• Journal of the Korean Statistical Society
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• v.19 no.1
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• pp.80-87
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• 1990

In reliability and life testing the mean residual life (MRL) of an item plays a significant role. While there has been a great deal of discussion on the theoretical aspects of the MRL, good estimators of MRL have been difficult to obtain. In this paper we propose a new estimator of the MRL of items at a given age, which is especially good for a small sample. The new estimator compares favorably with the empirical MRL estimator for small samples.

• Communications for Statistical Applications and Methods
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• v.1 no.1
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• pp.57-74
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• 1994

This paper deals with the estimation problems of a common mean of several independent normal populations with unknown variances, based on random samples of equal size. The authors suggest a promising approach and a new estimator to improve Graybill-Deal estimator further. By Monte Carlo simulation study, the efficiency of new estimator is compared with that of Graybill-Deal estimator.

• Journal of the Korean Data and Information Science Society
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• v.22 no.6
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• pp.1251-1256
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• 2011

Consider a p-variate ($p{\geq}4$) normal distribution with mean ${\theta}$ and identity covariance matrix. Using a simple property of noncentral chi square distribution, the generalized Bayes estimators dominating the Lindley estimator under quadratic loss are given based on the methods of Brown, Brewster and Zidek for estimating a normal variance. This result can be extended the cases where covariance matrix is completely unknown or ${\Sigma}={\sigma}^2I$ for an unknown scalar ${\sigma}^2$.

• Communications for Statistical Applications and Methods
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• v.12 no.2
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• pp.463-472
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• 2005

In this paper, the problem of estimating a p-variate (p $\ge$4) normal mean vector is considered in decision-theoretic set up. Using a simple property of the noncentral chi-square distribution, a sequence of estimators dominating the Lindley type estimator with the cases of unknown covariance matrices has been produced and each improved estimator is better than previous one.

• Communications for Statistical Applications and Methods
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• v.3 no.2
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• pp.129-138
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• 1996

We propose a smoothing estimator of mean residual life function based on Ghorai and Susarla's (1990) smooth estimator of distribution function under random censorship model and provide the asymptotic properties of this estimator. The Monte Carlo simulation is performed to compare the proposed estimator with the other estimators and an exmple is also given using the real data.

• Communications for Statistical Applications and Methods
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• v.17 no.3
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• pp.451-457
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• 2010

This paper considers the problem of nonparametric deconvolution density estimation when sample observa-tions are contaminated by double exponentially distributed errors. Three different deconvolution density estima-tors are introduced: a weighted kernel density estimator, a kernel density estimator based on the support vector regression method in a RKHS, and a classical kernel density estimator. The performance of these deconvolution density estimators is compared by means of a simulation study.

• Journal of Korean Society for Quality Management
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• v.26 no.1
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• pp.161-171
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• 1998

Kim and Kim (1997) developed a method of estimating the mean lifetime based on the augmented data after imputing censored observations. Assuming the linear relationship between lifetime and covariates, and then introducing the procedure of Buckley and James (1979) to estimate the mean lifetimes of censored observations, they proposed a mean lifetime estimator and its consistency under the regularity conditions. In this article, the Kim and Kim's estimator is compared with the estimator introduced by Gill (1983) through simulations under the various configurations. Also, their estimator is illustrated with two real data sets.

• Journal of Korean Society for Quality Management
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• v.12 no.1
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• pp.9-16
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• 1984

We study some estimation of the ${\theta}=P_r$(Y${\theta}$. We consider asymptotic property of estimators and maximum likelihood estimator is compared with unique minimum veriance unbiased estimator in moderate sample size.

• Communications for Statistical Applications and Methods
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• v.19 no.1
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• pp.13-22
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• 2012

Cut-off sampling that discards a part of the population from the sampling frame, is a widely used method for a business survey. Usually, to the estimate of population total, an accurate estimate of the total of the take-nothing stratum is required. Many estimators have been developed to estimate the total of the take-nothing stratum. Recently Kim and Shin (2011) suggested a composite estimator and showed the superiority of that estimator. In this paper, we suggest an alternative composite estimator obtained by combining BLUP estimator and a ratio estimator obtained by the small samples from the take-nothing stratum. Small simulation studies are performed for a comparison of the estimators and we confirm that the new suggested estimator is superior.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.5
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• pp.415-419
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• 2008

Detecting and correcting defects on LCD glasses early in the manufacturing process becomes important for panel makers to reduce the manufacturing costs and to improve productivity. Many attempts have been made and were successfully applied to detect and identify simple defects such as scratches, dents, and foreign objects on glasses. However, it is still difficult to robustly detect low-contrast defect region, called Mura or blemish area on glasses. Typically, these defect areas are roughly defined as relatively large, several millimeters of diameter, and relatively dark and/or bright region of low Signal-to-Noise Ratio (SNR) against background of low-frequency signal. The aim of this article is to present a robust algorithm to segment these blemish defects. Early 90's, a highly robust estimator, known as the Model-Fitting (MF) estimator was developed by X. Zhuang et. al. and have been successfully used in many computer vision application. Compared to the conventional Least-Square (LS) estimator the MF estimator can successfully estimate model parameters from a dataset of contaminated Gaussian mixture. Such a noise model is defined as a regular white Gaussian noise model with probability $1-\varepsilon$ plus an outlier process with probability $varepsilon$. In the sense of robust estimation, the blemish defect in images can be considered as being a group of outliers in the process of estimating image background model parameters. The algorithm developed in this paper uses a modified MF estimator to robustly estimate the background model and as a by-product to segment the blemish defects, the outliers.