• Asian Pacific Journal of Cancer Prevention
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• 제16권18호
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• pp.8567-8571
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• 2016

Background: The Cox model is known as one of the most frequently-used methods for analyzing survival data. However, in some situations parametric methods may provide better estimates. In this study, a Weibull parametric model was employed to assess possible prognostic factors that may affect the survival of patients with breast cancer. Materials and Methods: We studied 438 patients with breast cancer who visited and were treated at the Cancer Research Center in Shahid Beheshti University of Medical Sciences during 1992 to 2012; the patients were followed up until October 2014. Patients or family members were contacted via telephone calls to confirm whether they were still alive. Clinical, pathological, and biological variables as potential prognostic factors were entered in univariate and multivariate analyses. The log-rank test and the Weibull parametric model with a forward approach, respectively, were used for univariate and multivariate analyses. All analyses were performed using STATA version 11. A P-value lower than 0.05 was defined as significant. Results: On univariate analysis, age at diagnosis, level of education, type of surgery, lymph node status, tumor size, stage, histologic grade, estrogen receptor, progesterone receptor, and lymphovascular invasion had a statistically significant effect on survival time. On multivariate analysis, lymph node status, stage, histologic grade, and lymphovascular invasion were statistically significant. The one-year overall survival rate was 98%. Conclusions: Based on these data and using Weibull parametric model with a forward approach, we found out that patients with lymphovascular invasion were at 2.13 times greater risk of death due to breast cancer.

• Wind and Structures
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• 제23권1호
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• pp.75-90
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• 2016

A probabilistic approach that combines structural demand hazard analysis with cumulative damage assessment is presented and applied to a steel tower of a wind turbine. The study presents the step by step procedure to compare the reliability over time of the structure subjected to fatigue, assuming: a) a binomial Weibull annual wind speed, and b) a traditional Weibull probability distribution function (PDF). The probabilistic analysis involves the calculation of force time simulated histories, fatigue analysis at the steel tower base, wind hazard curves and structural fragility curves. Differences in the structural reliability over time depending on the wind speed PDF assumed are found, and recommendations about selecting a real PDF are given.

• 신재생에너지
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• 제20권2호
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• pp.2-16
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• 2024

Various loads induced by marine environmental conditions, such as waves, currents, and wind, are crucial for the operation and viability of offshore wind power (OWP) systems. In particular, waves have a significant impact on the stress and fatigue load of offshore structures, and highly reliable design parameters should be derived through extreme value analysis (EVA) techniques. In this study, extreme wave analyses were conducted with various Weibull distribution models to determine the reliable design parameters of an OWP system suitable for the Ulsan area. Forty-three years of long-term hindcast data generated by a numerical wave model were adopted as the analyses data, and the least-squares method was used to estimate the parameters of the distribution function for EVA. The inverse first-order reliability method was employed as the EVA technique. The obtained results were compared among themselves under the assumption that the marginal probability distributions were 2p, 3p, and exponentiated Weibull distributions.

• Water Engineering Research
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• 제1권1호
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• pp.11-23
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• 2000

Regional flood frequency analysis has been developed by employing the nearby site's information to improve a precision in estimating flood quantiles at the site of interest. In this paper, single site and regional flood frequency analyses were compared based of the 2-parameter Weibull model. For regional analysis, two approaches were employed. The First one is to use the asymptotic variances of the quantile estimators derived based of the assumption that all sites including the site of interest are independent each other. This approach may give the maximum regional gain due to the spatial independence assumption among sites. The second one in Hosking's regional L-moment algorithm. These methods were applied to annual flood data. As the results, both methods generally showed the regional gain at the site of interest depending on grouping the sites as homogeneous. And asymptotic formula generally shows smaller variance than those from Hosking's algorithm. If the shape parameter of the site of interest from single site analysis is quite different from that from regional analysis then Hosking's results might be better than the asymptotic ones because the formula was derived based on the assumption that all sites have the same regional shape parameter. Furthermore, in such a case, regional analysis might be worse than single site analysis in the sense of precision of flood quantile estimation. Even though the selected sites may satisfy Hosking's criteria, regional analysis may not give a regional gain for specific and nonexceedance probabilities.

