UDOM IR :: Browsing by Author "Rao, G. S."

Browsing by Author "Rao, G. S."

Now showing 1 - 10 of 10

A New Modification of the Weibull Distribution: Model, Theory, and Analyzing Engineering Data Sets
(MDPI AG, 2024) Alshanbari, H. M.; Ahmad, Z.; El-Bagoury, A. H.; Odhah, O. H.; Rao, G. S.
Symmetrical as well as asymmetrical statistical models play a prominent role in describing and predicting the real-world phenomena of nature. Among other fields, these models are very useful for modeling data in the sector of civil engineering. Due to the applicability of the statistical models in civil engineering and other related sectors, this paper offers a statistical methodology to improve the distributional flexibility of traditional models. The suggested method/approach is called the extended-X family of distributions. The proposed method has the ability to generate symmetrical and asymmetrical probability distributions. Based on the extended-X family approach, an updated version of the Weibull model, namely, the extended Weibull model, is studied. The proposed model is very flexible and has the ability to capture the symmetrical and asymmetrical shapes of its density function. For the extended-X method, the estimation of parameters, a simulation study, and some mathematical properties are derived. Finally, the practical illustration/usefulness of the suggested model is shown by analyzing two data sets taken from the field of engineering. Both data sets represent the fracture toughness of alumina (Al2O3).
Estimation of stress–strength reliability from exponentiated inverse rayleigh distribution
(World Scientific Publishing Company, 2019) Rao, G. S.; Mbwambo, S.; Josephat, P. K.
This paper considers the estimation of stress–strength reliability when two independent exponential inverse Rayleigh distributions with different shape parameters and common scale parameter. The maximum likelihood estimator (MLE) of the reliability, its asymptotic distribution and asymptotic confidence intervals are constructed. Comparisons of the performance of the estimators are carried out using Monte Carlo simulations, the mean squared error (MSE), bias, average length and coverage probabilities. Finally, a demonstration is delivered on how the proposed reliability model may be applied in data analysis of the strength data for single carbon fibers test data.
Group acceptance sampling plans for resubmitted lots under exponentiated Fréchet distribution
(Inderscience, 2019) Rao, G. S.; Rosaiah, K.; Babu, M. S.
In quality control, we used to develop different types of sampling plans to ensure the quality of product lifetime. In this paper, we develop a group acceptance sampling plan (GASP) for lot resubmitting, to ensure the quality of product lifetime assuming that the product lifetime follows the exponentiated Fréchet distribution. The GASP parameters are determined by satisfying the specified producer's and consumer's risks according to the experiment termination time and the number of testers. We compare the proposed plan with the ordinary group sampling plan and found that the proposed plan requires less sample size. Two examples are used for illustration.
Inspection plan for COVID-19 patients for Weibull distribution using repetitive sampling under indeterminacy
(Springer, 2021) Rao, G. S.; Aslam, M.
Background This research work is elaborated investigation of COVID-19 data for Weibull distribution under indeterminacy using time truncated repetitive sampling plan. The proposed design parameters like sample size, acceptance sample number and rejection sample number are obtained for known indeterminacy parameter. Methods The plan parameters and corresponding tables are developed for specified indeterminacy parametric values. The conclusion from the outcome of the proposed design is that when indeterminacy values increase the average sample number (ASN) reduces. Results The proposed repetitive sampling plan methodology application is given using COVID-19 data belong to Italy. The efficiency of the proposed sampling plan is compared with the existing sampling plans. Conclusions Using the tables and COVID-19 data illustration, it is concluded that the proposed plan required a smaller sample size as examined with the available sampling plans in the literature.
Life truncated multiple dependent state plan for imprecise Weibull distributed data
(Springer Science and Business Media LLC, 2024) Rao, G. S.; Aslam, M.; Josephat, P. K.; Al-Husseini, Z.; Albassam, M.
This paper aims to provide a multiple dependent state (MDS) sampling technique for light-emitting diode luminous intensities under indeterminacy by employing time truncated sampling schemes and the Weibull distribution. This indicates that ASN is significantly impacted by the indeterminacy parameter. Furthermore, a comparison is shown between the existing, indeterminate sampling plans and the recommended sample designs. The projected sampling technique is illustrated by calculating the luminous intensities of LEDs using the Weibull distribution. Based on the findings and practical example, we conclude that the suggested strategy needs a smaller sample size than SSP and the current MDS sampling plan.
