scholarly journals Microscopic origin of non-Gaussian distributions of financial returns

2008 ◽  
Vol 387 (7) ◽  
pp. 1603-1612 ◽  
Author(s):  
T.S. Biró ◽  
R. Rosenfeld
Keyword(s):  
Financial Returns ◽  
Gaussian Distributions ◽  
Non Gaussian ◽  
Microscopic Origin

scholarly journals Emergence of skewed non-Gaussian distributions of velocity increments in isotropic turbulence

2019 ◽  
Vol 4 (6) ◽  
Author(s):  
W. Sosa-Correa ◽  
R. M. Pereira ◽  
A. M. S. Macêdo ◽  
E. P. Raposo ◽  
D. S. P. Salazar ◽  
...  
Keyword(s):  
Isotropic Turbulence ◽  
Gaussian Distributions ◽  
Velocity Increments ◽  
Non Gaussian

scholarly journals DETERMINING SOURCE CUMULANTS IN FEMTOSCOPY WITH GRAM–CHARLIER AND EDGEWORTH SERIES

2011 ◽  
Vol 26 (24) ◽  
pp. 1771-1782 ◽  
Author(s):  
H. C. EGGERS ◽  
M. B. DE KOCK ◽  
J. SCHMIEGEL
Keyword(s):  
Central Limit Theorem ◽  
Limit Theorem ◽  
Central Limit ◽  
Fourth Order ◽  
Momentum Space ◽  
Crucial Role ◽  
Gaussian Distributions ◽  
Edgeworth Series ◽  
The Central Limit Theorem ◽  
Non Gaussian

Lowest-order cumulants provide important information on the shape of the emission source in femtoscopy. For the simple case of noninteracting identical particles, we show how the fourth-order source cumulant can be determined from measured cumulants in momentum space. The textbook Gram–Charlier series is found to be highly inaccurate, while the related Edgeworth series provides increasingly accurate estimates. Ordering of terms compatible with the Central Limit Theorem appears to play a crucial role even for non-Gaussian distributions.

scholarly journals Microscopic origin of the non-Gaussian behavior of dynamic structure factors of glassy matter

2004 ◽  
Vol 69 (13) ◽  
Author(s):  
C. Cabrillo ◽  
M. A. González ◽  
G. J. Cuello ◽  
F. J. Bermejo ◽  
M. L. Saboungi ◽  
...  
Keyword(s):  
Dynamic Structure ◽  
Structure Factors ◽  
Non Gaussian ◽  
Microscopic Origin

scholarly journals A projection pricing model for non-Gaussian financial returns

2019 ◽  
Vol 534 ◽  
pp. 122181 ◽  
Author(s):  
Ana Flávia P. Rodrigues ◽  
Charles C. Cavalcante ◽  
Vicente L. Crisóstomo
Keyword(s):  
Pricing Model ◽  
Financial Returns ◽  
Non Gaussian

Control charts for non-Gaussian distributions

2007 ◽  
Author(s):  
Florina Babus ◽  
Abdessamad Kobi ◽  
Th. Tiplica ◽  
Ioan Bacivarov ◽  
Angelica Bacivarov
Keyword(s):  
Control Charts ◽  
Gaussian Distributions ◽  
Non Gaussian

Reliability-Based Design Optimization With Confidence Level for Non-Gaussian Distributions Using Bootstrap Method

2011 ◽  
Vol 133 (9) ◽  
Author(s):  
Yoojeong Noh ◽  
Kyung K. Choi ◽  
Ikjin Lee ◽  
David Gorsich ◽  
David Lamb
Keyword(s):  
Statistical Model ◽  
Design Optimization ◽  
Confidence Intervals ◽  
Confidence Level ◽  
Bootstrap Method ◽  
Random Variables ◽  
Reliability Based Design Optimization ◽  
Gaussian Distributions ◽  
Reliability Based Design ◽  
Non Gaussian

For reliability-based design optimization (RBDO), generating an input statistical model with confidence level has been recently proposed to offset inaccurate estimation of the input statistical model with Gaussian distributions. For this, the confidence intervals for the mean and standard deviation are calculated using Gaussian distributions of the input random variables. However, if the input random variables are non-Gaussian, use of Gaussian distributions of the input variables will provide inaccurate confidence intervals, and thus yield an undesirable confidence level of the reliability-based optimum design meeting the target reliability βt. In this paper, an RBDO method using a bootstrap method, which accurately calculates the confidence intervals for the input parameters for non-Gaussian distributions, is proposed to obtain a desirable confidence level of the output performance for non-Gaussian distributions. The proposed method is examined by testing a numerical example and M1A1 Abrams tank roadarm problem.

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