Stress-Strength Parameter Estimation under Small Sample Size: A Testing Hypothesis Approach
Author(s) -
Hassan Alsuhabi,
Mohammad Mehdi Saber,
M. M. Abd ElRaouf
Publication year - 2021
Publication title -
computational intelligence and neuroscience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.605
H-Index - 52
eISSN - 1687-5273
pISSN - 1687-5265
DOI - 10.1155/2021/8705547
Subject(s) - estimator , sample size determination , inference , mathematics , statistics , exponential distribution , confidence interval , coverage probability , interval estimation , computer science , statistical inference , sample (material) , artificial intelligence , chemistry , chromatography
In this paper, uniformly most powerful unbiased test for testing the stress-strength model has been presented for the first time. The end of the paper is recommending a method which is appropriate for no large data where a normal asymptotic distribution is not applicable. The previous methods for inference on stress-strength models use almost all the asymptotic properties of maximum likelihood estimators. The distribution of components is considered exponential and generalized logistic. A corresponding unbiased confidence interval is constructed, too. We compare presented methodology with previous methods and show the method of this paper is logically better than other methods. Interesting result is that our recommended method not only uses from small sample size but also has better result than other ones.
Accelerating Research
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom
Address
John Eccles HouseRobert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom