A Prospect-Theory Approach to the Kelly Criterion for Fat-Tail Portfolios: The Case of the Student T-Distribution

2008 ◽  
Author(s):  
Roberto Osorio
Keyword(s):  
Prospect Theory ◽  
Theory Approach ◽  
Kelly Criterion ◽  
Fat Tail ◽  
Student T Distribution ◽  
T Distribution

scholarly journals A Robust SMC-PHD Filter for Multi-Target Tracking with Unknown Heavy-Tailed Measurement Noise

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3611
Author(s):  
Yang Gong ◽  
Chen Cui
Keyword(s):  
Target Tracking ◽  
Sequential Monte Carlo ◽  
Measurement Noise ◽  
Particle Simulation ◽  
Target Number ◽  
Student T Distribution ◽  
Multi Target Tracking ◽  
Heavy Tailed ◽  
Scale Matrix ◽  
T Distribution

In multi-target tracking, the sequential Monte Carlo probability hypothesis density (SMC-PHD) filter is a practical algorithm. Influenced by outliers under unknown heavy-tailed measurement noise, the SMC-PHD filter suffers severe performance degradation. In this paper, a robust SMC-PHD (RSMC-PHD) filter is proposed. In the proposed filter, Student-t distribution is introduced to describe the unknown heavy-tailed measurement noise where the degrees of freedom (DOF) and the scale matrix of the Student-t distribution are respectively modeled as a Gamma distribution and an inverse Wishart distribution. Furthermore, the variational Bayesian (VB) technique is employed to infer the unknown DOF and scale matrix parameters while the recursion estimation framework of the RSMC-PHD filter is derived. In addition, considering that the introduced Student- t distribution might lead to an overestimation of the target number, a strategy is applied to modify the updated weight of each particle. Simulation results demonstrate that the proposed filter is effective with unknown heavy-tailed measurement noise.

scholarly journals Power Birnbaum-Saunders Student t distribution

2017 ◽  
Vol 35 (1) ◽  
pp. 51-70
Author(s):  
Germán Moreno-Arenas ◽  
◽  
Guillermo Martínez-Flórez ◽  
Heleno Bolfarine ◽  
◽  
...  
Keyword(s):  
Student T Distribution ◽  
T Distribution
Sign in / Sign up

Export Citation Format

Share Document