• The Korean Journal of Applied Statistics
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• v.25 no.3
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• pp.435-446
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• 2012

Epidemiologic studies frequently try to estimate the impact of a specific risk factor. The risk difference and the risk ratio are generally useful measurements for this purpose. When using such measurements for rare events, the standard approaches based on the normal approximation may fail, in particular when no events are observed. In this paper, we discuss and evaluate several existing methods to construct confidence intervals around risk differences and risk ratios using Monte-Carlo simulations when the disease of interest is rare. The results in this paper provide guidance how to construct interval estimates of the risk differences and the risk ratios when no events are detected.

• The Korean Journal of Applied Statistics
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• v.18 no.3
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• pp.607-615
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• 2005

Recently the interval estimation of a binomial proportion is revisited in various literatures. This is mainly due to the erratic behavior of the coverage probability of the will-known Wald confidence interval. Various alternatives have been proposed. Among them, Agresti-Coull confidence interval has been recommended by Brown et al. (2001) with other confidence intervals for large sample, say n $\ge$ 40. On the other hand, a noninformative Bayesian approach called Polya posterior often produces statistics with good frequentist's properties. In this note, an interval estimator is developed using weighted Polya posterior. The resulting interval estimator is essentially the Agresti-Coull confidence interval with some improved features. It is shown that the weighted Polys posterior produce an effective interval estimator for small sample size and a severely skewed binomial distribution.

• Tuberculosis and Respiratory Diseases
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• v.67 no.2
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• pp.105-112
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• 2009

Background: Solitary pulmonary nodules (SPN) are encountered incidentally in 0.2% of patients who undergo chest X-ray or chest CT. Although SPN has malignant potential, it cannot be treated surgically by biopsy in all patients. The first stage is to determine if patients with SPN require periodic observation and biopsy or resection. An important early step in the management of patients with SPN is to estimate the clinical pretest probability of a malignancy. In every patient with SPN, it is recommended that clinicians estimate the pretest probability of a malignancy either qualitatively using clinical judgment or quantitatively using a validated model. This study examined whether Bayesian analysis or multiple logistic regression analysis is more predictive of the probability of a malignancy in SPN. Methods: From January 2005 to December 2008, this study enrolled 63 participants with SPN at the Kangnam Sacred Hospital. The accuracy of Bayesian analysis and Bayesian analysis with a FDG-PET scan, and Multiple logistic regression analysis was compared retrospectively. The accurate probability of a malignancy in a patient was compared by taking the chest CT and pathology of SPN patients with <30 mm at CXR incidentally. Results: From those participated in study, 27 people (42.9%) were classified as having a malignancy, and 36 people were benign. The result of the malignant estimation by Bayesian analysis was 0.779 (95% confidence interval [CI], 0.657 to 0.874). Using Multiple logistic regression analysis, the result was 0.684 (95% CI, 0.555 to 0.796). This suggests that Bayesian analysis provides a more accurate examination than multiple logistic regression analysis. Conclusion: Bayesian analysis is better than multiple logistic regression analysis in predicting the probability of a malignancy in solitary pulmonary nodules but the difference was not statistically significant.

• Journal of the Korean Data and Information Science Society
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• v.24 no.4
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• pp.857-865
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• 2013

Extreme rainfall causes heavy losses in human life and properties. Hence many works have been done to predict extreme rainfall by using extreme value distributions. In this study, we use a generalized extreme value distribution to derive the posterior predictive density with hierarchical Bayesian approach based on the data of Seoul area from 1973 to 2010. It becomes clear that the probability of the extreme rainfall is increasing for last 20 years in Seoul area and the model proposed works relatively well for both point prediction and predictive interval approach.

• Journal of the Korean Statistical Society
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• v.29 no.1
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• pp.17-27
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• 2000

In this paper, we develop noninformative priors that are used for estimating the reliability of stress-strength system under the Burr-type X model. A class of priors is found by matching the coverage probabilities of one-sided Bayesian credible interval with the corresponding frequentist coverage probabilities. It turns out that the reference prior as well as the Jeffreys prior are the second order matching prior. The propriety of posterior under the noninformative priors is proved. The frequentist coverage probabilities are investigated for samll samples via simulation study.

• Communications for Statistical Applications and Methods
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• v.30 no.2
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• pp.191-213
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• 2023

Unit distributions are frequently used in probability theory and statistics to depict meaningful variables having values between zero and one. Using convenient transformation, the unit inverse exponentiated weibull (UIEW) distribution, which is equally useful for modelling data on the unit interval, is proposed in this study. Quantile function, moments, incomplete moments, uncertainty measures, stochastic ordering, and stress-strength reliability are among the statistical properties provided for this distribution. To estimate the parameters associated to the recommended distribution, well-known estimation techniques including maximum likelihood, maximum product of spacings, least squares, weighted least squares, Cramer von Mises, Anderson-Darling, and Bayesian are utilised. Using simulated data, we compare how well the various estimators perform. According to the simulated outputs, the maximum product of spacing estimates has lower values of accuracy measures than alternative estimates in majority of situations. For two real datasets, the proposed model outperforms the beta, Kumaraswamy, unit Gompartz, unit Lomax and complementary unit weibull distributions based on various comparative indicators.

