Fourth-Order Statistics: Weak Fluctuation Theory

2009 ◽  
pp. 257-319
Keyword(s):  
Order Statistics ◽  
Fourth Order ◽  
Fluctuation Theory

SNR estimation of speech signals using subbands and fourth-order statistics

1999 ◽  
Vol 6 (7) ◽  
pp. 171-174 ◽  
Author(s):  
E. Nemer ◽  
R. Goubran ◽  
S. Mahmoud
Keyword(s):  
Order Statistics ◽  
Fourth Order ◽  
Speech Signals ◽  
Snr Estimation

Order determination of MA models using third- and fourth-order statistics

1998 ◽  
Vol 65 (3) ◽  
pp. 403-406 ◽  
Author(s):  
Diego P Ruiz ◽  
Antolino Gallego ◽  
Marı́a C Carrión ◽  
Jorge Portı́
Keyword(s):  
Order Statistics ◽  
Fourth Order ◽  
Order Determination

scholarly journals The Expected Utility Insurance Premium Principle with Fourth-Order Statistics: Does It Make a Difference?

Algorithms ◽  
2020 ◽  
Vol 13 (5) ◽  
pp. 116
Author(s):  
Alessandro Mazzoccoli ◽  
Maurizio Naldi
Keyword(s):  
Order Statistics ◽  
Fourth Order ◽  
Expected Utility ◽  
Value At Risk ◽  
Order Approximation ◽  
Insurance Premium ◽  
Second Order ◽  
Accurate Approximation ◽  
Increased Risk ◽  
The Impact

The expected utility principle is often used to compute the insurance premium through a second-order approximation of the expected value of the utility of losses. We investigate the impact of using a more accurate approximation based on the fourth-order statistics of the expected loss and derive the premium under this expectedly more accurate approximation. The comparison between the two approximation levels shows that the second-order-based premium is always lower (i.e., an underestimate of the correct one) for the commonest loss distributions encountered in insurance. The comparison is also carried out for real cases, considering the loss parameters values estimated in the literature. The increased risk of the insurer is assessed through the Value-at-Risk.

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