scholarly journals Tests Based on Simplicial Depth for AR(1) Models With Explosion

2016 ◽  
Vol 37 (6) ◽  
pp. 763-784 ◽  
Author(s):  
Christoph P. Kustosz ◽  
Anne Leucht ◽  
Christine H. MÜller
Keyword(s):  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Fanyu Meng ◽  
Wei Shao ◽  
Yuxia Su

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 d and sample size n 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.


2014 ◽  
Vol 28 (1) ◽  
pp. 306-322 ◽  
Author(s):  
Antoine Deza ◽  
Frédéric Meunier ◽  
Pauline Sarrabezolles

2016 ◽  
Vol 173 ◽  
pp. 125-146 ◽  
Author(s):  
Christoph P. Kustosz ◽  
Christine H. Müller ◽  
Martin Wendler

2008 ◽  
Vol 92 (3) ◽  
pp. 235-253 ◽  
Author(s):  
Mario Romanazzi

2006 ◽  
Vol 35 (4) ◽  
pp. 597-615 ◽  
Author(s):  
Antoine Deza ◽  
Sui Huang ◽  
Tamon Stephen ◽  
Tamas Terlaky

2010 ◽  
Vol 101 (4) ◽  
pp. 824-838 ◽  
Author(s):  
Robin Wellmann ◽  
Christine H. Müller

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