• Structural Engineering and Mechanics
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• v.37 no.4
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• pp.427-442
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• 2011

In fatigue life design of mechanical components, uncertainties arising from materials and manufacturing processes should be taken into account for ensuring reliability. A common practice is to apply a safety factor in conjunction with a physics model for evaluating the lifecycle, which most likely relies on the designer's experience. Due to conservative design, predictions are often in disagreement with field observations, which makes it difficult to schedule maintenance. In this paper, the Bayesian technique, which incorporates the field failure data into prior knowledge, is used to obtain a more dependable prediction of fatigue life. The effects of prior knowledge, noise in data, and bias in measurements on the distribution of fatigue life are discussed in detail. By assuming a distribution type of fatigue life, its parameters are identified first, followed by estimating the distribution of fatigue life, which represents the degree of belief of the fatigue life conditional to the observed data. As more data are provided, the values will be updated to reduce the credible interval. The results can be used in various needs such as a risk analysis, reliability based design optimization, maintenance scheduling, or validation of reliability analysis codes. In order to obtain the posterior distribution, the Markov Chain Monte Carlo technique is employed, which is a modern statistical computational method which effectively draws the samples of the given distribution. Field data of turbine components are exploited to illustrate our approach, which counts as a regular inspection of the number of failed blades in a turbine disk.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.2
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• pp.867-872
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• 2016

Background: Esophageal cancer is one of the most serious malignancies. Due to the aggressive nature of this cancer, the prognosis is poor. A network meta-analysis with simultaneous comparison of multiple treatments can help determine better treatment options that have higher effects on overall survival of patients with lower adverse events. The aim of this review is to simultaneously compare efficacy and adverse events of treatment interventions for esophageal cancer. Materials and Methods: In this review, only randomized control trials (RCT) will be considered for network meta-analysis. All international electronic databases including Medline, Web of Sciences, Scopus, Cochran's library, EMBASE and Cancerlit will be searched to find randomized control trials which compared two or more treatment interventions for esophageal cancer. A network plot will be drawn for visual representation of all available treatment interventions. Bayesian approach will be used to combine the direct and indirect evidence. Treatment effects (e.g. hazard ratio for time to event outcomes, risk ratio for binary outcomes, and rate ratio for count outcomes with 95% credible interval) will be reported. Moreover, cumulative probability of the treatment ranks will be reported using the surface under the cumulative ranking (SUCRA) graphs. Consistency assumption will be assessed by the loop-specific and design-by-treatment interaction approaches. Conclusions: The results of this study may be helpful for the patients, clinicians and health policy makers in selecting treatments that have the best effect on survival and lowest adverse events.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.38 no.7
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• pp.647-656
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• 2014

To supplement the ISO-GUM method for the evaluation of measurement uncertainty, a simulation program using the Monte Carlo method (MCM) was developed, and the MCM and GUM methods were compared. The results are as follows: (1) Even under a non-normal probability distribution of the measurand, MCM provides an accurate coverage interval; (2) Even if a probability distribution that emerged from combining a few non-normal distributions looks as normal, there are cases in which the actual distribution is not normal and the non-normality can be determined by the probability distribution of the combined variance; and (3) If type-A standard uncertainties are involved in the evaluation of measurement uncertainty, GUM generally offers an under-valued coverage interval. However, this problem can be solved by the Bayesian evaluation of type-A standard uncertainty. In this case, the effective degree of freedom for the combined variance is not required in the evaluation of expanded uncertainty, and the appropriate coverage factor for 95% level of confidence was determined to be 1.96.

• Journal of Preventive Medicine and Public Health
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• v.55 no.2
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• pp.164-172
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• 2022

Objectives: We aimed to estimate the space-time distribution of the risk of suicide mortality in Iran from 2006 to 2016. Methods: In this repeated cross-sectional study, the age-standardized risk of suicide mortality from 2006 to 2016 was determined. To estimate the cumulative and temporal risk, the Besag, York, and Mollié and Bernardinelli models were used. Results: The relative risk of suicide mortality was greater than 1 in 43.0% of Iran's provinces (posterior probability >0.8; range, 0.46 to 3.93). The spatio-temporal model indicated a high risk of suicide in 36.7% of Iran's provinces. In addition, significant upward temporal trends in suicide risk were observed in the provinces of Tehran, Fars, Kermanshah, and Gilan. A significantly decreasing pattern of risk was observed for men (β, -0.013; 95% credible interval [CrI], -0.010 to -0.007), and a stable pattern of risk was observed for women (β, -0.001; 95% CrI, -0.010 to 0.007). A decreasing pattern of suicide risk was observed for those aged 15-29 years (β, -0.006; 95% CrI, -0.010 to -0.0001) and 30-49 years (β, -0.001; 95% CrI, -0.018 to -0.002). The risk was stable for those aged >50 years. Conclusions: The highest risk of suicide mortality was observed in Iran's northwestern provinces and among Kurdish women. Although a low risk of suicide mortality was observed in the provinces of Tehran, Fars, and Gilan, the risk in these provinces is increasing rapidly compared to other regions.