• Communications for Statistical Applications and Methods
• /
• 제22권6호
• /
• pp.531-542
• /
• 2015

In this survey, we use the normal linear model to demonstrate the use of the linear Bayes method. The superiorities of linear Bayes estimator (LBE) over the classical UMVUE and MLE are established in terms of the mean squared error matrix (MSEM) criterion. Compared with the usual Bayes estimator (obtained by the MCMC method) the proposed LBE is simple and easy to use with numerical results presented to illustrate its performance. We also examine the applications of linear Bayes method to some other distributions including two-parameter exponential family, uniform distribution and inverse Gaussian distribution, and finally make some remarks.

• Journal of the Korean Statistical Society
• /
• 제27권1호
• /
• pp.1-10
• /
• 1998

In the problem of estimating a vector of unknown regression coefficients under the sum of squared error losses in a hierarchical linear model, we propose the hierarchical Bayes estimator of a vector of unknown regression coefficients in a hierarchical linear model, and then prove the admissibility of this estimator using Blyth's (196\51) method.

• Journal of the Korean Data and Information Science Society
• /
• 제13권2호
• /
• pp.227-234
• /
• 2002

In this paper, we consider hierarchical Bayes generalized linear models for the analysis of longitudinal count data. Specifically we introduce the hierarchical Bayes random effects models. We discuss implementation of the Bayes procedures via Markov chain Monte Carlo (MCMC) integration techniques. The hierarchical Baye method is illustrated with a real dataset and is compared with other statistical methods.

• Communications for Statistical Applications and Methods
• /
• 제8권2호
• /
• pp.407-416
• /
• 2001

Sample surveys are usually designed and analyzed to produce estimates for a large area or populations. Therefore, for the small area estimations, sample sizes are often not large enough to give adequate precision. Several small area estimation methods were proposed in recent years concerning with sample sizes. Here, we will compare simple Bayesian approach with Bayesian prediction for small area estimation based on linear regression model. The performance of the proposed method was evaluated through unemployment population data form Economic Active Population(EAP) Survey.

• 대한음성학회지:말소리
• /
• 제67호
• /
• pp.79-102
• /
• 2008

In this paper, we investigate the relation between Bayes factor and likelihood ratio test (LRT) approaches and apply the neighborhood information of Bayes factor to building an alternate hypothesis model of the LRT system. To consider the neighborhood approaches, we contemplate a distance measure between models and algorithms to be applied. We also evaluate several methods to improve performance of utterance verification using neighborhood information. Among these methods, the system which adopts anti-models built by collecting mixtures of neighborhood models obtains maximum error rate reduction of 17% compared to the baseline, linear and weighted combination of neighborhood models.

• 응용통계연구
• /
• 제35권3호
• /
• pp.445-455
• /
• 2022

본 논문에서는 고차원 희소 회귀분석을 위한 기존의 베이지안 방법들을 소개하고, 다양한 모의실험 세팅에서 성능을 비교한다. 특히, 확장 가능하고 정확한 베이지안 추론을 가능하게 하는 변분 베이즈 방법(variational Bayes method) (Ray와 Szabó, 2021) 에 중점을 둔다. 시뮬레이션 자료를 기반으로 한 희소 고차원 선형회귀분석을 실시하고 변분 베이즈 방법의 성능을 다른 베이지안 및 빈도론 방법들과 비교한다. 로지스틱 회귀분석에서 변분 베이즈 방법의 실제 성능을 확인하기 위해 백혈병 유전자 발현 자료를 사용하여 실자료 분석을 수행한다.

• 대한전자공학회:학술대회논문집
• /
• 대한전자공학회 2001년도 하계종합학술대회 논문집(4)
• /
• pp.159-162
• /
• 2001

In this Paper, a new multi-sensor single-target tracking method in cluttered environment is proposed. Unlike the established methods such as probabilistic data association filter (PDAF), the proposed method intends to reflect the information in detection phase into parameters in tracking so as to reduce uncertainty due to clutter. This is achieved by first modifying the Bayes risk in Bayesian detection criterion to incorporate the likelihood of measurements from multiple sensors. The final estimate is then computed by taking a linear combination of the likelihood and the estimate of measurements. We develop the procedure and discuss the results from representative simulations.

