Quantile estimation of stochastic frontiers with the normal-exponential specification

Author(s):  
Samah Jradi ◽  
Christopher F. Parmeter ◽  
John Ruggiero
Entropy ◽  
2021 ◽  
Vol 23 (1) ◽  
pp. 70
Author(s):  
Mei Ling Huang ◽  
Xiang Raney-Yan

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.


1976 ◽  
Vol 5 (3) ◽  
pp. 5-15
Author(s):  
G. W. J. Coppens ◽  
M. P. F. M. van Dongen ◽  
J. P. C. Kleijnen

2021 ◽  
Vol 55 (2) ◽  
pp. 87-108
Author(s):  
Mohammed Chowdhury ◽  
Bogdan Gadidov ◽  
Linh Le ◽  
Yan Wang ◽  
Lewis VanBrackle

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