Asymptotic Properties of the CRT Estimators for Multicentre Studies

Author(s):  
Vladimir V. Anisimov ◽  
Valerii V. Fedorov
2021 ◽  
Vol 108 (3) ◽  
pp. 231-232
Author(s):  
M Sund

Abstract In the March issue of BJS several hot topics within the breast surgery field are highlighted in beautifully planned and executed prospective multicentre trials. BJS encourages the surgical communities in most fields to move towards prospective collaborative and multicentre studies, thereby increasing both power and generalizability as well as reducing the risk of bias.


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.


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