scholarly journals On unbiased Lehmann-estimators of a variance of an exponential distribution with quadratic loss function

1982 ◽  
Vol 33 (2) ◽  
pp. 79-96 ◽  
Author(s):  
Jadwiga Kicinska-Slaby
Keyword(s):  
Exponential Distribution ◽  
Loss Function ◽  
Quadratic Loss ◽  
Quadratic Loss Function

scholarly journals On A Shape Parameter of Gompertz Inverse Exponential Distribution Using Classical and Non Classical Methods of Estimation

2020 ◽  
pp. 1-10
Author(s):  
Terna Godfrey Ieren ◽  
Adana’a Felix Chama ◽  
Olateju Alao Bamigbala ◽  
Jerry Joel ◽  
Felix M. Kromtit ◽  
...  
Keyword(s):  
Exponential Distribution ◽  
Loss Function ◽  
Real Life ◽  
Quadratic Loss ◽  
Prior Distributions ◽  
Quadratic Loss Function ◽  
Informative Prior ◽  
Informative Prior Distribution ◽  
Inverse Exponential Distribution ◽  
Bayesian Estimators

The Gompertz inverse exponential distribution is a three-parameter lifetime model with greater flexibility and performance for analyzing real life data. It has one scale parameter and two shape parameters responsible for the flexibility of the distribution. Despite the importance and necessity of parameter estimation in model fitting and application, it has not been established that a particular estimation method is better for any of these three parameters of the Gompertz inverse exponential distribution. This article focuses on the development of Bayesian estimators for a shape of the Gompertz inverse exponential distribution using two non-informative prior distributions (Jeffery and Uniform) and one informative prior distribution (Gamma prior) under Square error loss function (SELF), Quadratic loss function (QLF) and Precautionary loss function (PLF). These results are compared with the maximum likelihood counterpart using Monte Carlo simulations. Our results indicate that Bayesian estimators under Quadratic loss function (QLF) with any of the three prior distributions provide the smallest mean square error for all sample sizes and different values of parameters.

Applying the Quality Loss Function in Healthcare

2008 ◽  
pp. 102-107
Author(s):  
Elizabeth Cudney ◽  
Bonnie Paris
Keyword(s):  
Health Care ◽  
Loss Function ◽  
Health Care Services ◽  
Quadratic Loss ◽  
Healthcare Industry ◽  
Quality Loss ◽  
Quality Loss Function ◽  
Quadratic Loss Function ◽  
Care Services ◽  
Fundamental Value

Using the quadratic loss function is one way to quantify a fundamental value in the provision of health care services: we must provide the best care and best service to every patient, every time. Sole reliance on specification limits leads to a focus on “acceptable” performance rather than “ideal” performance. This paper presents the application of the quadratic loss function to quantify improvement opportunities in the healthcare industry.

scholarly journals SVQR with asymmetric quadratic loss function

2015 ◽  
Vol 26 (6) ◽  
pp. 1537-1545 ◽  
Author(s):  
Jooyong Shim ◽  
Malsuk Kim ◽  
Kyungha Seok
Keyword(s):  
Loss Function ◽  
Quadratic Loss ◽  
Quadratic Loss Function
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