• Communications for Statistical Applications and Methods
• /
• v.22 no.6
• /
• pp.589-598
• /
• 2015

In longitudinal studies missing data are common and require a complicated analysis. There are two popular modeling frameworks, pattern mixture model (PMM) and selection models (SM) to analyze the missing data. We focus on the PMM and we also propose Bayesian pattern mixture models using generalized linear mixed models (GLMMs) for longitudinal binary data. Sensitivity analysis is used under the missing not at random assumption.

• 한국경영정보학회:학술대회논문집
• /
• 2007.06a
• /
• pp.395-400
• /
• 2007

Previous studies present the mixed results on online reputation mechanism. In this study, we have found that an approach based on Bayesian statistics can explain most results of previous studies which are conflicting with each others. With this model, we explain why negative ratings have more significant marginal impacts on sellers' reputation than positive ones do. Furthermore, we even show why the feedbacks with a few negative ratings may increase the value of the item and final prices by confirming buyers' prior beliefs on the sellers' reputation much more than those without negative ratings. Also, we explain why there are not many negative ratings. Even though some studies suggest this because of generosity of users, our model shows that the reason is that the existence of FS itself prevents bad sellers from participating to the market as a signal itself. Even further, we show how this extreme tendency of positive ratings gets even stronger as markets evolve. Finally, to validate our analytical results, we examine the previous studies and see what factors effect the outcomes of their analyses.

• Nuclear Engineering and Technology
• /
• v.56 no.7
• /
• pp.2704-2710
• /
• 2024

Nuclear archaeology research provides scientific methods to reconstruct the operating histories of fissile material production facilities to account for past fissile material production. While it has typically focused on analyzing material in permanent reactor structures, spent fuel or high-level waste also hold information about the reactor operation. In this computational study, we explore a Bayesian inference framework for reconstructing the operational history from measurements of isotope ratios from a sample of nuclear waste. We investigate two different inference models. The first model discriminates between three potential reactors of origin (Magnox, PWR, and PHWR) while simultaneously reconstructing the fuel burnup, time since irradiation, initial enrichment, and average power density. The second model reconstructs the fuel burnup and time since irradiation of two batches of waste in a mixed sample. Each of the models is applied to a set of simulated test data, and the performance is evaluated by comparing the highest posterior density regions to the corresponding parameter values of the test dataset. Both models perform well on the simulated test cases, which highlights the potential of the Bayesian inference framework and opens up avenues for further investigation.

• The Korean Journal of Applied Statistics
• /
• v.24 no.6
• /
• pp.1095-1102
• /
• 2011

The classical two-period, two-sequence crossover design is no longer sufficient to assess various demands in a bioequivalence study. For instance, to estimate the within-subject and between-subject variances of test and reference formulations separately, it is necessary to use a replicate design in which each subject receives at least the reference formulation in two periods. Several designs were studied to satisfy the demands. It is provided a unified Bayesian approach applicable to those study designs. The benefit of the method in the bioequivalence study is discussed.

• Journal of the Korean Data and Information Science Society
• /
• v.10 no.1
• /
• pp.119-133
• /
• 1999

A Markov Chain Monte Carlo method is developed to compute the software reliability model. We consider computation problem for determining of posterior distibution in Bayseian inference. Metropolis algorithms along with Gibbs sampling are proposed to preform the Bayesian inference of the Mixed model with record value statistics. For model determiniation, we explored the prequential conditional predictive ordinate criterion that selects the best model with the largest posterior likelihood among models using all possible subsets of the component intensity functions. To relax the monotonic intensity function assumptions. A numerical example with simulated data set is given.

• The Korean Journal of Applied Statistics
• /
• v.22 no.6
• /
• pp.1167-1176
• /
• 2009

Few studies have investigated the quantitative relationship between port ownership structure and port efficiency with mixed results. This paper therefore contributes to the empirical literature by investigating the impact of port privatization on port efficiency using sample data drawn from the world's major ports. Moreover, this study applies the Bayesian approach to estimate the impact of port ownership on port efficiency. We fit Bayesian stochastic frontier model which is introduced by Griffin and Steel (2007) by WinBUGS. World's 25 main ports data are used for analysis. Based on MCMC sampling, we estimate parameters of the model and efficiency index of each ports. Moreover, we add estimates from package Frontier 4.1c in order to compare them with Bayesian results.

