• 한국수자원학회논문집
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• 제46권4호
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• pp.361-372
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• 2013

본 연구에서는 1961~2010년 사이의 서울 지점 연 최대치 독립 호우사상들을 베타분포로 분석하여 서울지점의 대표 호우사상을 결정하였다. 구체적으로 살펴보면, 먼저, 첨두강우량을 기준으로 연최대치 호우사상들을 상위 50%와 하위 50%로 구분한다. 각각의 경우에 대해 첨두강우량의 평균을 지나는 베타분포를 유도한다. 마지막으로, 유도된 두 베타분포의 산술평균을 우량주상도로 나타내어 대표 호우사상을 결정한다. 이렇게 유도된 대표 호우사상은 실제 호우사상과 유사한 모양을 갖는 것으로 확인되었으며, 특히 Huff 분포에 비해 큰 첨두 강우량을 갖는 것으로 확인되었다. 여러 강우시간분포 모형들과 비교해 본 결과 Keifer & Chu 모형이 본 연구의 결과와 가장 유사한 것으로 나타났다.

• 물과 미래
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• 제11권1호
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• pp.59-68
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• 1978

According to the simplified Gringorten's method of extreme values from data samples, daily maximum precipitation and return period at several stations in the central part of Korea were estimated. And also, it was known that the distribution of daily maximum precipitation of Sogcho, Chuncheon, Kangreung, Seoul, Inchon, Suwon, Seosan, Cheongju and Daejeon area belong to an exponential type of distribution.

• Communications for Statistical Applications and Methods
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• 제3권2호
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• pp.37-42
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• 1996

Maximum likelihood estimation of Cholesky decomposition is considered under normality assumption. It is shown that maximum liklihood estimation gives a Cholesky decomposition of the sample covariance matrix. The joint distribution of the maximum likelihood estimators is derived. The ussual algorithm for a Cholesky decomposition is shown to be equivalent to a maximumlikelihood estimation of a Cholesky root when the underlying distribution is a multivariate normal one.

• Communications for Statistical Applications and Methods
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• 제15권5호
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• pp.765-774
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• 2008

In this paper, we derive the approximate maximum likelihood estimators(AMLEs) and maximum likelihood estimator of the scale parameter in a triangular distribution based on progressive Type-II censored samples. We compare the proposed estimators in the sense of the mean squared error through Monte Carlo simulation for various progressive censoring schemes.

• Communications for Statistical Applications and Methods
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• 제10권3호
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• pp.909-917
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• 2003

Although many classification models have been used to classify binary data, none of the classification models dominates all varying circumstances depending on the number of variables and the size of data(Asparoukhov and Krzanowski (2001)). This paper proposes a classification model which uses information on marginal distributions of sub-variables and its maximum entropy distribution. Classification experiments by using simulation are discussed.

• Journal of the Korean Data and Information Science Society
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• 제11권1호
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• pp.57-64
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• 2000

By assuming a Type-I censored sample, we propose the approximate maximum likelihood estimators(AMLE) of the scale and location parameters of the gamma distribution. We compare the proposed estimators with the maximum likelihood estimators(MLE) in the sense of the mean squared errors(MSE) through Monte Carlo method.

• Communications for Statistical Applications and Methods
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• 제18권5호
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• pp.657-666
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• 2011

In this paper, we derive the maximum likelihood estimator(MLE) and some approximate maximum likelihood estimators(AMLEs) of the scale parameter in an exponentiated half logistic distribution based on progressively Type-II censored samples. We compare the proposed estimators in the sense of the mean squared error(MSE) through a Monte Carlo simulation for various censoring schemes. We also obtain the AMLEs of the reliability function.

• ETRI Journal
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• 제32권1호
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• pp.160-162
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• 2010

For the acoustic models of embedded speech recognition systems, hidden Markov models (HMMs) are usually quantized and the original full space distributions are represented by combinations of a few quantized distribution prototypes. We propose a maximum likelihood objective function to train the quantized distribution prototypes. The experimental results show that the new training algorithm and the link structure adaptation scheme for the quantized HMMs reduce the word recognition error rate by 20.0%.

• Journal of the Korean Data and Information Science Society
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• 제23권1호
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• pp.219-225
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• 2012

We derive approximate maximum likelihood estimators of two parameters in a weighted exponential distribution, and derive the density function for the ratio Y=(X+Y) of two independent weighted exponential random variables X and Y, and then observe the skewness of the ratio density.

• Journal of the Korean Data and Information Science Society
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• 제19권4호
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• pp.1335-1344
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• 2008

The maximum likelihood method does not admit explicit solutions when the sample is multiply censored and progressive censored. So we shall propose some approximate maximum likelihood estimators (AMLEs) of the scale parameter for the power function distribution based on multiply Type-II censored samples and progressive Type-II censored samples when shape parameter is known. We compare the proposed estimators in the sense of the mean squared error (MSE) through Monte Carlo simulation for various censoring schemes.