• Proceedings of the Korean Association for Survey Research Conference
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• 2000.11a
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• pp.229-250
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• 2000

신뢰할 만한 소지역 통계 작성을 위한 다양한 소지역 추정 기법들이 최근 많은 관심속에 개발되고 있다. 이 논문은 다양한 소지역 추정 기법들 중 일부 기법들에 대한 간략한 소개 및 실례를 제시한다. 먼저 대표적인 소지역에 대한 간접추정법인 인구통계학적 방법, 합성추정법과 복합추정법에 관한 이론 및 추정절차를 살펴보았고, 모형 기반 추정법으로써 경험적 베이즈(EB) 추정법과 계층적 베이즈(HB) 추정법을 소개하였다. 마지막으로 합성추정법과 복합추정법을 이용하여 충북의 시군구 실업자 추정에 적용해 보았고, 시군구 실업자 추정결과를 직접 추정법의 결과와 비교하였다.

• Journal of the Korean Data and Information Science Society
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• v.12 no.2
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• pp.47-55
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• 2001

Usually estimating the means is used for statistical inference. However depending the purpose of survey, sometimes totals will give the better and more meaningful in statistical inference than the means. Here in this study, we dealt with the unemployment population of small areas with using 4 different small area estimation methods: Direct, Synthetic, Composite, Bayes estimation. For all the estimates considered in this study, the average of absolute bias and men square error were obtained in the Monte Carlo Study which was simulated using data from 1998 Economic Active Population Survey in Korea.

• The Korean Journal of Applied Statistics
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• v.12 no.2
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• pp.605-619
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• 1999

본 논문은 회귀분석에서 오차항의 1차 자기상관 존재 여부 및 그 값을 검정하는 방법을 베이지안 접근법으로 제안하였다. 이 방법은 모수공간의 다중분할로 인해 얻어진 여러 가설들에 대한 다중결정문제를 다중 베이즈요인에 관한 이론과 일반화 Savage-Dickey 밀도비를 이용한 사후확률 추정법을 합성하여 개발되었다. 이 방법은 기존의 검정법들에서 가능한 검정 뿐 아니라 이들이 해결할 수 없는 자기상관에 대한 다중결정문제에도 사용이 가능한데 그 효용성이 있다. 모의실험을 통하여 제안된 검정법의 유효성을 평가하였다.

• The Korean Journal of Applied Statistics
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• v.11 no.1
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• pp.97-111
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• 1998

This paper suggests a Bayesian method for testing first-order markov correlation among linear regression disturbances. As a Bayesian test criterion, Bayes factor is derived in the form of generalized Savage-Dickey density ratio that is easily estimated by means of posterior simulation via Gibbs sampling scheme. Performance of the Bayesian test is evaluated and examined based upon a Monte Carlo experiment and an empirical data analysis. Efficiency of the posterior simulation is also examined.

• The Korean Journal of Applied Statistics
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• v.15 no.1
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• pp.139-151
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• 2002

This study is concerned with model selection and diagnostics for nonlinear regression model through Bayes factor. In this paper, we use informative prior and simulate observations from the posterior distribution via Markov chain Monte Carlo. We propose the Laplace approximation method and apply the Laplace-Metropolis estimator to solve the computational difficulty of Bayes factor.

• Journal of the Korean Data and Information Science Society
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• v.27 no.5
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• pp.1133-1146
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• 2016

Counts or averages over arbitrary regions are often analyzed using conditionally autoregressive (CAR) models. The spatial neighborhoods within CAR model are generally formed using only the inter-distance or boundaries between the sub-regions. Kyung and Ghosh (2009) proposed a new class of models to accommodate spatial variations that may depend on directions, using different weights given to neighbors in different directions. The proposed model, directional conditionally autoregressive (DCAR) model, generalized the usual CAR model by accounting for spatial anisotropy. Bayesian inference method is discussed based on efficient Markov chain Monte Carlo (MCMC) sampling of the posterior distributions of the parameters. The method is illustrated using a data set of median property prices across Greater Glasgow, Scotland, in 2008.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.6
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• pp.57-63
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• 2008

In this paper, we present an efficient noise reduction method using bivariate Gaussian density function in the wavelet domain. In our method, the probability model for the interstate dependency in the wavelet domain is modeled by bivariate Gaussian function, and then, the noise reduction is performed by Bayesian estimation. The statistical parameter for Bayesian estimation can be approximately obtained by the $H{\ddot{o}}lder$ inequality. The simulation results show that our method outperforms the previous methods using bivariate probability models.

• The Journal of the Acoustical Society of Korea
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• v.12 no.5
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• pp.32-37
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• 1993

여러 신호원의 방향을 추정하는 결정-추정 방법을 제안하였다. 이 방법은 조건부 평균 다중신호분류 영 스펙트럼에 바탕을 두고 있으며 신호원수를 모를때에도 쓸 수 잇다. 컴퓨터 모의 실험으로 MUSIC dud tvprxmfja의 분해 확률과 조건부 평균 MUSIC dud 스펙트럼의 분해 확률은 거의 같다는 것을 알 수 있어?. 그리고 신호원 수를 결정할 때 정보 이론적 판단 기준과 베이즈 접근 방법이 같은 결과를 낸다는 것도 알 수 있었다.

• The Korean Journal of Applied Statistics
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• v.20 no.2
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• pp.229-244
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• 2007

Among the small area estimation methods, it has been known that hierarchical Bayesian(HB) approach is the most reasonable and effective method. However any model based approaches need good explanatory variables and finding them is the key role in the model based approach. As the lacking of explanatory variables, adopting the spatial terms in the model was introduced. Here in this paper, we evaluate the model based methods with/without spatial terms using the diagnostic methods which were introduced by Brown et al. (2001). And Economic Active Population Survey(2005) is used for data analysis.

• The Korean Journal of Applied Statistics
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• v.28 no.3
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• pp.407-415
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• 2015

$Na{\ddot{i}}ve$ Bayes Classification is based on input variables that are a conditionally independent given output variable. The $Na{\ddot{i}}ve$ Bayes assumption is unrealistic but simplifies the problem of high dimensional joint probability estimation into a series of univariate probability estimations. Thus $Na{\ddot{i}}ve$ Bayes classier is often adopted in the analysis of massive data sets such as in spam e-mail filtering and recommendation systems. In this paper, we propose a variable selection method based on ${\chi}^2$ statistic on input and output variables. The proposed method retains the simplicity of $Na{\ddot{i}}ve$ Bayes classier in terms of data processing and computation; however, it can select relevant variables. It is expected that our method can be useful in classification problems for ultra-high dimensional or big data such as the classification of diseases based on single nucleotide polymorphisms(SNPs).