• The Korean Journal of Applied Statistics
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• v.27 no.6
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• pp.991-1002
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• 2014

Both Bayesian and maximum likelihood methods are efficient for the estimation of regression coefficients of various Tobit regression models (see. e.g. Chib, 1992; Greene, 1990; Lee and Choi, 2013); however, some researchers recognized that the maximum likelihood method tends to underestimate the disturbance variance, which has implications for the estimation of marginal effects and the asymptotic standard error of estimates. The underestimation of the maximum likelihood estimate in a seemingly unrelated Tobit regression model is examined. A Bayesian method based on an objective noninformative prior is shown to provide proper estimates of the disturbance variance as well as other regression parameters

• The Transactions of the Korea Information Processing Society
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• v.6 no.8
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• pp.2060-2071
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• 1999

The complicate software failure system is defined to the superposition of the points of failure from several component point process. Because the likelihood function is difficulty in computing, we consider Gibbs sampler using iteration sampling based method. For each observed failure epoch, we applied to latent variables that indicates with component of the superposition mode. For model selection, we explored the posterior Bayesian criterion and the sum of relative errors for the comparison simple pattern with superposition model. A numerical example with NHPP simulated data set applies the thinning method proposed by Lewis and Shedler[25] is given, we consider Goel-Okumoto model and Weibull model with GOS, inference of parameter is studied. Using the posterior Bayesian criterion and the sum of relative errors, as we would expect, the superposition model is best on model under diffuse priors.

• The Korean Journal of Financial Management
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• v.22 no.2
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• pp.43-69
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• 2005

This study provides empirical evidence that the stochastic beta model based on Bayesian analysis outperforms the existing conditional beta model and GARCH model in terms of the estimation accuracy and the explanatory power in the cross-section of stock returns in Korea. Betas estimated by the stochastic beta model explain $30{\sim}50%$ of the cross-sectional variation in stock-returns, whereas other time-varying beta models account for less than 3%. Such a difference in explanatory power across models turns out to come from the fact that the stochastic beta model absorbs the variation due to the market anomalies such as size, BE/ME, and idiosyncratic volatility. These results support the rational asset pricing model in that market anomalies are closely related to the variation of expected returns generated by time-varying betas.

• Journal of the Korean Geophysical Society
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• v.2 no.2
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• pp.143-152
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• 1999

We have studied a denoising technique involving wavelet transform for improving the quality of geophysical data during the preprocessing stage. To assess the effectiveness of this technique, we have made synthetic data contaminated by random noises and compared the results of denoising with those obtained by conventional low-pass filtering. The low-pass filtering of the sinusoidal signal having a sharp discontinuity between the first and last sample values shows apparent errors related to Gibbs' phenomena. For the case of bump signal, the low-pass filtering induces maximum errors on peak values by removing some high-frequency components of signal itself. The wavelet transform technique, however, denoises these signals with much less adverse effects owing to its pertinent properties on locality of wavelet and easy discrimination of noise and signal in the wavelet domain. The field data of gravity tide are denoised by using soft threshold, which shrinked all the wavelet coefficients toward the origin, and the G-factor is determined by comparing the denoised data and theoretical data.

• Journal of the Korean Data and Information Science Society
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• v.28 no.6
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• pp.1457-1469
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• 2017

Private education expenses is one of the key issues in Korea and there have been many discussions about it. Academically, most of previous researches for private education expenses have used multiple regression linear model based on ordinary least squares (OLS) method. However, if the data do not satisfy the basic assumptions of the OLS method such as the normality and homoscedasticity, there is a problem with the reliability of estimations of parameters. In this case, quantile regression model is preferred to OLS model since it does not depend on the assumptions of nonnormality and heteroscedasticity for the data. In the present study, the data from a survey on private education expenses, conducted by Statistics Korea in 2015 has been analyzed for investigation of the impacting factors for private education expenses. Since the data do not satisfy the OLS assumptions, quantile regression model has been employed in Bayesian approach by using gibbs sampling method. The analysis results show that the gender of the student, parent's age, and the time and cost of participating after school are not significant. Household income is positively significant in proportion to the same size for all levels (quantiles) of private education expenses. Spending on private education in Seoul is higher than other regions and the regional difference grows as private education expenditure increases. Total time for private education and student's achievement have positive effect on the lower quantiles than the higher quantiles. Education level of father is positively significant for midium-high quantiles only, but education level of mother is for all but low quantiles. Participating after school is positively significant for the lower quantiles but EBS textbook cost is positively significant for the higher quantiles.

• Journal of the Korea Society of Computer and Information
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• v.14 no.7
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• pp.1-8
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• 2009

In this paper, make a study decision problem called an optimal release policies after testing a software system in development phase and transfer it to the user. The optimal software release policies which minimize a total average software cost of development and maintenance under the constraint of satisfying a software reliability requirement is generally accepted. The Bayesian parametric inference of model using log Poisson execution time employ tool of Markov chain(Gibbs sampling and Metropolis algorithm). In a numerical example by T1 data was illustrated. make out estimating software optimal release time from the maximum likelihood estimation and Bayesian parametric estimation.

• Convergence Security Journal
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• v.6 no.3
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• pp.117-126
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• 2006

Bayesian inference and model selection method for software reliability growth models are studied. Software reliability growth models are used in testing stages of software development to model the error content and time intervals between software failures. In this paper, could avoid multiple integration using Gibbs sampling, which is a kind of Markov Chain Monte Carlo method to compute the posterior distribution. Bayesian inference for general order statistics models in software reliability with diffuse prior information and model selection method are studied. For model determination and selection, explored goodness of fit (the error sum of squares), trend tests. The methodology developed in this paper is exemplified with a software reliability random data set introduced by of Weibull distribution(shape 2 & scale 5) of Minitab (version 14) statistical package.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.13 no.6
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• pp.489-495
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• 2020

The amount of data is increasing through various IT devices in a hyper-connected society where the 4th revolution is progressing, and new value can be created by analyzing that data. This paper was collected total 1,526 articles from 2017 to 2019 in central magazines, economic magazines, regional associations, and major broadcasting companies with the keyword "(East Coast Tourism or East Coast Travel) and Gangwon-do" through Bigkinds. It was performed the topic modeling using LDA algorithm implemented in the R language to analyze the collected 1,526 articles. It was extracted keywords for each year from 2017 to 2019, and classified and compared keywords with high frequency for each year. It was setted the optimal number of topics to 8 using Log Likelihood and Perplexity, and then inferred 8 topics using the Gibbs Sampling method. The inferred topics were Gangneung and Beach, Goseong and Mt.Geumgang, KTX and Donghae-Bukbu line, weekend sea tour, Sokcho and Unification Observatory, Yangyang and Surfing, experience tour, and transportation network infra. The changes of articles on East coast tourism was was analyzed using the proportion of the inferred eight topics. As the result, the proportion of Unification Observatory and Mt. Geumgang showed no significant change, the proportion of KTX and experience tour increased, and the proportion of other topics decreased in 2018 compared to 2017. In 2019, the proportion of KTX and experience tour decreased, but the proportion of other topics showed no significant change.