• Journal of the Korean Data and Information Science Society
• /
• 제28권3호
• /
• pp.659-668
• /
• 2017

The inverse Weibull distribution (IWD) can be readily applied to a wide range of situations including applications in medicines, reliability and ecology. It is generally known that the lifetimes of test items may not be recorded exactly. In this paper, therefore, we consider the maximum likelihood estimation (MLE) and Bayes estimation of the entropy of a IWD under generalized progressive hybrid censoring (GPHC) scheme. It is observed that the MLE of the entropy cannot be obtained in closed form, so we have to solve two non-linear equations simultaneously. Further, the Bayes estimators for the entropy of IWD based on squared error loss function (SELF), precautionary loss function (PLF), and linex loss function (LLF) are derived. Since the Bayes estimators cannot be obtained in closed form, we derive the Bayes estimates by revoking the Tierney and Kadane approximate method. We carried out Monte Carlo simulations to compare the classical and Bayes estimators. In addition, two real data sets based on GPHC scheme have been also analysed for illustrative purposes.

• 한국수자원학회논문집
• /
• 제50권7호
• /
• pp.489-502
• /
• 2017

본 연구에서는 충주댐 유역에 대해 다목적 댐 예측유입량 산정기법 BAYES-ESP를 개발하고 평가하였다. BAYES-ESP 기법은 기존 ESP (Ensemble Streamflow Prediction) 기법에 베이지안 이론을 적용하여 개발하였으며, 수문모델은 ABCD를 활용하였다. 입력자료는 기온, 강수량 자료와 댐 관측유입량 자료를 활용하였으며, 기온 및 강수량은 기상청, 국토교통부, 한국수자원공사의 지점관측자료, 댐 관측유입량은 한국수자원공사의 자료를 이용하였다. 적용성 평가방법은 시계열 분석과 Skill Score를 활용하였으며, 평가기간은 1986~2015년이다. 시계열 분석 결과 ESP 댐 예측유입량(ESP)는 매년 전망값의 큰 차이가 없었으며, 다우년 및 과우년의 예측성이 떨어지는 것으로 나타났다. BAYES-ESP 댐 예측유입량(BAYES-ESP)는 ESP가 관측유입량에 비해 과소모의하는 경향을 보정하였으며, 특히 다우년에 개선효과가 있는 것으로 나타났다. 월별 평균 댐 관측유입량과의 Skill Score 비교분석결과 ESP는 1~3월에 SS가 비교적 높은 값을 보였으며, 나머지 월에는 음의 값을 나타내었다. BAYES-ESP는 ESP와 관측 값 간의 선형적 관계를 갖는 1~3월에 ESP의 정확도를 향상시키는 것으로 나타났다. ESP 기법은 국내 강수특성상 우리나라에 적용하기에는 한계가 있었으며, 이를 개선한 BAYES-ESP 기법은 댐 유입량 예측연구에 가치가 있다고 판단된다.

• Communications for Statistical Applications and Methods
• /
• 제27권2호
• /
• pp.189-199
• /
• 2020

This paper studies a Bayesian ordered multiple linear regression model with skew normal error. It is reasonable that the kind of inherent information available in an applied regression requires some constraints on the coefficients to be estimated. In addition, the assumption of normality of the errors is sometimes not appropriate in the real data. Therefore, to explain such situations more flexibly, we use the skew-normal distribution given by Sahu et al. (The Canadian Journal of Statistics, 31, 129-150, 2003) for error-terms including normal distribution. For Bayesian methodology, the Markov chain Monte Carlo method is employed to resolve complicated integration problems. Also, under the improper priors, the propriety of the associated posterior density is shown. Our Bayesian proposed model is applied to NZAPB's apple data. For model comparison between the skew normal error model and the normal error model, we use the Bayes factor and deviance information criterion given by Spiegelhalter et al. (Journal of the Royal Statistical Society Series B (Statistical Methodology), 64, 583-639, 2002). We also consider the problem of detecting an influential point concerning skewness using Bayes factors. Finally, concluding remarks are discussed.