• Communications for Statistical Applications and Methods
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• v.29 no.4
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• pp.413-420
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• 2022

In this study we adapt discrete weibull regression model for clustered count data. Discrete weibull regression model has an attractive feature that it can handle both under and over dispersion data. We analyzed the eighth Korean National Health and Nutrition Examination Survey (KNHANES VIII) from 2019 to assess the factors influencing the 1 month outpatient stay in 17 different regions. We compared the results using clustered discrete Weibull regression model with those of Poisson, negative binomial, generalized Poisson and Conway-maxwell Poisson regression models, which are widely used in count data analyses. The results show that the clustered discrete Weibull regression model using random intercept model gives the best fit. Simulation study is also held to investigate the performance of the clustered discrete weibull model under various dispersion setting and zero inflated probabilities. In this paper it is shown that using a random effect with discrete Weibull regression can flexibly model count data with various dispersion without the risk of making wrong assumptions about the data dispersion.

• Communications for Statistical Applications and Methods
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• v.26 no.3
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• pp.325-336
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• 2019

In this article we extend the discrete Weibull regression model in the presence of missing data. Discrete Weibull regression models can be adapted to various type of dispersion data however, it is not widely used. Recently Yoo (Journal of the Korean Data and Information Science Society, 30, 11-22, 2019) adapted the discrete Weibull regression model using single imputation. We extend their studies by using multiple imputation also with several various settings and compare the results. The purpose of this study is to address the merit of using multiple imputation in the presence of missing data in discrete count data. We analyzed the seventh Korean National Health and Nutrition Examination Survey (KNHANES VII), from 2016 to assess the factors influencing the variable, 1 month hospital stay, and we compared the results using discrete Weibull regression model with those of Poisson, negative Binomial and zero-inflated Poisson regression models, which are widely used in count data analyses. The results showed that the discrete Weibull regression model using multiple imputation provided the best fit. We also performed simulation studies to show the accuracy of the discrete Weibull regression using multiple imputation given both under- and over-dispersed distribution, as well as varying missing rates and sample size. Sensitivity analysis showed the influence of mis-specification and the robustness of the discrete Weibull model. Using imputation with discrete Weibull regression to analyze discrete data will increase explanatory power and is widely applicable to various types of dispersion data with a unified model.

• Nuclear Engineering and Technology
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• v.55 no.5
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• pp.1924-1934
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• 2023

In this study, the probabilistic fatigue life model for Ni-base alloys was developed based on the Weibull distribution using statistical analysis of fatigue data reported in NUREG/CR-6909 and the new fatigue data of Alloy 52M/152 and 82/182. The developed Weibull model can consider right-censored data (i.e., non-failed data) and quantify the improved safety (or reliability) based on the level of failure probability. The overall margin in the current fatigue design limit model (ASME design curve + NUREG/CR-6909 F_en model) is similar to that of the Weibull model with a cumulative failure probability of approximately 2.5%. The margin in the current fatigue design limit model demonstrated inconsistencies for the Ni-base alloy weld data, whereas the Weibull model showed a consistent margin. Therefore, the Weibull model can systematically mitigate the excessive safety margin.

• Journal of Ocean Engineering and Technology
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• v.10 no.2
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• pp.61-68
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• 1996

Weibull distribution function is applied very successfully to the strength of brittle materials such as ceramics and the weakest link model is applied to explain the ovents. This paper deals with the effect of specimen size on the strength of ceramics. The values of tensile strength were calculated by the Monte-Calro simuation. The tensile strength obtained was plotted on Weibull probabillity papers and represented by the 3-parameter Weibull distribution. The strength distribution function was compared with the theoretical weibull distribution. As a result, it was found that the Weibull shape parameter was changed due to the size and there was a possibility of a false indication as if the weakest link model holds good. We should be very careful when we apply the Weibull statistics to estimate the strength of products.

