A computational algorithm for LU decomposition of a sequence of matrices that differ only in one or more elements

1989 ◽  
Vol 24 (4) ◽  
pp. 8-16
Author(s):  
M. S. Zahir
Keyword(s):  
Computational Algorithm ◽  
Lu Decomposition

Localization Algorithm of Sparse Targets Based on LU-decomposition

2014 ◽  
Vol 35 (9) ◽  
pp. 2234-2239 ◽  
Author(s):  
Chun-hui Zhao ◽  
Yun-long Xu ◽  
Hui Huang
Keyword(s):  
Lu Decomposition ◽  
Localization Algorithm

scholarly journals A Method for Confidence Intervals of High Quantiles

Entropy ◽  
2021 ◽  
Vol 23 (1) ◽  
pp. 70
Author(s):  
Mei Ling Huang ◽  
Xiang Raney-Yan
Keyword(s):  
Confidence Intervals ◽  
Computational Algorithm ◽  
Geometric Mean ◽  
Asymptotic Properties ◽  
Quantile Estimation ◽  
Heavy Tailed Distributions ◽  
High Quantiles ◽  
High Quantile ◽  
Heavy Tailed ◽  
Infinite Moments

The high quantile estimation of heavy tailed distributions has many important applications. There are theoretical difficulties in studying heavy tailed distributions since they often have infinite moments. There are also bias issues with the existing methods of confidence intervals (CIs) of high quantiles. This paper proposes a new estimator for high quantiles based on the geometric mean. The new estimator has good asymptotic properties as well as it provides a computational algorithm for estimating confidence intervals of high quantiles. The new estimator avoids difficulties, improves efficiency and reduces bias. Comparisons of efficiencies and biases of the new estimator relative to existing estimators are studied. The theoretical are confirmed through Monte Carlo simulations. Finally, the applications on two real-world examples are provided.

Sign in / Sign up

Export Citation Format

Share Document