Framework and Case Study for Optimizing Target Value of a Process Using Taguchi’s Asymmetric Loss Function

2021 ◽  
pp. 189-196
Author(s):  
Anoop Pandey ◽  
Manish Chaturvedi
Keyword(s):  
Loss Function ◽  
Asymmetric Loss ◽  
Target Value ◽  
Asymmetric Loss Function

A Note on Forecasting the Euro: Do Forecasters Have an Asymmetric Loss Function?

2013 ◽  
Vol 28 (2) ◽  
pp. 333-343 ◽  
Author(s):  
Ulrich Fritsche ◽  
Christian Pierdzioch ◽  
Jan-Christoph Rülke ◽  
Georg Stadtmann
Keyword(s):  
Loss Function ◽  
Asymmetric Loss ◽  
Asymmetric Loss Function

scholarly journals Bayesian Estimates Based On Record Values Under Weighted LINEX Loss Function

2020 ◽  
pp. 11-19 ◽  
Author(s):  
Fuad Al-Duais ◽  
Mohammed Alhagyan
Keyword(s):  
Weibull Distribution ◽  
Loss Function ◽  
Scale Parameter ◽  
Reliability Function ◽  
Record Values ◽  
Linex Loss Function ◽  
Asymmetric Loss ◽  
Bayes Estimates ◽  
Linex Loss ◽  
Asymmetric Loss Function

In this paper, we developed linear exponential (LINEX) loss function by emerging weights to produce weighted linear exponential (WLINEX) loss function. Then we utilized WLINEX to derive scale parameter and reliability function of the Weibull distribution based on record values when the shape parameter is known. After, we estimated scale parameter and reliability function of Weibull distribution by using maximum likelihood (ML) estimation and by several Bayes estimations.  The Bayes estimates were obtained with respect to symmetric loss function (squared error loss (SEL)), asymmetric loss function (LINEX) and asymmetric loss function (WLINEX). The ML and the different Bayes estimates are compared via a Monte Carlo simulation study. The result of simulation mentioned that the proposed WLINEX loss function is promising and can be used in real environment especially at the case of underestimate where it revealed better performance than LINEX loss function for estimating scale parameter.

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