• 한국세라믹학회지
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• 제34권9호
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• pp.927-934
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• 1997

Static and cyclic fatigue tests were carried out for alumina specimen to study the statistical analyses (normal, lognormal and Weibull distribution) of fatigue lifetime data and nominal initial crack length data. Fatigue lifetime data followed Weibull distribution better than normal or lognormal distribution, for the shape parameter of the notched specimen was larger than that of the unnotched specimen. The nominal initial crack length data obtained from fatigue lifetime followed the lognormal and Weibull distribution better than normal distribution, for the coefficient of variation of the unnotched specimen was larger than that of the notched specimen, and shape parameter of unnotched specimen was smaller than that of the notched specimen.

• Communications for Statistical Applications and Methods
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• 제24권3호
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• pp.271-290
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• 2017

We study a bivariate response regression model with arbitrary marginal distributions and joint distributions using Frank and Clayton's families of copulas. The proposed model is used for fitting dependent bivariate data with explanatory variables using the log-odd log-logistic Weibull distribution. We consider likelihood inferential procedures based on constrained parameters. For different parameter settings and sample sizes, various simulation studies are performed and compared to the performance of the bivariate odd-log-logistic-Weibull regression model. Sensitivity analysis methods (such as local and total influence) are investigated under three perturbation schemes. The methodology is illustrated in a study to assess changes on schoolchildren's oral health-related quality of life (OHRQoL) in a follow-up exam after three years and to evaluate the impact of caries incidence on the OHRQoL of adolescents.

• 한국물환경학회지
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• 제31권2호
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• pp.142-150
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• 2015

The relationship between the number of benthic macroinvertebrate species and the accumulated survey area were investigated in a clean stream and an impaired stream of Korea. Five models to characterize species-area functions were compared, and the Weibull model fitted species-area data well. The other models (Arrhenius, Romell-Gleason, Kylin, Lognormal model) had small or notable bias. The maximum number of species and half-saturation area derived from the Weibull model may be used as the indicators of the carrying capacity and the habitat complexity respectively.

• 산업경영시스템학회지
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• 제30권1호
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• pp.66-73
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• 2007

일반 투자안의 의사결정에서와 마찬가지로 산업설비의 경제성 분석에서도 가장 중요한 결정 요소 중의 하나가 설비의 생존곡선 추정이다. 설비의 자산 가치가 감소하는 원인은 여러 가지가 있으나, 여러 원인 중 물리적 훼손이 과거의 산업설비에서는 가장 중요한 원인이었으므로 기존의 생존모형 분석에서는 lows 생존곡선을 이용하여 설비의 생존곡선을 추정하였다. 그러나 새로운 기술상의 변화로 인한 첨단 생산시스템의 설비교체 분석 시에는 적합지 않다. 따라서, 본 연구에서 제안된 혼합형 Weibull 함수를 이용하여 설비의 폐기 형태를 추정함으로써 설비들의 실제적인 생존곡선을 정확하게 파악할 수 있다.

• 대한산업공학회지
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• 제42권4호
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• pp.256-262
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• 2016

The Weibull is widely used in reliability analysis, and several studies have attempted to improve estimation of the distribution's parameters. least squares estimation (LSE) or Maximum likelihood estimation (MLE) are often used to estimate distribution parameters. However, it has been proven that Bayesian methods are more suitable for small sample sizes than LSE and MLE. In this work, the Weibull parameter estimation accuracy of LSE, MLE, and Bayesian method are compared for sample sets with 3 to 30 data points. The Bayesian method was most accurate for sample sizes under 25, and the accuracy of the Bayesian method was similar to LSE and MLE as the sample size increased.

• 한국해안해양공학회:학술대회논문집
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• 한국해안해양공학회 2006년도 추계학술대회 발표논문집 Proceedings of Coastal and Ocean Engineering in Korea
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• pp.65-68
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• 2006