• Journal of the Korean Statistical Society
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• v.29 no.1
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• pp.9-16
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• 2000

This article addresses the problem of maximum likelihood estimation in ARCH regression with lagged dependent variables. Some topics in asymptotics of the model such as uniform expansion of likelihood function and construction of a class of MLE are discussed, and the regularity property of MLE is obtained. The error process here is possibly non-Gaussian.

• Communications for Statistical Applications and Methods
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• v.27 no.4
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• pp.469-486
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• 2020

Entropy is an important term in statistical mechanics that was originally defined in the second law of thermodynamics. In this paper, we consider the maximum likelihood estimation (MLE), maximum product spacings estimation (MPSE) and Bayesian estimation of the entropy of an inverse Weibull distribution (InW) under a generalized type I progressive hybrid censoring scheme (GePH). The MLE and MPSE of the entropy cannot be obtained in closed form; therefore, we propose using the Newton-Raphson algorithm to solve it. Further, the Bayesian estimators for the entropy of InW based on squared error loss function (SqL), precautionary loss function (PrL), general entropy loss function (GeL) and linex loss function (LiL) are derived. In addition, we derive the Lindley's approximate method (LiA) of the Bayesian estimates. Monte Carlo simulations are conducted to compare the results among MLE, MPSE, and Bayesian estimators. A real data set based on the GePH is also analyzed for illustrative purposes.

• Journal of the Korean Institute of Telematics and Electronics
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• v.26 no.9
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• pp.1436-1443
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• 1989

In this paper, we propose a maximum-likelihood estimation(MLE) method to obtain the location and the amplitude of the pulses in MPE( multi-pulse excitation)-LPC speech synthesis using multi-pulses as excitation source. This MLE method computes the value maximizing the likelihood function with respect to unknown parameters(amplitude and position of the pulses) for the observed data sequence. Thus in the case of overlapped pulses, the method is equivalent to Ozawa's crosscorrelation method, resulting in equal amount of computation and sound quality with the cross-correlation method. We show by computer simulation: the multi-pulses obtained by MLE method are(1) pseudo-periodic in pitch in the case of voicde sound, (2) the pulses are random for unvoiced sound, (3) the pulses change from random to periodic in the interval where the original speech signal changes from unvoiced to voiced. Short time power specta of original speech and syunthesized speech obtained by using multi-pulses as excitation source are quite similar to each other at the formants.

• The Journal of the Acoustical Society of Korea
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• v.41 no.2
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• pp.143-149
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• 2022

In this paper, we evaluate the underwater frequency diversity communication performance of Selective Combination (SC) using Maximum Likelihood Estimation (MLE). In an underwater multipath frequency selective channel, destructive interference fading due to delay spread of a received signal affects the increase in error and Signal to Noise Ratio (SNR) variability of an underwater acoustic communication. Selective Combination frequency diversity using a single sensor is applied as a transmission performance improvement technique according to the frequency selectivity of a channel. In the sea experiment applying MLE for SC decision value extraction, we evaluate the performance of SC frequency diversity and MLE-SC frequency diversity. In experiment result, we confirm through experiment that the Bit Error Rate (BER) is relatively lower when the decision value extracted through MLE-SC is applied than when the SC decision value is fixed.

• Speech Sciences
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• v.11 no.4
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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.

• The Transactions of the Korea Information Processing Society
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• v.1 no.4
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• pp.511-516
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• 1994

Parameter estimation techniques are discussed for the complex frequency analysis of an electromagnetic scatterer. The paper suggests how the Maximum Likelihood estimation technique can be applied for this purpose. Experiments on hypothetical data sets demonstrate that the Maximum Likelihood technique is better than the Pencil of Functions technique. Although there have been several techniques including MLE suggested as tools of the parameter estimation, the proposed method has strong advantages under the noise-contaminated sample data environment because it uses minimal dimension of system matrix that stands totally independent of the length of extracted data set.

• Communications for Statistical Applications and Methods
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• v.16 no.6
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• pp.1055-1066
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• 2009

Many articles have considered a hybrid censoring scheme, which is a mixture of Type-I and Type-II censoring schemes. We introduce a double hybrid censoring scheme and derive some approximate maximum likelihood estimators(AMLEs) of the scale parameter for the half logistic distribution under the proposed double hybrid censored samples. The scale parameter is estimated by approximate maximum likelihood estimation method using two different Taylor series expansion types. We also obtain the maximum likelihood estimator(MLE) and the least square estimator(LSE) of the scale parameter under the proposed double hybrid censored samples. We compare the proposed estimators in the sense of the mean squared error. The simulation procedure is repeated 10,000 times for the sample size n = 20(10)40 and various censored samples. The performances of the AMLEs and MLE are very similar in all aspects but the MLE and LSE have not a closed-form expression, some numerical method must be employed.

• Journal of Korean Institute of Industrial Engineers
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• v.42 no.4
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• pp.256-262
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• 2016

The Weibull is widely used in reliability analysis, and several studies have attempted to improve estimation of the distribution's parameters. least squares estimation (LSE) or Maximum likelihood estimation (MLE) are often used to estimate distribution parameters. However, it has been proven that Bayesian methods are more suitable for small sample sizes than LSE and MLE. In this work, the Weibull parameter estimation accuracy of LSE, MLE, and Bayesian method are compared for sample sets with 3 to 30 data points. The Bayesian method was most accurate for sample sizes under 25, and the accuracy of the Bayesian method was similar to LSE and MLE as the sample size increased.

• The Korean Journal of Applied Statistics
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• v.4 no.1
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• pp.13-24
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• 1991

This paper presents a Monte Carlo evaluation of estimators for nonlinear consored simultaneous equations models. We examine the performance of the maximum likelihood estimator (MLE), the two-step quasi maximum likelihood estimator (2QMLE) proposed by Lee and Hurd (1989), and another quasi MLe using least squares at the first step (LSAE) under varying degrees of freedom and underlying distributions, Although QMLE's are not necessarily consistent, the Monte Carlo results show that the 2QMLE may be used as an alternative to MLE when MLE is not applicable in practice.

• Korean Journal of Remote Sensing
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• v.20 no.5
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• pp.315-327
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• 2004

Nowadays, interests in land cover classification using not only multi-sensor images but also thematic GIS information are increasing. Often, although useful GIS information for the classification is available, the traditional MLE (maximum likelihood estimation techniques) does not allow us to use the information, due to the fact that it cannot handle the GIS data properly. This paper propose two extended MLE algorithms that can integrate both remote sensing images and GIS thematic data for land-cover classification. They include modified MLE and Bayesian predictive likelihood estimation technique (BPLE) techniques that can handle both categorical GIS thematic data and remote sensing images in an integrated manner. The proposed algorithms were evaluated through supervised land-cover classification with Landsat ETM+ images and an existing land-use map in the Gongju area, Korea. As a result, the proposed method showed considerable improvements in classification accuracy, when compared with other multi-spectral classification techniques. The integration of remote sensing images and the land-use map showed that overall accuracy indicated an improvement in classification accuracy of 10.8% when using MLE, and 9.6% for the BPLE. The case study also showed that the proposed algorithms enable the extraction of the area with land-cover change. In conclusion, land cover classification results produced through the integration of various GIS spatial data and multi-spectral images, will be useful to involve complementary data to make more accurate decisions.