• 응용통계연구
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• 제24권6호
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• pp.1095-1102
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• 2011

The classical two-period, two-sequence crossover design is no longer sufficient to assess various demands in a bioequivalence study. For instance, to estimate the within-subject and between-subject variances of test and reference formulations separately, it is necessary to use a replicate design in which each subject receives at least the reference formulation in two periods. Several designs were studied to satisfy the demands. It is provided a unified Bayesian approach applicable to those study designs. The benefit of the method in the bioequivalence study is discussed.

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

• Journal of the Korean Data and Information Science Society
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• 제18권4호
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• pp.1179-1189
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• 2007

In this paper, we estimate the proportion of individuals having health insurance in a given year for several small domains cross-classified by age, sex and other demographic characteristics using the data provided by the National Center for Health Statistics(NCHS). We employ Bayesian as well as frequentist methodology to obtain small domain estimates and the associated measures of precision. One of the new features of our study is that we utilize the survey weights along with the model to derive the small domain estimates.

• 한국통신학회논문지
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• 제19권8호
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• pp.1446-1452
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• 1994

본 논문은 일반적으로 채택하고 있는 소자(device)의 수명분포인 와이블(Weibull) 분포를 적용하여 소자의 가속(accelerated) 수명 테스트에서 얻은 데이터, 즉 소자의 고정 시간을 이용하여 소자의 수명을 예측(prediction)하는데 필요한 보수(parameter)들을 추정 하는데 베이지안(Bayesian) 추정법을 이용하였다. 베이지안 추정법에서 모수를 추정하기 위해서는 사전정보가 있어야 하는데 본 논문에서는 사전정보 없이 현재의 정보만을 이용하여 모수를 추정하는 방법을 제안하였다. 스트레스가 온도인 경우, Arrhenius 모델을 적용하여 소자의 정상동작 상태에서의 수명을 예측 하는데 선형 추정을 하였다.

• Animal Bioscience
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• 제34권2호
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• pp.185-191
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• 2021

Objective: The objective of this study was to estimate the genetic parameters for worm resistance (WR) and associated characteristics, using the linear-threshold animal model via Bayesian inference in single- and multiple-trait analyses. Methods: Data were collected from a herd of Santa Inês breed sheep. All information was collected with animals submitted to natural contamination conditions. All data (number of eggs per gram of feces [FEC], Famacha score [FS], body condition score [BCS], and hematocrit [HCT]) were collected on the same day. The animals were weighed individually on the day after collection (after 12-h fasting). The WR trait was defined by the multivariate cluster analysis, using the FEC, HCT, BCS, and FS of material collected from naturally infected sheep of the Santa Inês breed. The variance components and genetic parameters for the WR, FEC, HCT, BCS, and FS traits were estimated using the Bayesian inference under the linear and threshold animal model. Results: A low magnitude was obtained for repeatability of worm-related traits. The mean values estimated for heritability were of low-to-high (0.05 to 0.88) magnitude. The FEC, HCT, BCS, FS, and body weight traits showed higher heritability (although low magnitude) in the multiple-trait model due to increased information about traits. All WR characters showed a significant genetic correlation, and heritability estimates ranged from low (0.44; single-trait model) to high (0.88; multiple-trait model). Conclusion: Therefore, we suggest that FS be included as a criterion of ovine genetic selection for endoparasite resistance using the trait defined by multivariate cluster analysis, as it will provide greater genetic gains when compared to any single trait. In addition, its measurement is easy and inexpensive, exhibiting greater heritability and repeatability and a high genetic correlation with the trait of resistance to worms.

• 한국수자원학회논문집
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• 제48권10호
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• pp.793-806
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• 2015

미계측유역의 유출량 모의는 수문학 분야에서 필수적인 사항이다. 강우-유출 모형을 이용하여 신뢰성 있는 유출량을 모의하기 위한 핵심사항은 강우-유출 모형의 매개변수를 추정하는 것이다. 하지만 현재 우리나라는 불충분한 수문자료로 인해 매개변수 추정에 어려움이 존재한다. 본 연구의 목표는 불확실성 반영을 위한 Bayesian 통계기법 기반의 강우-유출 모형의 매개변수를 지역화 하는 것이다. 그 방법은 다음과 같다. 첫째, 본 연구는 세계적으로 널리 사용되고 있는 Sacramento 강우-유출 모형에 Bayesian Markov Chain Monte Carlo 기법을 연계한 Bayesian Sacramento 강우-유출 모형을 사용하여 계측유역을 대상으로 13개 매개변수를 최적화하고 각 매개변수의 사후분포를 도출하였다. 둘째, 매개변수와 유역특성인자 사이에 회귀특성을 얻기 위해 다중선형회귀분석을 적용하여 유역특성을 고려한 지역화 매개변수를 결정하였다. 다중회귀분석을 통하여 산정된 지역화 매개변수를 계측유역에 전이하여 유출량을 모의 후 통계적 효율기준인 N-S계수, 일치계수 및 상관계수를 사용하여 지역화 매개변수 검증을 수행하였다.

