• The Korean Journal of Applied Statistics
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• v.29 no.1
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• pp.99-112
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• 2016

Asymmetric jump-diffusion models are effectively used to model the dynamic behavior of asset prices with abrupt asymmetric upward and downward changes. However, the estimation of their extension to the multivariate asymmetric jump-diffusion model has been hampered by the analytically intractable likelihood function. This article confronts the problem using a data augmentation method and proposes a new Bayesian method for a multivariate asymmetric Laplace jump-diffusion model. Unlike the previous models, the proposed model is rich enough to incorporate all possible correlated jumps as well as mention individual and common jumps. The proposed model and methodology are illustrated with a simulation study and applied to daily returns for the KOSPI, S&P500, and Nikkei225 indices data from January 2005 to September 2015.

• The Korean Journal of Applied Statistics
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• v.31 no.5
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• pp.573-585
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• 2018

In this study, we fit the logistic regression model and naïve Bayesian classifier model using 11 risk factors to predict the incidence rate probability for type 2 diabetes mellitus. We then introduce how to construct a nomogram that can help people visually understand it. We use data from the 2013-2015 Korean National Health and Nutrition Examination Survey (KNHANES). We take 3 interactions in the logistic regression model to improve the quality of the analysis and facilitate the application of the left-aligned method to the Bayesian nomogram. Finally, we compare the two nomograms and examine their utility. Then we verify the nomogram using the ROC curve.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.5
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• pp.655-669
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• 2018

In this study, we developed a hybrid forecasting model based on a four-parameter distribution which allows a simultaneous season-ahead forecasting for both seasonal rainfall and sub-daily rainfall in Han-River and Geum-River basins. The proposed model is mainly utilized a set of time-varying predictors and the associated model parameters were estimated within a Bayesian nonstationary rainfall frequency framework. The hybrid forecasting model was validated through an cross-validatory experiment using the recent rainfall events during 2014~2017 in both basins. The seasonal precipitation results showed a good agreement with the observations, which is about 86.3% and 98.9% in Han-River basin and Geum-River basin, respectively. Similarly, for the extreme rainfalls at sub-daily scale, the results showed a good correspondence between the observed and simulated rainfalls with a range of 65.9~99.7%. Therefore, it can be concluded that the proposed model could be used to better consider climate variability at multiple time scales.

• Communications for Statistical Applications and Methods
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• v.19 no.2
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• pp.237-246
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• 2012

Bayesian methods have been recently used to identify multiple change-points. However, the studies for small data are limited. This paper suggests the Bayesian noncentral t distribution change-point model for small data, and applies the Metropolis-Hastings-within-Gibbs Sampling algorithm to the proposed model. Numerical results of simulation and real data show the performance of the new model in terms of the quality of the resulting estimation of the numbers and positions of change-points for small data.

• KDI Journal of Economic Policy
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• v.31 no.1
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• pp.1-25
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• 2009

This paper attempts to estimate the natural rates of output and interest of Korea in a simple DSGE set-up with a few stylized New Keynesian features using Bayesian methods. The major findings of this paper are as follows. First, the estimates of output gaps are less volatile than the measures from conventional approaches, although they exhibit non-negligible variations depending on the model specification. Another key finding is that the hybrid type Phillips curve with a backward-looking component and/or habit formation in consumption may play an important role in characterizing the macroeconomic dynamics of Korea.

• Journal of the Korean Data and Information Science Society
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• v.24 no.4
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• pp.755-762
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• 2013

In this paper we consider the NHPP software reliability model. And we deal with the maximum likelihood estimation and the Bayesian estimation with conjugate prior for parameter inference in the mean value function of Goel-Okumoto model (1979). The parameter estimates for the proposed model is presented by MLE and Bayes estimator in data set. We compare the predicted number of faults with the actual data set using the proposed mean value function.

• The Korean Journal of Applied Statistics
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• v.18 no.3
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• pp.597-606
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• 2005

The nonresponse in sample survey causes a problem when it comes time to analyze dataset in public-use files where the user has only complete-data methods available and has limited information about the reasons for nonresponse. Recently imputation for nonresponse is becoming a standard approach for handling nonresponse and various imputation methods have been devised . However, most imputation methods concern with continuous traits while many interesting features are measured by binary or ordered categorical scales in sample survey. In this note. an imputation method for ignorable nonresponse in binary or ordered categorical traits is considered.

• 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.

• The Korean Journal of Applied Statistics
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• v.28 no.2
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• pp.137-149
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• 2015

Flood planning needs to recognize trends for extreme precipitation events. Especially, the r-year return level is a common measure for extreme events. In this paper, we present a nonstationary temporal model for precipitation return levels using a hierarchical Bayesian modeling. For intensity, we model annual maximum daily precipitation measured in Korea with a generalized extreme value (GEV). The temporal dependence among the return levels is incorporated to the model for GEV model parameters and a linear model with autoregressive error terms. We apply the proposed model to precipitation data collected from various stations in Korea from 1973 to 2011.

• The Korean Journal of Applied Statistics
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• v.14 no.2
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• pp.449-462
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• 2001

베이지안 다중 검정방법(multiple hypothesis test)은 여러 통계모형에서 성공적인 결과를 주는 것으로 알려져있다. 일반적으로, 베이지안 가설검정은 고려중인 모형에 대한 사후확률을 계산하여 가장 높은 확률은 갖는 모형을 선택하기 때문에 귀무가설의 기각여부에만 관심을 가지는 고전적인 분산분석 검정과는 달리 좀 더 구체적인 모형을 선택할 수 있는 장점이 있다. 이 논문에서는 독립이면서 로그정규분포를 따르는 K($\geq$3)개 모집단의 모수에 대한 가설 검정방법으로 O’Hagan(1995)이 제안한 부분 베이즈 요인을 이용한 베이지안 방법을 제안한다. 이 때 모수에 대한 사전분포로는 무정보적 사전분포를 사용한다. 제안한 검정 방법의 유용성을 알아보기 위하여 실제 자료의 분석과 모의 실험을 이용하여 고전적인 검정방법과 그 결과를 비교한다.