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  • Statistics In Transition

 

Research Article | 27-May-2018

POWER ISHITA DISTRIBUTION AND ITS APPLICATION TO MODEL LIFETIME DATA

A study on two-parameter power Ishita distribution (PID), of which Ishita distribution introduced by Shanker and Shukla (2017 a) is a special case, has been carried out and its important statistical properties including shapes of the density, moments, skewness and kurtosis measures, hazard rate function, and stochastic ordering have been discussed. The maximum likelihood estimation has been discussed for estimating its parameters. An application of the distribution has been explained with a

Kamlesh Kumar Shukla, Rama Shanker

Statistics in Transition New Series, Volume 19 , ISSUE 1, 135–148

Research Article | 13-December-2018

DEVELOPING SINGLE-ACCEPTANCE SAMPLING PLANS BASED ON A TRUNCATED LIFETIME TEST FOR AN ISHITA DISTRIBUTION

Acceptance sampling plans are statistical procedures that are used for quality control and improvement in cases where it is not possible to test every item in a lot of materials. The outcome of this test determines whether the entire lot is accepted or rejected based on a random sample. In this procedure, an important characteristic of the materials is their lifetime sampling distribution, and this can vary from sample to sample. In this article, a new lifetime distribution, known as an Ishita

Amjad D. Al-Nasser, Amer I. Al-Omari, Ahmed Bani-Mustafa, Khalifa Jaber

Statistics in Transition New Series, Volume 19 , ISSUE 3, 393–406

Article | 20-September-2020

Poisson Weighted Ishita Distribution: Model for Analysis of Over-Dispersed Medical Count Data

A new over-dispersed discrete probability model is introduced, by compounding the Poisson distribution with the weighted Ishita distribution. The statistical properties of the newly introduced distribution have been derived and discussed. Parameter estimation has been done with the application of the maximum likelihood method of estimation, followed by the Monte Carlo simulation procedure to examine the suitability of the ML estimators. In order to verify the applicability of the proposed

Bilal Ahmad Para, Tariq Rashid Jan

Statistics in Transition New Series, Volume 21 , ISSUE 3, 171–184

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