Computing Simplicial Depth by Using Importance Sampling Algorithm and Its Application

Fanyu Meng, Wei Shao and Yuxia Su

Mathematical Problems in Engineering, 2021, vol. 2021, 1-11

Abstract:

Simplicial depth (SD) plays an important role in discriminant analysis, hypothesis testing, machine learning, and engineering computations. However, the computation of simplicial depth is hugely challenging because the exact algorithm is an NP problem with dimension and sample size as input arguments. The approximate algorithm for simplicial depth computation has extremely low efficiency, especially in high-dimensional cases. In this study, we design an importance sampling algorithm for the computation of simplicial depth. As an advanced Monte Carlo method, the proposed algorithm outperforms other approximate and exact algorithms in accuracy and efficiency, as shown by simulated and real data experiments. Furthermore, we illustrate the robustness of simplicial depth in regression analysis through a concrete physical data experiment.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:6663641

DOI: 10.1155/2021/6663641

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