• Journal of Korean Society of Industrial and Systems Engineering
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• v.32 no.3
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• pp.99-109
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• 2009

Negative binomial yield model for semiconductor manufacturing consists of two parameters which are the average number of defects per die and the clustering parameter. Estimating the clustering parameter is quite complex because the parameter has not clear closed form. In this paper, a Bayesian approach using Markov Chain Monte Carlo is proposed to estimate the clustering parameter. To find an appropriate estimation method for the clustering parameter, two typical estimators, the method of moments estimator and the maximum likelihood estimator, and the proposed Bayesian estimator are compared with respect to the mean absolute deviation between the real yield and the estimated yield. Experimental results show that both the proposed Bayesian estimator and the maximum likelihood estimator have excellent performance and the choice of method depends on the purpose of use.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.5A
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• pp.484-489
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• 2007

The main disadvantage of orthogonal frequency division multiplexing (OFDM) systems is its sensitivity to carrier frequency offset and timing offset. This paper proposes a simple way of improving the performance of the integer frequency offset (IFO) estimator in OFDM-based digital video broadcasting (DVB) system. By modifying the conventional maximum likelihood (ML) estimator to have multi-stage estimation strategy, IFO estimator is derived. Simulations indicate that the proposed IFO estimator works robustly with reduced computational burden when compared to ML estimator.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.6
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• pp.93-99
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• 2007

Robust nonlinear image denoising algorithms for the class of ${\alpha}$-stable distribution are introduced. The proposed amplitude-limited sample average filter(ALSAF) proves to be the maximum likelihood estimator under the heavy-tailed Gaussian noise environments. The error norm for this estimator is equivalent to Huber#s minimax norm. It is optimal in the respect of maximizing the efficacy under the above noise environment. It is mired with the myriad filter to propose an amplitude-limited myriad filter(ALMF). The behavior and performance of the ALSAF and ALMF in ${\alpha}$-stable noise environment are illustrated and analyzed through simulation.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38C no.4
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• pp.365-370
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• 2013

In this paper, we propose robust blind estimators for the frequency offset of orthogonal frequency division multiplexing in non-Gaussian noise environments. We first propose a maximum likelihood (ML) estimator in non-Gaussian noise modeled as a complex isotropic Cauchy process, and then, a simpler estimator based on the ML estimator is proposed. From numerical results, we confirm that the proposed estimators are robust to the non-Gaussian noise and have a better estimation performance over the conventional estimator in non-Gaussian noise environments.

• Journal of the Korean Statistical Society
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• v.21 no.2
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• pp.187-200
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• 1992

Samples are often found to be too heterogeneous to be explained by a one-parameter family of models in the sense that the implicit mean-variance relationship in such a family is violated by the data. This phenomenon is often called over-dispersion. The most frequently used method in dealing with over-dispersion is to mix a one-parameter family creating a two parameter marginal mixture family for the data. In this paper, we investigate performance of estimators such as maximum likelihood estimator, method of moment estimator, and maximum quasi-likelihood estimator in negative binomial and beta-binomial distribution. Simulations are done for various mean parameter and dispersion parameter in both distributions, and we conclude that the moment estimators are very superior in the sense of bias and asymptotic relative efficiency.

• Journal of the Korean Data and Information Science Society
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• v.15 no.3
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• pp.655-662
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• 2004

In this paper, we assume that strengths of two components system follow a type II bivariate Pareto model with bivariate type I censored data. And these two components are subjected to a common stress which is independent of the strengths of the components. We obtain estimators for the system reliability based on likelihood function and relative frequency, respectively. Also we construct approximated confidence intervals for the reliability based on maximum likelihood estimator and relative frequency estimator, respectively. Finally we present a numerical study.

• Communications for Statistical Applications and Methods
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• v.16 no.2
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• pp.229-238
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• 2009

We propose a simple algorithm for obtaining MTL(maximum trimmed likelihood) estimates. The algorithm finds the subset to use to obtain the global maximum in the series of eliminating process which depends on the likelihood of cells in a contingency table. To evaluate the performance of the algorithm for MTL estimators, we conducted simulation studies. The results showed that the algorithm is very competitive in terms of computational burdens required to get the same or the similar results in comparison with the complete enumeration.

• Communications for Statistical Applications and Methods
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• v.6 no.1
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• pp.305-318
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• 1999

In this paper we derive the higher order expansions of the first four cumulants of multi-dimensional Maximum Likelihood Estimator (MLE) under the general parametric model up to and including terms of order O({{{{ {n }^{-1 } }}}}) Also we obtain the explicit form of the expansion of the normalizing trans formation of multi-dimensional MLE and show that the suggested normalizing process is much better than the normal approximation based on central limit theorem through example.

• Journal of the Korean Statistical Society
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• v.31 no.3
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• pp.329-342
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• 2002

We deal with asymptotic properties of Maximum Likelihood Estimator(MLE) for the parameters appearing in a Hilbert space-valued Stochastic Differential Equation(SDE) and a Stochastic Partial Differential Equation(SPDE). In paractice, the available data are only the finite dimensional projections to the solution of the equation. Using these data we obtain MLE and consider the asymptotic properties as the dimension of projections increases. In particular we explore a relationship between the conditions for the solution and asymptotic properties of MLE.

• Journal of the Korean Data and Information Science Society
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• v.9 no.2
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• pp.203-209
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• 1998

By assuming a progressively censored sample, we propose the approximate maximum likelihood estimator (AMLE) of the location nd the scale parameters of the two-parameter normal distribution and obtain the asymptotic variances and covariance of the AMLEs. An example is given to illustrate the methods of estimation discussed in this paper.