ANOVA Assisted Variable Selection in High-dimensional Multicategory Response Data

Abstract

Multinomial logistic regression is preferred in the classification of multicategory response data for its ease of interpretation and the ability to identify the associated input variables for each category. However, identifying important input variables in high-dimensional data poses several challenges as the majority of variables are unnecessary in discriminating the categories. Frequently used techniques in identifying important input variables in high-dimensional data include regularisation techniques such as Least Absolute Selection Shrinkage Operator (LASSO) and sure independent screening (SIS) or combinations of both. In this paper, we propose to use ANOVA, to assist the SIS in variable screening for high-dimensional data when the response variable is multicategorical. The new approach is straightforward and computationally effective. Simulated data without and with correlation are generated for numerical studies to illustrate the methodology, and the results of applying the methods on real data are presented. In conclusion, ANOVA performance is comparable with SIS in variable selection for uncorrelated input variables and performs better when used in combination with both ANOVA and SIS for correlated input variables.