Optimal design of variable acceptance sampling plans for mixture distribution

Ramyamol, P. C.; Kumar, M.

doi:10.1080/02664763.2019.1610162

Cited by 7 publications

(5 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The data sets used in the manuscript are taken from open sources. Data presented in Table 4 and Table 5 are obtained from Ramyamol and Kumar (2019) and Collett (2023), respectively.…”

Section: Discussionmentioning

confidence: 99%

“…A sample of size n = 48 with ρ = 0.3098 is taken as in Ramyamol and Kumar (2019). ASP under PTIICS-BR using the linear life-stress model is applied to this exponential-Rayleigh data since its ETC is lower than the Arrhenius model (from Table 3).…”

Section: Comparative Analysis Using a Real-life Datamentioning

confidence: 99%

“…Hence, we accept the lot with ETC = 1154.2971 units since ϑ + ϕ > t 2 . Meanwhile, in Ramyamol and Kumar (2019), they accept the lot with a lower ETC for exponential mixture under type-I censoring, even though testing is stopped at T = 2000 units. Their ETC hinges primarily on the cost of failed items alone.…”

Section: Comparative Analysis Using a Real-life Datamentioning

confidence: 99%

“…Despite the significance of mixture distributions in several practical statistical areas, variable acceptance sampling plans (ASPs) for mixed distributions are not well addressed in the literature. Ramyamol and Kumar (2019) derived single and sequential variable sampling plans for a mixture of two exponential distributions. They have also defined two different sequential sampling plans and optimal plan parameters are obtained by minimizing total testing cost.…”

Section: Start Endmentioning

confidence: 99%

“…In this section, the proposed ASPs are demonstrated using a real-life case, and a comparative study is done with an existing model in the literature. For that, we consider a dataset used in Ramyamol and Kumar (2019). Their study revolves around the duration until the failure of ARC-1 VHF communication transmitter-receivers in a particular commercial airline.…”

Section: Comparative Analysis Using a Real-life Datamentioning

confidence: 99%

See 4 more Smart Citations

Optimal Variable Acceptance Sampling Plan under Progressive Type-II Censoring for Mixture Distribution: Applications in Breast Cancer Data

Mathai,

Kumar

2024

Preprint

View full text Add to dashboard Cite

Mixture distributions are widely utilized in various practical problems such as clinical experiments and electronic component life testing. Despite this, the literature does not extensively cover acceptance sampling plans associated with these distributions. In this paper, variable acceptance sampling plans are designed for an exponential-Rayleigh mixture distribution using partially accelerated life tests (PALT). Under progressive type-II censoring schemes with binomial removals, the maximum likelihood estimates (MLEs) of the unknown parameters of the mixture distribution are derived for Arrhenius and linear life-stress relationships. Based on these relationships, optimal variable sampling plans are formulated and plan parameters are determined by solving corresponding optimization problems. The study presents numerical findings, a comparative analysis, and sensitivity assessments. Finally, the practical applicability and relevance of the proposed acceptance sampling plans are demonstrated using real-world datasets of breast cancer patients' records and failure lifetime data from communication transmitter-receivers in a commercial aircraft.

show abstract

“…The data sets used in the manuscript are taken from open sources. Data presented in Table 4 and Table 5 are obtained from Ramyamol and Kumar (2019) and Collett (2023), respectively.…”

Section: Discussionmentioning

confidence: 99%

Section: Comparative Analysis Using a Real-life Datamentioning

confidence: 99%

Section: Comparative Analysis Using a Real-life Datamentioning

confidence: 99%

Section: Start Endmentioning

confidence: 99%

Section: Comparative Analysis Using a Real-life Datamentioning

confidence: 99%

See 3 more Smart Citations