• Communications for Statistical Applications and Methods
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• 제16권2호
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• pp.349-361
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• 2009

In this paper, we derive the approximate maximum likelihood estimators of the shape parameter and the scale parameter in a Weibull distribution under multiply Type-II censoring by the approximate maximum likelihood estimation method. We develop three modified empirical distribution function type tests for the Weibull distribution based on multiply Type-II censored samples. We also propose modified normalized sample Lorenz curve plot and new test statistic.

• Journal of the Korean Statistical Society
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• 제25권2호
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• pp.161-173
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• 1996

When we deal with a Hilbert space-valued Stochastic Differential Equation (SDE) (or Stochastic Partial Differential Equation (SPDE)), depending on some unknown parameters, the solution usually has a Fourier series expansion. In this situation we consider the maximum likelihood method for the statistical estimation problem and derive the asymptotic properties (consistency and normality) of the Maximum Likelihood Estimator (MLE).

• 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.

• Journal of the Korean Data and Information Science Society
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• 제18권3호
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• pp.793-801
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• 2007

For multiply Type-II censored samples from a half-triangle distribution, the maximum likelihood method does not admit explicit solutions. In this case, we propose some explicit estimators of the location parameter in the half-triangle distribution by the approximate maximum likelihood methods. 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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• 제18권3호
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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 Electrical Engineering and Technology
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• 제12권3호
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• pp.1016-1026
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• 2017

An adaptive extended Kalman filter based on the maximum likelihood (EKF-ML) is proposed for detecting voltage sag in this paper. Considering that the choice of the process and measurement error covariance matrices affects seriously the performance of the extended Kalman filter (EKF), the EKF-ML method uses the maximum likelihood method to adaptively optimize the error covariance matrices and the initial conditions. This can ensure that the EKF has better accuracy and faster convergence for estimating the voltage amplitude (states). Moreover, without more complexity, the EKF-ML algorithm is almost as simple as the conventional EKF, but it has better anti-disturbance performance and more accuracy in detection of the voltage sag. More importantly, the EKF-ML algorithm is capable of accurately estimating the noise parameters and is robust against various noise levels. Simulation results show that the proposed method performs with a fast dynamic and tracking response, when voltage signals contain harmonics or a pulse and are jointly embedded in an unknown measurement noise.

• 한국통신학회논문지
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• 제32권7C호
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• pp.616-626
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• 2007

무선통신 채널에서 높은 전송 속도를 가능하게 하는 공간다중화 MIMO 시스템 수신부에서 다중화된 신호를 검출하는 것은 어려운 작업이며, 최근 다양한 신호검출 기법들이 개발되어졌다. 다양한 신호검출 기법 중 maximum likelihood detection with QR decomposition and M-algorithm (QRM-MLD), sphere decoding (SD)과 같은 기존 기법들은 maximum likelihood (ML)기법과 유사한 성능을 가진 것으로 보고되었다. 본 논문에서는 ML 기법과 거의 동일한 성능을 가지면서 낮은 연산복잡도를 보이는 새로운 신호검출 기법을 제안한다. 모의실험을 통하여 제안된 기법은 ML 기법과 거의 동일한 성능을 보이면서 MMSE-OSIC와 유사한 연산복잡도를 가지는 것을 보인다. 또한 기존의 QRM-MLD, SD 기법들의 경우 hard decision 후 추가적인 연산을 통해 soft decision을 위한 log likelihood ratio(LLR) 값을 생성하는 반면, 제안된 기법에서는 추가적인 연산 없이 LLR 값을 성공적으로 생성할 수 있음을 보인다.

• 한국통신학회논문지
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• 제40권6호
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• pp.981-989
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• 2015

최적 ML(Maximum Likelihood) 기법 및 sphere decoding(SD), QRM-MLD(QR decomposition with M-algorithm Maximum Likelihood Detection) 기반의 준 최적 검출 기법을 적용한 MIMO(Multiple-Input Multiple-Output) 시스템에서의 LLR(Log Likelihood Ratio) 계산은 변조 차수 및 송/수신 안테나의 수가 증가할수록 그 복잡도가 지수적으로 증가하여 구현 및 성능 면에서 큰 문제점을 야기한다. 본 논문에서는 고차 변조 방식 기반의 $N_T{\times}N_R$ MIMO시스템 수신기의 QRM-MLD 기반 MIMO 검출기에서 연판정 시 아주 낮은 복잡도로 1dB 이내의 ML 검출 기법에 대한 오류 성능 접근도를 갖는 LLR 계산 방법을 제시하고, 컴퓨터 시뮬레이션을 통해 여러 M 값에 대한 MIMO 시스템의 BER(Bit Error Rate) 결과를 도출하고 분석하여 제시된 방법의 유효성을 검증한다.

• 음성과학
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• 제11권4호
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• pp.43-52
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• 2004

Model parameters in HMM based speech recognition systems are normally estimated using Maximum Likelihood Estimation(MLE). The MLE method is based mainly on the principle of statistical data fitting in terms of increasing the HMM likelihood. The optimality of this training criterion is conditioned on the availability of infinite amount of training data and the correct choice of model. However, in practice, neither of these conditions is satisfied. In this paper, we propose a training algorithm, MCE(Minimum Classification Error), to improve the performance of a speech recognizer detecting mispronunciation of a foreign language. During the conventional MLE(Maximum Likelihood Estimation) training, the model parameters are adjusted to increase the likelihood of the word strings corresponding to the training utterances without taking account of the probability of other possible word strings. In contrast to MLE, the MCE training scheme takes account of possible competing word hypotheses and tries to reduce the probability of incorrect hypotheses. The discriminant training method using MCE shows better recognition results than the MLE method does.

• Communications for Statistical Applications and Methods
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• 제28권6호
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• pp.627-641
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• 2021

An asymmetric least squares estimation method has been employed to estimate linear models for percentile regression. An asymmetric maximum likelihood estimation (AMLE) has been developed for the estimation of Poisson percentile linear models. In this study, we propose generalized nonlinear percentile regression using the AMLE, and the use of the parametric bootstrap method to obtain confidence intervals for the estimates of parameters of interest and smoothing functions of estimates. We consider three conditional distributions of response variables given covariates such as normal, exponential, and Poisson for three mean functions with one linear and two nonlinear models in the simulation studies. The proposed method provides reasonable estimates and confidence interval estimates of parameters, and comparable Monte Carlo asymptotic performance along with the sample size and quantiles. We illustrate applications of the proposed method using real-life data from chemical and radiation epidemiological studies.