• Journal of the Korean Statistical Society
• /
• 제32권2호
• /
• pp.175-191
• /
• 2003

An empirical Bayesian approach is discussed for estimation of characteristics from the two-way balanced rotation sampling design which includes U.S. Current Population Survey and Canadian Labor Force Survey as special cases. An empirical Bayesian estimator is derived for monthly effect under presence of two types of biases and correlations It is shown that the marginal distribution of observation provides more general correlation structure than that frequentist has assumed. Consistent estimators are derived for hyper-parameters in Normal priors.

• 품질경영학회지
• /
• 제42권4호
• /
• pp.747-756
• /
• 2014

Purpose: The purpose of this study is to apply the Bayesian estimation methodology for producing 'Korean Standard -Quality Excellence Index' model and prove the effectiveness of the new approach based on survey data by comparing the current index with the new index produced by Bayesian estimation method. Methods: The 'Korean Standard -Quality Excellence Index' was produced through the collected survey data by Bayesian estimation method and comparing the deviation with two results for confirming the effectiveness of suggested application. Results: The statistical analysis result shows that suggested estimator, that is, empirical Bayes estimator improves the effectiveness of the index with regard to reduce the error under specific loss function, which is suggested for checking the goodness of fit. Conclusion: Considering the Bayesian techniques such as empirical Bayes estimator for producing the quality excellence index reduces the error for estimating the parameter of interest and furthermore various Bayesian perspective approaches seems to be meaningful for producing the corresponding index.

• Communications for Statistical Applications and Methods
• /
• 제19권6호
• /
• pp.885-898
• /
• 2012

In this paper, we consider Bayesian approaches to partially linear models, in which a regression function is represented by a semiparametric additive form of a parametric linear regression function and a nonparametric regression function. We make a comparative study on the performance of widely used Bayesian partially linear models in terms of empirical analysis. Specifically, we deal with three Bayesian methods to estimate the nonparametric regression function, one method using Fourier series representation, the other method based on Gaussian process regression approach, and the third method based on the smoothness of the function and differencing. We compare the numerical performance of three methods by the root mean squared error(RMSE). For empirical analysis, we consider synthetic data with simulation studies and real data application by fitting each of them with three Bayesian methods and comparing the RMSEs.

• 자원ㆍ환경경제연구
• /
• 제11권1호
• /
• pp.99-119
• /
• 2002

As an alternative to classical maximum likelihood approach for analyzing dichotomous choice contingent valuation (DCCV) data, this paper develops a Bayesian approach. By using the idea of Gibbs sampling and data augmentation, the approach enables one to perform exact inference for DCCV models. A by-product from the approach is welfare measure, such as the mean willingness to pay, and its confidence interval, which can be used for policy analysis. The efficacy of the approach relative to the classical approach is discussed in the context of empirical DCCV studies. It is concluded that there appears to be considerable scope for the use of the Bayesian analysis in dealing with DCCV data.

• Earthquakes and Structures
• /
• 제12권6호
• /
• pp.653-663
• /
• 2017

A seismic margin assessment evaluates how much margin exists for the system under beyond design basis earthquake events. Specifically, the seismic margin for the entire system is evaluated by utilizing a systems analysis based on the sub-system and component seismic fragility data. Each seismic fragility curve is obtained by using empirical, experimental, and/or numerical simulation data. The systems analysis is generally performed by employing a fault tree analysis. However, the current practice has clear limitations in that it cannot deal with the uncertainties of basic components and accommodate the newly observed data. Therefore, in this paper, we present a Bayesian-based seismic margin assessment that is conducted using seismic fragility data and fault tree analysis including Bayesian inference. This proposed approach is first applied to the pooltype nuclear research reactor system for the quantitative evaluation of the seismic margin. The results show that the applied approach can allow updating by considering the newly available data/information at any level of the fault tree, and can identify critical scenarios modified due to new information. Also, given the seismic hazard information, this approach is further extended to the real-time risk evaluation. Thus, the proposed approach can finally be expected to solve the fundamental restrictions of the current method.

