• 대한수학회보
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• 제37권3호
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• pp.507-517
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• 2000

Existence of Gibbs' phenomenon has been well known in wavelet expansions. But the estimation of its size is another problem. Because of the oscillation of wavelets, it is not easy to estimate the Gibbs size of wavelet expansions. For wavelets defined via Fourier transforms, we give a new formula to calculate the size of overshoot. But using this we compute the size of Gibbs effect for Barttle-Lemarier wavelets.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2014년도 춘계학술발표대회
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• pp.612-614
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• 2014

급격한 가격변동에 대한 다양한 속성을 갖는 주가 기대치가 상이하여 기대치 가격을 중심으로 주가가 오르고 내리는 현상이 항상 발생한다. 이 현상은 Gibbs Effect와 매우 흡사한 성격을 갖고 있다. 장이 시작할때와 끝날때의 변동성을 수학적으로 모델링하고 이를 기반으로 한 거래기법을 연구 할 필요성을 갖게되어 시스템구현을 통해 매매기법을 분석하게 되었다.

• 대한수학회논문집
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• 제13권1호
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• pp.181-193
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• 1998

The Shannon sampling series is the prototype of an interpolating series or sampling series. Also the Shannon wavelet is one of the protypes of wavelets. But the coefficients of the Shannon sampling series are different function values at the point of discontinuity, we analyze the Gibbs phenomenon for the Shannon sampling series. We also find a way to go around this overshoot effect.

• Asian-Australasian Journal of Animal Sciences
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• 제25권11호
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• pp.1511-1514
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• 2012

Epistasis that may explain a large portion of the phenotypic variation for complex economic traits of animals has been ignored in many genetic association studies. A Baysian method was introduced to draw inferences about multilocus genotypic effects based on their marginal posterior distributions by a Gibbs sampler. A simulation study was conducted to provide statistical powers under various unbalanced designs by using this method. Data were simulated by combined designs of number of loci, within genotype variance, and sample size in unbalanced designs with or without null combined genotype cells. Mean empirical statistical power was estimated for testing posterior mean estimate of combined genotype effect. A practical example for obtaining empirical statistical power estimates with a given sample size was provided under unbalanced designs. The empirical statistical powers would be useful for determining an optimal design when interactive associations of multiple loci with complex phenotypes were examined.

• 응용통계연구
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• 제27권2호
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• pp.263-275
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• 2014

본 연구에서는 비동차 마코프 체인에서 개체들의 전이 행태를 분석하기 위한 계층적 베이지안 방법론을 사용하여 혼합 효과 모델을 소개 하였다. 모델의 모수들에 대한 사후분포가 분석적으로 구해질 수 없는 형태를 가지기 때문에 깁스(Gibbs) 샘플링 시뮬레이션 방법을 사용하여 조건부 사후확률로부터 샘플이 추출되었고, 실제 자료분석을 예를 사용하였다.

• The Journal of Asian Finance, Economics and Business
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• 제8권4호
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• pp.665-672
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• 2021

The paper examines the impact of shadow economy and corruption, along with public expenditure, trade openness, foreign direct investment (FDI), inflation, and tax revenue on the economic growth of the BRICS countries. Data were collected from the World Bank, Transparency International, and Heritage Foundation over the 1991-2017 period. The Bayesian linear regression method is used to examine whether shadow economy, corruption and other indicators affect the economic growth of countries studied. This paper applies the normal prior suggested by Lemoine (2019) while the posterior distribution is simulated using Monte Carlo Markov Chain (MCMC) technique through the Gibbs sampling algorithm. The results indicate that public expenditure and trade openness can enhance the BRICS countries' economic growth, with the positive impact probability of 75.69% and 67.11%, respectively. Also, FDI, inflation, and tax revenue positively affect this growth, though the probability of positive effect is ambiguous, ranging from 51.13% to 56.36%. Further, the research's major finding is that shadow economy and control of corruption have a positive effect on the economic growth of the BRICS countries. Nevertheless, the posterior probabilities of these two factors are 62.23% and 65.25%, respectively. This result suggests that their positive effect probability is not high.

• Journal of the Korean Statistical Society
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• 제29권1호
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• pp.81-94
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• 2000

For the balanced variance component model, sometimes intraclass correlation coefficient is of interest. If there is little information about the parameter, then the reference prior(Berger and Bernardo, 1992) is widely used. Pettit nd Young(1990) considered a measrue of the effect of a single observation on a logarithmic Bayes factor. However, under such a reference prior, the Bayes factor depends on the ratio of unspecified constants. In order to discard this problem, influence diagnostic measures using the intrinsic Bayes factor(Berger and Pericchi, 1996) is presented. Finally, one simulated dataset is provided which illustrates the methodology with appropriate simulation based computational formulas. In order to overcome the difficult Bayesian computation, MCMC methods, such as Gibbs sampler(Gelfand and Smith, 1990) and Metropolis algorithm, are empolyed.

• Journal of the Korean Data and Information Science Society
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• 제7권2호
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• pp.247-256
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• 1996

본 논문은 깁스 표본 기법을 이용하여 Demster et al.(1981)에 의해 소개된 Field Mice자료를 분석하기 위하여 베이지안 계층적 모형을 적용시켜 보았다. Jeffrey의 사전확률을 이용한 사후 평균을 깁스 표본 기법을 이용하여 구하였고, 이로 부터 얻은 베이지안 추정량을 최소 자승 추정량, EM알고리즘을 이용한 랜덤 효과를 포함한 가능도함수에 대한 최대 가능도 추정량(MLR)과 비교하였다.

• Communications for Statistical Applications and Methods
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• 제23권2호
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• pp.179-187
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• 2016

The fixed effects panel probit model faces "incidental parameters problem" because it has a property that the number of parameters to be estimated will increase with sample size. The maximum likelihood estimation fails to give a consistent estimator of slope parameter. Unlike the panel regression model, it is not feasible to find an orthogonal reparameterization of fixed effects to get a consistent estimator. In this note, a hierarchical Bayesian model is proposed. The model is essentially equivalent to the frequentist's random effects model, but the individual specific effects are estimable with the help of Gibbs sampling. The Bayesian estimator is shown to reduce reduced the small sample bias. The maximum likelihood estimator in the random effects model is also efficient, which contradicts Green (2004)'s conclusion.

• 응용통계연구
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• 제22권3호
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• pp.607-616
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• 2009

In many epidemiological studies, the occurrence times of the event of interest are right-censored or interval censored. In certain situations such as the AIDS data, however, the incubation period which is the time between HIV infection and the diagnosis of AIDS is usually doubly censored. In this paper, we impute the interval censored HIV infection time using three imputation methods. Mid imputation, conditional mean imputation and approximate Bayesian bootstrap are implemented to obtain right censored data, and then Gibbs sampler is used to estimate the coefficient factor of the incubation period. By using Bayesian approach, flexible modeling and the use of prior information is available. We applied both parametric and semi-parametric methods for estimating the effect of the covariate and compared the imputation results incorporating prior information for the covariate effects.