• Advanced Composite Materials
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• v.16 no.3
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• pp.245-258
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• 2007

The single-modal Weibull model has been assessed on Tyranno ZMI Si-Zr-C-O fiber if a set of shape and scale parameters accurately reproduced the effect of the size of the diameter on strength. The tensile data of a single fiber have been divided into two expedient groups as 'small diameter' group and 'large diameter' group in deriving the parameters, which should be consistent if the Weibull model accurately reproduced the size effect. However, the derived Weibull parameters were inconsistent between the two groups. Thereby the authors have concluded that the parameters of the single-modal Weibull model are dependent on the fiber diameter, so that the model is inadequate to reproduce the strength size effect. On the other hand, Weibull parameters were found consistent between the two groups by excluding the data of 'large mirror zone' sample, which was defined as the sample around 10% mirror zone area of the fracture surface. What is more, the exclusion reduced the strength variance more drastically in the 'large diameter' group than in the 'small diameter' group, even though the 'large mirror zone' samples were found identical in the percentage between the two groups. The authors therefore conclude that diameter limitation to the 'small diameter' group level can lead to drastically less distributed strength values than the estimated strength through the Weibull scaling on the present Tyranno ZMI Si-Zr-C-O fiber.

• Proceedings of the Korean Statistical Society Conference
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• 2001.11a
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• pp.143-148
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• 2001

A dataset having missing observations is often completed by using imputed values. In this paper the performances and accuracy of complete case methods and four imputation procedures are evaluated when missing values exist only on the response variables in the Weibull regression model. Our simulation results show that compared to other imputation procedures, in particular, hotdeck and Weibull regression imputation procedure can be well used to compensate for missing data. In addition an illustrative real data is given.

• Communications for Statistical Applications and Methods
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• v.7 no.1
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• pp.23-36
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• 2000

This paper discusses the local influence approach to the Weibull regression model with censored data. Diagnostics for the Weibull regression model are proposed and developed when simultaneous perturbations of the response vector are allowed.

• Journal of Korean Society of Transportation
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• v.5 no.2
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• pp.73-80
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• 1987

Various procedures for evaluation of traffic noise annoyance have been proposed. However, most of the studies of this type are restricted for improving traffic flow. In this paper, a method to predict the road traffic noise is proposed in terms of equivalent continuous A-Weighted sound pressure level (Leq), based on a probability model. First, distribution of the road traffic noise level are investigated. second, the weibull distribution parameters are estimated by using the quantification theory. Finally, a prediction model of the road traffic noise is proposed based on the weibull distribution model The predicted values of the Leq are closely matched the measured data.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.55 no.3
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• pp.109-115
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• 2006

The Weibull distribution is a good candidate for accurate probabilistic model with its rich shape-forming ability and relatively simple CDF(cumulative distribution function). If there are sufficient information to get convincible mean and variance for a probabilistic event, reliable parameters of the Weibull distribution can be determined uniquely. However, sufficient information is not given as usual. There needs more deliberate model building method for that case. This Paper presents an effective parameter estimation technique for Weibull distribution with limited failure data.

• Food Science and Preservation
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• v.18 no.3
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• pp.423-428
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• 2011

Inactivation kinetic data of Escherichia coli O157:H7, Listeria monocytogenes, Staphylococcus aureus, Salmonella Typhimurium, and Salmonella Enteritidis via treatment with aqueous chlorine dioxide treatment at a specific concentration were evaluated using the first-order kinetic and Weibull models. The Weibull model showed a better fit with the kinetic data than the first-order kinetic model. The survival curves after the aqueous chlorine dioxide treatment showed $t_R$ values(time required to reduce microbial populations by 90%) of 2.49 min for E. coli O157:H7 at 5 ppm, 1.47 min for L. monocytogenes at 5 ppm, 0.94 min for S. aureus at 5 ppm, 0.87 min for S. Typhimurium at 1 ppm, and 0.08 min for S. Enteritidis at 1 ppm, according to the Weibull model.