• Communications for Statistical Applications and Methods
• /
• v.22 no.6
• /
• pp.625-637
• /
• 2015

We develop a Bayesian clustering procedure based on a Dirichlet process prior with cluster specific random effects. Gibbs sampling of a normal mixture of linear mixed regressions with a Dirichlet process was implemented to calculate posterior probabilities when the number of clusters was unknown. Our approach (unlike its counterparts) provides simultaneous partitioning and parameter estimation with the computation of the classification probabilities. A Monte Carlo study of curve estimation results showed that the model was useful for function estimation. We find that the proposed Dirichlet process mixture model with cluster specific random effects detects clusters sensitively by combining vague edges into different clusters. Examples are given to show how these models perform on real data.

• The Korean Journal of Applied Statistics
• /
• v.15 no.2
• /
• pp.405-414
• /
• 2002

We consider the problem of estimating the error variance of in a two-way mixed-effects ANOVA model using noninformative priors. First, we derive Jeffreys' prior, a reference prior, and matching priors. We then provide marginal posterior distributions under those noninformative priors. Finally, we provide graphs of marginal posterior densities of the error variance and credible intervals for the error variance in two real data set and compare these credible intervals.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.38 no.2
• /
• pp.249-259
• /
• 2018

More than half of annual rainfall occurs in summer season in Korea due to its climate condition and geographical location. A frequency analysis is mostly adopted for designing hydraulic structure under the such concentrated rainfall condition. Among the various distributions, univariate Gumbel distribution has been routinely used for rainfall frequency analysis in Korea. However, the distributional changes in extreme rainfall have been globally observed including Korea. More specifically, the univariate Gumbel distribution based rainfall frequency analysis is often fail to describe multimodal behaviors which are mainly influenced by distinct climate conditions during the wet season. In this context, we purposed a Gumbel mixture distribution based rainfall frequency analysis with a Bayesian framework, and further the results were compared to that of the univariate. It was found that the proposed model showed better performance in describing underlying distributions, leading to the lower Bayesian information criterion (BIC) values. The mixed Gumbel distribution was more robust for describing the upper tail of the distribution which playes a crucial role in estimating more reliable estimates of design rainfall uncertainty occurred by peak of upper tail than single Gumbel distribution. Therefore, it can be concluded that the mixed Gumbel distribution is more compatible for extreme frequency analysis rainfall data with two or more peaks on its distribution.

• Safety and Health at Work
• /
• v.7 no.4
• /
• pp.322-325
• /
• 2016

Background: Musculoskeletal disorders (MSDs) are a common problem among carpet weavers. This study was undertaken to introduce affecting personal and occupational factors in developing the number of MSDs among carpet weavers. Methods: A cross-sectional study was performed among 862 weavers in seven towns with regard to workhouse location in urban or rural regions. Data were collected by using questionnaires that contain personal, workplace, and information tools and the modified Nordic MSDs questionnaire. Statistical analysis was performed by applying Poisson and negative binomial mixed models using a full Bayesian hierarchical approach. The deviance information criterion was used for comparison between models and model selection. Results: The majority of weavers (72%) were female and carpet weaving was the main job of 85.2% of workers. The negative binomial mixed model with lowest deviance information criterion was selected as the best model. The criteria showed the convergence of chains. Based on 95% Bayesian credible interval, the main job and weaving type variables statistically affected the number of MSDs, but variables age, sex, weaving comb, work experience, and carpet weaving looms were not significant. Conclusion: According to the results of this study, it can be concluded that occupational factors are associated with the number of MSDs developing among carpet weavers. Thus, using standard tools and decreasing hours of work per day can reduce frequency of MSDs among carpet weavers.