CMStatistics 2022: Start Registration
View Submission - CMStatistics
Title: On cross-distance selection algorithm for hybrid sufficient dimension reduction Authors:  Yujin Park - Ewha Womans University (Korea, South) [presenting]
Kyongwon Kim - Ewha Womans University (Korea, South)
Jae Keun Yoo - Ewha Womans University (Korea, South)
Abstract: Given the extensive development of a variety of sufficient dimension reduction (SDR) methodologies, a hybrid SDR method was proposed combining two pre-existing SDR methods. In particular, a bootstrap approach was used to select a proper weight. Since bootstrapping is computationally intensive and time-consuming, the hybrid reduction approach has not been widely used, although it is more accurate than conventional single SDR methods. To overcome these deficits, we propose a novel cross-distance selection algorithm. Similar to the bootstrapping method, the proposed selection algorithm is data-driven and has a strong rationale for its performance. The numerical studies demonstrate that the chosen hybrid method from our proposed algorithm offers a good estimation quality and reduces the computing time dramatically at the same time. Furthermore, our real data analysis confirms that the proposed selection algorithm has potential advantages with its practical usefulness over the existing bootstrapping method.