A New Acceptance Sampling Plans Based on Percentiles for Odds Exponential Log Logistic Distribution

Rao, G. Srinivasa; Rosaiah, K.; Kalyani, K.; Sivakumar, D. C. U.

doi:10.2174/1876527001607010045

Cited by 5 publications

(4 citation statements)

References 14 publications

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“…Balamurali et al [25] developed acceptance sampling plans based on median life for exponentiated half logistic distribution. Rao et al [26] developed a new acceptance sampling plans based on percentiles for odds exponential log logistic distribution.…”

Section: A New Acceptance Sampling Plansmentioning

confidence: 99%

On Predictive Modeling for the Al₂O₃ Data Using a New Statistical Model and Machine Learning Approach

El-Morshedy

Almaspoor

Rao

et al. 2022

Advances in Civil Engineering

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In this article, we focused on predictive modeling for real data by means of a new statistical model and applying different machine learning algorithms. The importance of statistical methods in various research fields is modeling the real data and predicting the future behavior of data. For modeling and predicting real-life data, a series of statistical models have been introduced and successfully implemented. This study introduces another novel method, namely, a new generalized exponential-X family for generating new distributions. This method is introduced by using the T-X approach with the exponential model. A special case of the new method, namely, a new generalized exponential Weibull model, is introduced. The applicability of the new method is illustrated by means of a real application related to the alumina (Al2O3) data set. Acceptance sampling plans are developed for this distribution using percentiles when the life test is truncated at the pre-assigned time. The minimum sample size needed to make sure that the required lifetime percentile is determined for a specified customer’s risk and producer’s risk simultaneously. The operating characteristic value of the sampling plans is also provided. The plan methodology is illustrated using Al2O3 fracture toughness data. Using the same data set, we implement various machine learning approaches including the support vector machine (SVR), group method of data handling (GMDH), and random forest (RF). To evaluate their forecasting performances, three statistical measures of accuracy, namely, root-mean-square error (RMSE), mean absolute error (MAE), and Akaike information criterion (AIC) are computed.

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Section: A New Acceptance Sampling Plansmentioning

confidence: 99%

On Predictive Modeling for the Al₂O₃ Data Using a New Statistical Model and Machine Learning Approach

El-Morshedy

Almaspoor

Rao

et al. 2022

Advances in Civil Engineering

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“…In Table 6, we compare the plan parameters of the proposed sampling plan with the odds exponential log -logistic distribution (OELLD) which was studied by [24] when = 0.05 and for various levels of = 1.0, 1.5, 2.0, 2.5. The sample size for the OGELLD is smaller as compared to OELLD for 50 th percentiles.…”

Section: Comparison Of Distributionsmentioning

confidence: 99%

“…New acceptance sampling plans based on percentiles for exponentiated Fréchet distribution was constructed by [23]. An ASPs based on percentiles for Odds exponential log logistic distribution (OELLD) was discussed by [24]. New ASPs based on life tests for Birnbaum-Saunders distributions was considered by [25].…”

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confidence: 99%

Odd generalized exponential log logistic distribution: A new acceptance sampling plans based on percentiles

Rao¹,

Rosaiah²,

Sivakumar³

et al. 2019

IJAAS

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<span>In this paper, acceptance sampling plans are developed for the odd generalized exponential log logistic distribution based on percentiles when the life test is truncated at a pre-specified (pre-determined) time. The minimum sample size necessary to ensure the specified life percentile is obtained under a given consumer’s risk. The operating characteristic values of the sampling plans as well as the producer’s risk are presented. One example with real data set is also given as an illustration.</span>

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“…Because of the simplicity of usage of the plan, much exertion has been made in the course of recent decades to explore the acceptance of single sampling plans for the testing and review of items for various sampling circumstances. A number of authors have developed singlesampling plans for different distributions: Epstein [1]; Goode and Kao [2]; Gupta and Groll [3]; Gupta [4]; Kantam et al [5]; Baklizi [6]; Baklizi and Masri [7]; Tsai and Wu [8]; Balakrishnan et al [9]; Rosaiah and Kantam [10]; Rosaiah et al [11]; Rao et al [12,13]; and Rosaiah et al [14]. e above-listed plan has also recently been designed for various lifetime distributions.…”

Section: Introductionmentioning

confidence: 99%

Double-Acceptance Sampling Plan for Exponentiated Fréchet Distribution with Known Shape Parameters

Babu

Rao

Rosaiah

2021

Mathematical Problems in Engineering

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We suppose that a product’s lifetime follow the exponentiated Fréchet distribution of defined shape parameters. Based on this assumption, a double-acceptance sampling plan is constructed. The zero and one failure framework is essentially thought of: if no errors are found from the first sample, then the lot is approved; also, if at least two failures occur, it is rejected. In the first sample, if one failure is observed, then the second sample is taken and decided for the same length as the first one. The cumulative sample sizes of the first and second samples are determined on the basis of the stated confidence level of the consumer to ensure that the actual median is longer than the given life. As indicated by the various ratios of the actual median life to specified median lifetime, the operating characteristics are calculated and placed in presented tables. To decrease the risk of the producer at the predefined level, the minimum ratios of this sort are additionally obtained. Lastly, examples are provided for representation reasons for the proposed model.

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A New Acceptance Sampling Plans Based on Percentiles for Odds Exponential Log Logistic Distribution

Cited by 5 publications

References 14 publications

On Predictive Modeling for the Al₂O₃ Data Using a New Statistical Model and Machine Learning Approach

On Predictive Modeling for the Al₂O₃ Data Using a New Statistical Model and Machine Learning Approach

Odd generalized exponential log logistic distribution: A new acceptance sampling plans based on percentiles

Double-Acceptance Sampling Plan for Exponentiated Fréchet Distribution with Known Shape Parameters

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A New Acceptance Sampling Plans Based on Percentiles for Odds Exponential Log Logistic Distribution

Cited by 5 publications

References 14 publications

On Predictive Modeling for the Al2O3 Data Using a New Statistical Model and Machine Learning Approach

On Predictive Modeling for the Al2O3 Data Using a New Statistical Model and Machine Learning Approach

Odd generalized exponential log logistic distribution: A new acceptance sampling plans based on percentiles

Double-Acceptance Sampling Plan for Exponentiated Fréchet Distribution with Known Shape Parameters

Contact Info

Product

Resources

About

On Predictive Modeling for the Al₂O₃ Data Using a New Statistical Model and Machine Learning Approach

On Predictive Modeling for the Al₂O₃ Data Using a New Statistical Model and Machine Learning Approach