• Communications for Statistical Applications and Methods
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• 제20권5호
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• pp.387-394
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• 2013

In this paper, we develop the noninformative priors for the ratio of the scale parameters in the inverted exponential distributions. The first and second order matching priors, the reference prior and Jeffreys prior are developed. It turns out that the second order matching prior matches the alternative coverage probabilities, is a cumulative distribution function matching prior and is a highest posterior density matching prior. In addition, the reference prior and Jeffreys' prior are the second order matching prior. We show that the proposed reference prior matches the target coverage probabilities in a frequentist sense through a simulation study as well as provide an example based on real data is given.

• Journal of the Korean Statistical Society
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• 제32권3호
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• pp.271-288
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• 2003

In this paper, when the variance of the normal distribution is related to the mean, we develop noninformative priors such as matching priors and reference priors. We prove that the second order matching prior matches alternative coverage probabilities up to the same order and also it is a HPD matching prior. It turns out that one-at-a-time reference prior satisfies a second order matching criterion. Then using these noninformative priors, we develop a Bayesian test procedure for the equality of the means and variances of two independent normal distributions using fractional Bayes factor. Some simulation study is performed, and a real data example is also provided.

• Journal of the Korean Statistical Society
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• 제35권3호
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• pp.225-238
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• 2006

This paper considers noninformative priors for the scale parameter of exponential distribution when the data are collected in step stress accelerated life tests. We find the Jeffreys' and reference priors for this model and show that the reference prior satisfies first order matching criterion. Also, we show that there exists no second order matching prior in this problem. Some simulation results are given and we perform Bayesian analysis for proposed priors using some data.

• Communications for Statistical Applications and Methods
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• 제9권2호
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• pp.401-410
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• 2002

This paper revisits parallel-line bioassay problem, from a Bayesian point of view using noninformative priors such as Jeffreys' prior, reference priors, and probability matching priors. After finding the orthogonal transformation, the class of first order and second order probability matching priors are derived. Jeffreys' prior and reference priors are derived also. Numerical examples are given to show the effectiveness of noninformative priors.

• Journal of the Korean Statistical Society
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• 제36권1호
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• pp.77-91
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• 2007

This paper considers development of noninformative priors for the familial data when the families have equal number of offspring. Several noninformative priors including the widely used Jeffreys' prior as well as the different reference priors are derived. Also, a simultaneously-marginally-probability-matching prior is considered and probability matching priors are derived when the parameter of interest is inter- or intra-class correlation coefficient. The simulation study implemented by Gibbs sampler shows that two-group reference prior is slightly edge over the others in terms of coverage probability.

• 한국통계학회:학술대회논문집
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• 한국통계학회 2003년도 추계 학술발표회 논문집
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• pp.15-19
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• 2003

In this paper, we develop the Jeffreys' prior, reference priors and the probability matching priors for the intraclass correlation coefficient of a symmetric normal distribution. We next verify propriety of posterior distributions under those noninformative priors. We examine whether reference priors satisfy the probability matching criterion.

• Communications for Statistical Applications and Methods
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• 제14권2호
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• pp.431-442
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• 2007

In this paper, we develop the noninformative priors when the parameter of interest is the difference between quantiles of two exponential distributions. We want to develop the first and second order probability matching priors. But we prove that the second order probability matching prior does not exist. It turns out that Jeffreys' prior does not satisfy the first order matching criterion. The Bayesian credible intervals based on the first order probability matching prior meet the frequentist target coverage probabilities much better than the frequentist intervals of Jeffreys' prior. Some simulation and real example will be given.

• 대한의용생체공학회:학술대회논문집
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• 대한의용생체공학회 1998년도 추계학술대회
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• pp.62-63
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• 1998

In this paper, a new method to find the corpus callosum from sagittal brain MR images is proposed, which uses the statistical characteristics and shape information of corpus callosum. First, we extract regions satisfying the statistical characteristics of the corpus callosum and then find a region matching the shape information. In order to match the shape information, a new directed window region growing algorithm is proposed instead of using conventional contour matching algorithms. Using the proposed algorithm, we adaptively relax the statistical requirement until we find a region matching the shape information. Experiments show very promising results.

• Communications for Statistical Applications and Methods
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• 제15권3호
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• pp.429-440
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• 2008

In this paper, we develop the noninformative priors when the parameter of interest is the common coefficient of variation in two inverse Gaussian distributions. We want to develop the first and second order probability matching priors. But we prove that the second order probability matching prior does not exist. It turns out that the one-at-a-time and two group reference priors satisfy the first order matching criterion but Jeffreys' prior does not. The Bayesian credible intervals based on the one-at-a-time reference prior meet the frequentist target coverage probabilities much better than that of Jeffreys' prior. Some simulations are given.

• 스마트미디어저널
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• 제3권3호
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• pp.15-19
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• 2014

지문 인식 시스템은 지문인식 장치(fingerprint acquisition device)를 통하여 아날로그(analog) 지문으로부터 변환된 디지털(digital) 지문에서 특이점을 추출하고 추출한 특이점들에 대한 정보를 데이터베이스에 등록하는 오프라인(off-line) 처리와 사용자가 시스템에 접근 할 때 입력한 지문으로부터 특이점을 추출한 후 데이터베이스에 저장되어 있는 특이점들과 정합(matching) 하여 사용자의 시스템 접근 여부를 결정하는 온라인(on-line) 처리로 이루어져 있다. 본 논문에서는 다양한 생체인식시스템 중, 통계 지문 인식 정합 방법인 지문의 특이점 을 사용한다. 좌표를 기준으로서 유사성 거리 측정을 하고, 그리고 특정한 각도, 통계 배포 판에 따라서 지문 인식 모델을 찾는 것이다.