Neutrosophic log-logistic distribution model in complex alloy metal melting point applications
(Springer, 2023) Rao, G. S.
The log-logistic distribution is more comprehensively applied in the area of survival and reliability engineering analysis for modelling the lifetime data practices of both human and electronic designs. The goal of this paper is to develop a generalization of the classical pattern log-logistic distribution, known as the neutrosophic log-logistic distribution (NLLD), to model various survival and reliability engineering data with indeterminacies. The developed distribution is especially useful for modeling indeterminate data that is roughly positively skewed. This paper discusses the developed NLLD’s main statistical properties such as neutrosophic survival function, neutrosophic hazard rate, neutrosophic moments, and neutrosophic mean time failure. Furthermore, the neutrosophic parameters are estimated using the well-known maximum likelihood (ML) estimation method in a neutrosophic environment. A simulation study is carried out to establish the achievement of the estimated neutrosophic parameters. As a final point, the proposed NLLD applications in the real world have been discussed with the help of real data. The real data illustrated that the efficiency of the proposed model as compared with the existing models.
On predictive modeling for the Al2O3 data using a new statistical model and machine learning approach
(Hindawi Publishing Corporation, 2022) El-Morshedy, M.; Almaspoor, Z.; Rao, G. S.; Ilyas, M.; Al-Bossly, A.
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.
Pareto Distribution-Based Shewhart Control Chart for Early Detection of Process Mean Shifts
(Springer Science and Business Media LLC, 2024) Saghir, A.; Rao, G. S.; Aslam, M.; Janjua, A. A.
The Pareto distribution is of paramount importance in actuarial science, wealth distribution, finance, etc. This paper introduces a control chart inspired by Shewhart's methodology, designed for monitoring shifts in the Pareto distribution through a repetitive sampling approach. The chart employs a modified statistic that combines shape and threshold parameters as its plotting statistic. Coefficients for the Shewhart-type Pareto chart are computed for two-phase limits. The performance of the suggested chart is assessed in terms of run length characteristics, assuming a shift in the process mean. Additionally, we conduct an efficiency comparison with existing control charts. The findings suggest that, on average, the proposed Pareto chart demonstrates greater efficiency in promptly detecting changes compared to alternative methods. To illustrate the practical application of our approach, we present an example using revenue data.
Testing average traffic fatality using sampling plan for exponentiated half logistic distribution under indeterminacy
(Elsevier, 2023) Rao, G. S.; Kirigiti, P. J.
A time-truncated sampling plan for the exponentiated half logistic distribution under the indeterminacy is developed in the present investigation. The proposed design parameters are ascertained by fixing the indeterminacy parameter with the known shape parameter. For different values of indeterminacy parameters at known shape values, the parameters of the proposed plan are determined. The results show that the indeterminacy parameter influences the sample size of the proposed design for exponentiated half-logistic distribu- tion. The results clearly indicate that while indeterminacy parameters increase, the sample size reduces. The relevance of the designed plan is established using data set on traffic fa- tality on roads in the United States. From the traffic fatality illustration, it is resolved that the proposed plan is helpful to test the aggregate traffic fatality at more modest values of sample size as analogized to the available classical sampling plan.
Uncertainty-based sampling plans for various statistical distributions
(AIMS Press, 2023) Khan, N. S.; Rao, G. S.; Sherwani, R. A. K.; Hussein, A.
This research work appertains to the acceptance sampling plan under the neutrosophic statistical interval method (ASP-NSIM) based on gamma distribution (GD), Burr type XII distribution (BXIID) and the Birnbaum-Saunders distribution (BSD). The plan parameters will be determined using the neutrosophic non-linear optimization problem. We will provide numerous tables for the three distributions using various values of shape parameters and degree of indeterminacy. The efficiency of the proposed ASP-NSIM will be discussed over the existing sampling plan in terms of sample size. The application of the proposed ASP-NSIM will be given with the aid of industrial data.

Browsing by Author "Rao, G. S."

Results Per Page

Sort Options