Variable sampling plans for normal distribution indexed by Taguchi's loss function

1997 ◽  
Vol 44 (6) ◽  
pp. 591-603 ◽  
Author(s):  
Ikuo Arizono ◽  
Akihiro Kanagawa ◽  
Hiroshi Ohta ◽  
Kyouko Watakabe ◽  
Kouji Tateishi
Keyword(s):  
Normal Distribution ◽  
Loss Function ◽  
Sampling Plans ◽  
Variable Sampling ◽  
Taguchi’S Loss Function

Taguchi's Loss Function in the Weight Quality of Products: Case Study of Cheese Making

2021 ◽  
pp. 453-466
Author(s):  
Nicolás Francisco Mateo-Díaz ◽  
Lidilia Cruz-Rivero ◽  
Luis Adolfo Meraz-Rivera ◽  
Jesús Ortiz-Martínez
Keyword(s):  
Loss Function ◽  
Cheese Making ◽  
Quality Of Products ◽  
Taguchi’S Loss Function

Designing variable sampling plans based on lifetime performance index under failure censoring reliability tests

2020 ◽  
Vol 32 (3) ◽  
pp. 354-370
Author(s):  
Hasan Rasay ◽  
Farnoosh Naderkhani ◽  
Amir Mohammad Golmohammadi
Keyword(s):  
Performance Index ◽  
Sampling Plans ◽  
Lifetime Performance ◽  
Variable Sampling

Inadmissibility of the Maximum Likelihood Estimator in the Presence of Prior Information

1970 ◽  
Vol 13 (3) ◽  
pp. 391-393 ◽  
Author(s):  
B. K. Kale
Keyword(s):  
Maximum Likelihood ◽  
Normal Distribution ◽  
Maximum Likelihood Estimator ◽  
Loss Function ◽  
Prior Information ◽  
Mean Squared Error ◽  
Closed Interval ◽  
Likelihood Estimator ◽  
Usual Estimator ◽  
Squared Error

Lehmann [1] in his lecture notes on estimation shows that for estimating the unknown mean of a normal distribution, N(θ, 1), the usual estimator is neither minimax nor admissible if it is known that θ belongs to a finite closed interval [a, b] and the loss function is squared error. It is shown that , the maximum likelihood estimator (MLE) of θ, has uniformly smaller mean squared error (MSE) than that of . It is natural to ask the question whether the MLE of θ in N(θ, 1) is admissible or not if it is known that θ ∊ [a, b]. The answer turns out to be negative and the purpose of this note is to present this result in a slightly generalized form.

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