• Journal of the Korean Data and Information Science Society
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• 제24권6호
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• pp.1543-1550
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• 2013

In this paper, we study Bayesian estimation for the finite population proportion in binary data under selection bias. We use a Bayesian nonignorable selection model to accommodate the selection mechanism. We compare four possible estimators of the finite population proportions based on data analysis as well as Monte Carlo simulation. It turns out that nonignorable selection model might be useful for weekly biased samples.

• Communications for Statistical Applications and Methods
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• 제4권3호
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• pp.795-805
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• 1997

In this paper, we consider a Bayesian forecasting method for the analysis of repeated surveys. It is assumed that the parameters of the superpopulation model at each time follow a stochastic model. We propose Bayesian prediction procedures for the finite population total under dynamic generalized linear models. Some numerical studies are provided to illustrate the behavior of the proposed predictors.

• 한국신뢰성학회:학술대회논문집
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• 한국신뢰성학회 2005년도 학술발표대회 논문집
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• pp.259-264
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• 2005

In this paper, we generalize the software reliability growth model by assuming that the testing cost and maintenance cost are random and adopts the Bayesian approach to determine the optimal software release time. Numerical examples are provided to illustrate the Bayesian method for certain parametric models.

• Asian Pacific Journal of Cancer Prevention
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• 제15권6호
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• pp.2893-2900
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• 2014

Background: Statistical methods are very important to precisely measure breast cancer patient survival times for healthcare management. Previous studies considered basic statistics to measure survival times without incorporating statistical modeling strategies. The objective of this study was to develop a data-based statistical probability model from the female breast cancer patients' survival times by using the Bayesian approach to predict future inferences of survival times. Materials and Methods: A random sample of 500 female patients was selected from the Surveillance Epidemiology and End Results cancer registry database. For goodness of fit, the standard model building criteria were used. The Bayesian approach is used to obtain the predictive survival times from the data-based Exponentiated Exponential Model. Markov Chain Monte Carlo method was used to obtain the summary results for predictive inference. Results: The highest number of female breast cancer patients was found in California and the lowest in New Mexico. The majority of them were married. The mean (SD) age at diagnosis (in years) was 60.92 (14.92). The mean (SD) survival time (in months) for female patients was 90.33 (83.10). The Exponentiated Exponential Model found better fits for the female survival times compared to the Exponentiated Weibull Model. The Bayesian method is used to obtain predictive inference for future survival times. Conclusions: The findings with the proposed modeling strategy will assist healthcare researchers and providers to precisely predict future survival estimates as the recent growing challenges of analyzing healthcare data have created new demand for model-based survival estimates. The application of Bayesian will produce precise estimates of future survival times.

• 한국수산과학회지
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• 제41권2호
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• pp.107-112
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• 2008

To evaluate uncertainty and risk in biological reference points, we applied a bootstrapping method and a Bayesian procedure to estimate the related confidence intervals. Here we provide an example of the maximum sustainable yield (MSY) of turban shell, Batillus cornutus, estimated by the Schaefer and Fox models. Fitting the time series of catch and effort from 1968 to 2006 showed that the Fox model performs better than the Schaefer model. The estimated MSY and its bootstrap percentile confidence interval (CI) at ${\alpha}=0.05$ were 1,680 (1,420-1,950) tons for the Fox model and 2,170 (1,860-2,500) tons for the Schaefer model. The CIs estimated by the Bayesian approach gave similar ranges: 1,710 (1,450-2,000) tons for the Fox model and 2,230 (1,760-2,930) tons for the Schaefer model. Because uncertainty in effort and catch data is believed to be greater for earlier years, we evaluated the influence of sequentially excluding old data points by varying the first year of the time series from 1968 to 1992 to run 'backward' bootstrap resampling. The results showed that the means and upper 2.5% confidence limit (CL) of MSY varied greatly depending on the first year chosen whereas the lower 2.5% CL was robust against the arbitrary selection of data, especially for the Schaefer model. We demonstrated that the bootstrap and Bayesian approach could be useful in precautionary fisheries management, and we advise that the lower 2.5% CL derived by the Fox model is robust and a better biological reference point for the turban shells of Jeju Island.

• Journal of the Korean Data and Information Science Society
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• 제6권2호
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• pp.29-39
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• 1995

A model used in the past may be altered at will in modeling for the future. For this situation, the multiprocess dynamic model provides a general framework. In this paper we consider the multiprocess discount normal model with parameters having a time dependent non-linear structure. This model has nice properties such as insensitivity to outliers and quick reaction to abrupt changes of pattern.

• Communications for Statistical Applications and Methods
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• 제13권1호
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• pp.141-150
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• 2006

Penalized likelihood regression for exponential families have been considered by Kim (2005) through smoothing parameter selection and asymptotically efficient low dimensional approximations. We derive approximate Bayesian confidence intervals based on Bayes model associated with lower dimensional approximations to provide interval estimates in penalized likelihood regression and conduct empirical studies to access their properties.

• 대한산업공학회지
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• 제32권2호
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• pp.141-152
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• 2006

This paper presents the development of a decision model to examine the optimal repair action for a deteriorating system. In order to make a reasonable decision, it is necessary to perform an analysis of the uncertainties embedded in deterioration and to evaluate the repair actions based on the expected future cost. Focusing on the power law failure model, the uncertainties related to deterioration are analyzed based on the Bayesian approach. In addition, we develop a decision model for the optimal repair action by applying a repair cost function. A case study is given to illustrate a decision-making process by analyzing the loss incurred due to deterioration.

• Journal of the Korean Data and Information Science Society
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• 제27권1호
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• pp.245-254
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• 2016

Many surveys provide categorical data and there may be one or more missing categories. We describe a nonignorable nonresponse model for the analysis of two-way contingency tables from small areas. There are both item and unit nonresponse. One approach to analyze these data is to construct several tables corresponding to missing categories. We describe a hierarchical Bayesian model to analyze two-way categorical data from different areas. This allows a "borrowing of strength" of the data from larger areas to improve the reliability in the estimates of the model parameters corresponding to the small areas. Also we use a nonignorable nonresponse model with Bayesian uncertainty analysis by placing priors in nonidentifiable parameters instead of a sensitivity analysis for nonidentifiable parameters. We use the griddy Gibbs sampler to fit our models and compute DIC and BPP for model diagnostics. We illustrate our method using data from NHANES III data on thirteen states to obtain the finite population proportions.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2010년도 학술발표회
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• pp.581-585
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• 2010

This study introduced a Bayesian based frequency analysis in which the statistical trend seasonal analysis for hydrologic extreme series is incorporated. The proposed model employed Gumbel and GEV extreme distribution to characterize extreme events and a fully coupled bayesian frequency model was finally utilized to estimate design rainfalls in Seoul. Posterior distributions of the model parameters in both trend and seasonal analysis were updated through Markov Chain Monte Carlo Simulation mainly utilizing Gibbs sampler. This study proposed a way to make use of nonstationary frequency model for dynamic risk analysis, and showed an increase of hydrologic risk with time varying probability density functions. In addition, full annual cycle of the design rainfall through seasonal model could be applied to annual control such as dam operation, flood control, irrigation water management, and so on. The proposed study showed advantage in assessing statistical significance of parameters associated with trend analysis through statistical inference utilizing derived posterior distributions.