scholarly journals Confidence interval estimation by bootstrap method for uncertainty quantification using random sampling method

2015 ◽  
Vol 52 (7-8) ◽  
pp. 993-999 ◽  
Author(s):  
Tomohiro Endo ◽  
Tomoaki Watanabe ◽  
Akio Yamamoto
2021 ◽  
Vol 4 (1) ◽  
pp. 93-100
Author(s):  
Larasati Larasati ◽  
Rudi Ruswandi ◽  
Fitriani Fitriani

Confidence interval estimation of parameters determines the value interval, which is calculated based on statistical measurements and has specific estimates probability that contains the actual parameters. A method is needed to estimate the parameters' confidence interval, and the methods used are the Score method and the Bootstrap method. This study aims to estimate parameters by using the maximum likelihood estimation method and analyze the reliability of the aircraft engine cooling system's lifetime that follows the Gamma Distribution, and estimate the confidence interval of the parameters.


2020 ◽  
Vol 1/2020 (13) ◽  
pp. 40-50
Author(s):  
Jarno Klaudia ◽  
◽  
Smaga Łukasz ◽  

This paper is aimed at presenting application of bootstrap interval estimation methods to the assessment of financial investment’s effectiveness and risk. At first, we give an overview of various methods of bootstrap confidence interval estimation, i.e. bootstrap-t interval, percentile interval and BCa interval. Then, bootstrap confidence interval estimation methods are used to estimate confidence intervals for the Sharpe ratio and TailVaR of the Warsaw Stock Exchange sectoral indices. The results show that the bootstrap confidence intervals of different types are quite similarly positioned for each of the analysed index and measure. Taking into the account the locations of confidence intervals for both the Sharpe ratio and TailVaR, the real estate sector tends to be the most advantageous from the investor’s viewpoint.


Disasters ◽  
2009 ◽  
Vol 34 (1) ◽  
pp. 164-175 ◽  
Author(s):  
Kevin Sullivan ◽  
S.M. Moazzem Hossain ◽  
Bradley A. Woodruff

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