國立臺北大學統計學系

專題演講

 

講題：Variable Selection for High-Dimensional Regression Models with Higher-Order Interactions

主講人：黃學涵 助研究員 (中央研究院統計科學研究所)

時間：115年05月13日 (星期三，13：10~15：00)

地點：三峽校區商學院3F13教室

Abstract

This work proposes the network orthogonal greedy algorithm (Network OGA), an efficient method designed to capture higher-order (beyond second-order) interactions. By integrating the concepts of ranking and stepwise forward regression, Network OGA leverages the advantages of both approaches. The algorithm is applicable to high-dimensional interaction models of arbitrary unknown orders. We establish the sure screening property for Network OGA and demonstrate that, when coupled with a high-dimensional information criterion (HDIC), the method achieves variable selection consistency. Simulation studies and a real data analysis further validate its superior performance.

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國立臺北大學統計學系  敬邀

115.05.10

附件：演講摘要