• ETRI Journal
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• v.32 no.1
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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%.

• 한국데이터정보과학회:학술대회논문집
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• 2006.11a
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• pp.183-195
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• 2006

In this paper, we derive several approximate maximum likelihood estimators of the scale and location parameters in the Rayleigh distribution based on multiply Type-II censored samples. We compare the proposed estimators in the sense of the mean squared error for various censored samples.

• Journal of the Korean Data and Information Science Society
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• v.10 no.1
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• pp.11-18
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• 1999

To estimate the mean and the variance of a lognormal distribution, Finney (1941) derived the uniformly minimun variance unbiased estimators(UMVUE) in the form of infinite series. However, the conditions ${\sigma}^{2}\;>\;n\;and\;{\sigma}^{2}\;<\;\frac{n}{4}$ for computing $E(\hat{\theta}_{AM})\;and\;E(\hat{\eta}^{2}_{AM})$ are necessary. In this paper, we give an alternative derivation of the UMVUE's.

• Journal of the Korean Data and Information Science Society
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• v.19 no.4
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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.

• Journal of the Korean Data and Information Science Society
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• v.16 no.1
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• pp.115-126
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• 2005

In this paper, we derive the approximate maximum likelihood estimators of the scale parameter and location parameter of the double exponential distribution based on Type-II censored samples. We compare the proposed estimators in the sense of the mean squared error for various censored samples.

• Journal of the Korean Data and Information Science Society
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• v.16 no.1
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• pp.145-156
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• 2005

In this paper, we derive the approximate maximum likelihood estimators (AMLEs) of the scale parameter of the half-logistic distribution based on multiply Type-II censored samples. We compare the proposed estimators in the sense of the mean squared error (MSE) for various censored samples.

• Journal of the Korean Data and Information Science Society
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• v.6 no.1
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• pp.97-103
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• 1995

In this paper, we propose estimators of Gini index of the exponential distribution. We also obtain the distribution and the moments of the proposed estimators. The moments of the proposed estimators are derived by special function. We compare the maximum likelihood estimator (MLE) of Gini index with the proposed estimator of Gini index in the sense of MSE through Monte Carlo Method.

• Journal of the Korean Data and Information Science Society
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• v.18 no.3
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• pp.817-826
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• 2007

In this paper, we derive the approximate maximum likelihood estimators of the scale parameter and the location parameter in a generalized extreme value distribution under multiply Type-II censoring by the approximate maximum likelihood estimation method. We compare the proposed estimators in the sense of the mean squared error for various censored samples.

• Journal of the Korean Data and Information Science Society
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• v.25 no.3
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• pp.643-653
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• 2014

In this paper, we propose some estimators for the extreme value distribution based on the interval method and mid-point approximation method from the progressive Type-I interval censored sample. Because log-likelihood function is a non-linear function, we use a Taylor series expansion to derive approximate likelihood equations. We compare the proposed estimators in terms of the mean squared error by using the Monte Carlo simulation.

• Journal of the Korean Statistical Society
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• v.32 no.3
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• pp.289-298
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• 2003

We consider the estimation problem for the scale parameter of the Rayleigh distribution using weighted balanced loss function (WBLF) which reflects both goodness of fit and precision. Under WBLF, we obtain the optimal estimator which creates a kind of balance between Bayesian and non-Bayesian estimation. We also deal with the estimation of R ＝ P(Y < X) when Y and X are two independent but not identically distributed Rayleigh distribution under squared error loss function.