on some criteria such as AIC. These criteria do not allow for any relationship between predictors. However, in practice, the analysis is driven by following three principles: Effect Hierarchy, Effect Sparsity, and Effect Heredity Principle. The approach depending solely on those criteria ignore these principles, so it would often select a hard to interpretable models, for instance, which are consisted with only interaction terms. In this article, we extend the LASSO method to identify significant interaction terms mainly focusing on the heredity principle. And we compare the proposed method with ordinary LASSO and traditional variable selection approach. In the example, we analyze the data obtained from designed experiments such as Placket-Burman design and supersaturated design.