• 한국경영과학회지
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• 제16권1호
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• pp.113-119
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• 1991

The author is currently assistant professor of Management Science at Korea Advanced Institute of Science and Technology, following a few years as assistant professor of Industrial Engineering at Kyung Hee University, Korea. He received his doctorate from the department of Industrial Engineering and Operations Research, University of California, Berkeley. His research interests are time series and forecasting modelling, Bayesian forecasting and the related software development. He is now teaching time series analysis and econometrics at the graduate level.

• 한국정보과학회논문지:소프트웨어및응용
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• 제31권1호
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• pp.1-7
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• 2004

본 논문에서는 필기체 숫자인식을 위해서 계층적으로 서로 다른 레벨의 정보를 표현할 수 있는 구조화된 특징들의 추출 방법과 특징들 사이에 의존도를 이용하여 분류하는 베이지안 망을 제안한다. 이러한 계층적 특징들을 추출하기 위해서 레벨 단위로 가버 필터들을 정의하고, FLD(Fisher Linear Discriminant) 척도를 이용하여 최적화된 가버 필터들을 선택한다. 계층적 가버 특징들은 최적화된 가버 특징들을 이용하여 추출되며, 하위 레벨일수록 더욱 국부적인 정보를 표현한다. 추출된 계층적 가버 특징들의 분류성능 향상을 위해서 가버 특징들 사이의 계층적 의존도를 이용하는 베이지안 망을 생성한다. 본 논문에서 제안하는 방법은 naive Bayesian 분류기, k-nearest neighbor 분류기, 그리고 신경망 분류기들과 함께 필기체 숫자인식에 적용되어 계층적 가버 특징들의 효율성과 계층적 의존도를 이용하는 베이지안 망은 분류성능을 향상시킬 수 있다는 것을 보여준다.

• Communications for Statistical Applications and Methods
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• 제29권3호
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• pp.373-391
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• 2022

The distribution of observations in most econometric studies with spatial heterogeneity is skewed. Usually, a single transformation of the data is used to approximate normality and to model the transformed data with a normal assumption. This assumption is however not always appropriate due to the fact that panel data often exhibit non-normal characteristics. In this work, the normality assumption is relaxed in spatial mixed models, allowing for spatial heterogeneity. An inference procedure based on Bayesian mixed modeling is carried out with a multivariate skew-elliptical distribution, which includes the skew-t, skew-normal, student-t, and normal distributions as special cases. The methodology is illustrated through a simulation study and according to the empirical literature, we fit our models to non-life insurance consumption observed between 1998 and 2002 across a spatial panel of 103 Italian provinces in order to determine its determinants. Analyzing the posterior distribution of some parameters and comparing various model comparison criteria indicate the proposed model to be superior to conventional ones.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2003년도 학술회의 논문집 정보 및 제어부문 B
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• pp.588-591
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• 2003

This paper presents a new short-term load forecasting system using data mining. Since the electric load has very different pattern according to the day, it definitely gives rise to the forecasting error if only one forecasting model is used. Thus, to resolve this problem, the fuzzy model-based classifier and predictor are proposed for the forecasting of the hourly electric load. The proposed classifier is the multi-input and multi-output fuzzy system of which the consequent part is composed of the Bayesian classifier. The proposed classifier attempts to categorize the input electric load into Monday, Tuesday$\sim$Friday, Saturday, and Sunday electric load, Then, we construct the Takagi-Sugeno (T-S) fuzzy model-based predictor for each class. The parameter identification problem is converted into the generalized eigenvalue problem (GEVP) by formulating the linear matrix inequalities (LMIs). Finally, to show the feasibility of the proposed method, this paper provides the short-term load forecasting example.