• 대한산업공학회지
• /
• 제26권3호
• /
• pp.193-199
• /
• 2000

In many studies, considerable attention has been focussed upon choosing a model which represents underlying process of time series and forecasting the future. In the real world, however, there may be some cases that one model can not reflect all the characteristics of original time series. Under such circumstances, we may get better performance by combining the forecasts from several models. The most popular methods for combining forecasts involve taking a weighted average of multiple forecasts. But the weights are usually unstable. In cases the assumptions of normality and unbiasedness for forecast errors are satisfied, a Bayesian method can be used for updating the weights. In the real world, however, there are many circumstances the Bayesian method is not appropriate. This paper proposes a PNN(Probabilistic Neural Net) approach as a method for combining forecasts that can be applied when the assumption of normality or unbiasedness for forecast errors is not satisfied. In this paper, PNN method, which is similar to Bayesian approach, is suggested as an updating method of the unstable weights in the combination of the forecasts. The PNN method has been usually used in the field of pattern recognition. Unlike the Bayesian approach, it requires no assumption of a specific prior distribution because it gets probabilities by using the distribution estimated from given data. Empirical results reveal that the PNN method offers superior predictive capabilities.

• 인지과학
• /
• 제27권4호
• /
• pp.541-572
• /
• 2016

인과 추론은 심리학에서는 물론 최근 베이스 접근법을 취하는 인지과학자들에 의해서도 활발히 연구되고 있다. 본 연구는 인과추론에 대한 대표적 심리학 이론인 힘-확률대비이론(a power probabilistic contrast theory of causality)을 중심으로 인과 추론의 최근 동향을 개관하고자 한다. 힘-확률대비이론에서는, 원인은 결과를 일으키거나 억제하는 힘(power)인데, 이 힘은 특정한 조건하에서 통계적 상관을 통해 파악될 수 있다고 가정한다. 본 논문에서는 이 이론에 대한 초기의 경험적 지지 증거를 먼저 살펴본 다음, 베이스 접근에 기반을 둔 이론과의 쟁점을 명확히 하고, 원인은 맥락에 무관하게 동일하게 작동한다는 인과적 불변성 가정(causal invariance hypothesis)을 중심으로 한 보다 최근의 연구 결과를 소개하고자 한다. 이 연구들은 종래의 통계적 접근법으로는 잘 설명되지 않는 결과를 제시함으로써, 철학, 통계학, 그리고 인공 지능 등과 같은 인접 분야에 인과성에 대한 힘 이론을 진지하게 고려할 것을 촉구하고 있다.

• 한국지능정보시스템학회:학술대회논문집
• /
• 한국지능정보시스템학회 2005년도 춘계학술대회
• /
• pp.232-239
• /
• 2005

Collaborative-filtering-enabled Web sites that recommend books, CDs, movies, and so on, have become very popular on the Internet. Such sites recommend items to a user on the basis of the opinions of other users with similar tastes. This paper discuss an approach to collaborative filtering based on the Simple Bayesian and apply this model to two variants of the collaborative filtering. One is user-based collaborative filtering, which makes predictions based on the users' similarities. The other is item-based collaborative filtering which makes predictions based on the items' similarities. To evaluate the proposed algorithms, this paper used a database of movie recommendations. Empirical results show that the proposed Bayesian approaches outperform typical correlation-based collaborative filtering algorithms.

• 응용통계연구
• /
• 제20권2호
• /
• pp.229-244
• /
• 2007

국내외에서 소지역 추정에 관한 많은 연구가 진행되고 있다. 보조 자료가 충분히 있는 경우 모형기반 추정법을 사용하는 것이 일반적이며 이 중에서 계층적 베이지안(Hierarchical Bayesian: HB) 추정법이 가장 좋은 것으로 알려져 있다. 그러나 보조 자료가 충분하지 않은 경우에는 모형 기반 추정법의 사용은 제한적이다. 최근 충분한 보조 자료가 없는 경우 공간 정보를 보조 자료로 사용하는 방법이 제안되었다. 본 논문에서는 공간통계량과 베이즈 접근방법을 활용한 모형기반의 소지역 통계량들을 모형 검진방법(Diagnostic method)들을 이용하여 비교 분석하였다. 분석에 사용된 자료는 2005년도 경제활동인구 조사이며 소지역(시,군,구)통계를 추정하여 비교하였다.

• 응용통계연구
• /
• 제18권1호
• /
• pp.67-81
• /
• 2005

프레일티모형에 대한 기존의 추론방법은 동측치가 많은 경우에 그 성능이 떨어진다. 그 이유는 사용된 경험적 우도함수가 동측치가 많은 자료에는 적합하지 않기 때문이다. 본 논문에서는 동측치가 많은 프레일티 모형에서의 새로운 추론방법을 제안한다. 이항형태의 경험적우도함수를 바탕으로 베이지안 부스트랩을 사용하여 모수의 사후분포를 구한다. 제안된 방법의 장점은 기존에 제안된 주변최대우도추정량에 비하여 계산이 수월하고 안정적인 결과를 제공하는데 있다. 이를 실증적으로 비교하기 위하여 제안된 방법을 주변최대우도추정량과 가상실험을 통하여 비교한다.