Estimation and Testing in Type-II Generalized Half Logistic Distribution

Kantam, R. R. L.; Ramakrishna, V.; Ravikumar, Muppirala

doi:10.22237/jmasm/1398917760

Cited by 8 publications

(5 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this paper, we discussed proposed a GASP for a resubmitted lots when the lifetime of the product follows the Type II Generalized half-logistic distribution proposed by Kantam et al [37].…”

Section: Design Of Group Acceptance Sampling Plan For Resubmitted Lotsmentioning

confidence: 99%

Group Acceptance Sampling Plans for Resubmitted Lots UnderType II Generalized Half Logistic Distribution

2020

View full text Add to dashboard Cite

In this research article, we build-up a group acceptance sampling plan (GASP) for a resubmitting lot when the lifetime of a product follow the Type II Generalized half-logistic distribution (GHLD). The design parameters of the group acceptance sampling plan for the resubmitted are determined by fixing the experiment termination time and the numbers of testers at the stated producer’s and consumer’s risks both satisfy at the same time. We contrast the proposed GASP with the ordinary group sampling plan and the results are demonstrated with a live data set.

show abstract

Section: Design Of Group Acceptance Sampling Plan For Resubmitted Lotsmentioning

confidence: 99%

Group Acceptance Sampling Plans for Resubmitted Lots UnderType II Generalized Half Logistic Distribution

2020

View full text Add to dashboard Cite

show abstract

“…Olopade (2008) [3] considered two distributions, named type-I and type-III generalized half-logistic distributions. Kantam et al (2014) [4] proposed a type-II generalized half-logistic distribution (GHLD-II for short). For the purpose of this paper, a brief presentation of the GHLD-II is necessary.…”

Section: Introductionmentioning

confidence: 99%

“…The flexibility of the GHLD-II is mainly in the mode and tail of the distribution, making it an interesting distribution for the modeling of lifetime phenomena. It is proven to define a better model than the exponential, Weibull, and half-logistic models (see Kantam et al (2014) [4]).…”

Section: Introductionmentioning

confidence: 99%

Bivariate Generalized Half-Logistic Distribution: Properties and Its Application in Household Financial Affordability in KSA

Hassan

Chesneau

2022

MCA

View full text Add to dashboard Cite

The generalized half-logistic distribution is ideal to fit the lifetime of some products, such as ball bearings and electrical insulation. In this paper, we aim to extend this scope by creating a motivated bivariate version. We thus introduce the bivariate generalized half-logistic distribution using the Farlie Gumbel Morgenstern (FGM) copula, which is called the FGM bivariate generalized half-logistic distribution (FGMBGHLD for short). In particular, the FGMBGHLD finds application in describing bivariate lifetime datasets that have weak correlations between variables. Some statistical properties and functions of our new distribution, such as the product moments, moment generating function, reliability function, and hazard rate function, are derived. We discuss the maximum likelihood estimation method of the FGMBGHLD parameters. As an application of the FGMBGHLD in reliability, we consider the stress–strength model when the stress and strength random variables are dependent. We also derive the point and interval estimates of the stress–strength coefficient. Finally, we use the data from the household income and expenditure survey of KSA 2018 for Saudi households by administrative region to demonstrate the practicability of the proposed model. A comparison with a modern bivariate Weibull distribution is performed.

show abstract

“…and Anjaneyulu, G. V. S. R. (2011) studied how the least square method be good for estimating the parameters to Two Parameter Weibull Distribution from an optimally constructed grouped sample. Kantam et al (2013) discussed the estimation and testing in Type I generalized half logistic distribution. Rama Mohan, CH and Anjaneyulu, GVSR (2014) was studied estimation of scale and shape parameters of type I generalized half logistic distribution using Median Ranks Method.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Location (μ) and Scale (λ) for Two-Parameter Half Logistic Pareto Distribution (HLPD) by Least Square Regression Method

Vijayalakshmi¹

2019

IJRASET

View full text Add to dashboard Cite

In this paper, we propose the estimation of Location (µ) and Scale (λ) parameters using two step least square estimation method. We also computed Average Estimate (AE), Variance (VAR), Standard Deviation (STD), Mean Absolute Deviation (MAD), Mean Square Error (MSE), Simulated Error (SE) and Relative Absolute Bias (RAB) for both the parameters under complete sample based on 1000 simulations to assess the performance of the estimators. In this paper, finally we shows that the best performance of Least Square Regression Estimation Method.

show abstract

Estimation and Testing in Type-II Generalized Half Logistic Distribution

Cited by 8 publications

References 7 publications

Group Acceptance Sampling Plans for Resubmitted Lots UnderType II Generalized Half Logistic Distribution

Group Acceptance Sampling Plans for Resubmitted Lots UnderType II Generalized Half Logistic Distribution

Bivariate Generalized Half-Logistic Distribution: Properties and Its Application in Household Financial Affordability in KSA

Estimation of Location (μ) and Scale (λ) for Two-Parameter Half Logistic Pareto Distribution (HLPD) by Least Square Regression Method

Contact Info

Product

Resources

About