• Journal of the Korean Statistical Society
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• v.28 no.1
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• pp.93-106
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• 1999

In this paper we consider estimation of a real valued parameter in the drift coefficient of a Hilbert space valued Ito stochastic differential equation. First we consider observation of the corresponding diffusion in a fixed time interval [0, T] and prove the Bernstein - von Mises theorem concerning the convergence of posterior distribution of the parameter given the observation, suitably normalised and centered at the MLE, to the normal distribution as Tlongrightarrow$\infty$. As a consequence, the Bayes estimator of the drift parameter becomes asymptotically efficient and asymptotically equivalent to the MLE as Tlongrightarrow$\infty$. Next, we consider observation in a random time interval where the random time is determined by a predetermined level of precision. We show that the sequential MLE is better than the ordinary MLE in the sense that the former is unbiased, uniformly normally distributed and efficient but is latter is not so.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.2
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• pp.307-309
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• 2015

In this letter, a DFT-based channel estimator is proposed for the uplink of LTE-A systems to solve the leakage and enhance the spectral efficiency. It is confirmed that the proposed estimator can significantly improve user and cell spectral efficiencies compared to conventional estimators.

• Communications for Statistical Applications and Methods
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• v.9 no.1
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• pp.291-304
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• 2002

This article is concerned with an effective algorithm for the identification of multiple outliers in linear regression. It proposes a hybrid algorithm which employs the least median of squares estimator, instead of the least squares estimator, to construct an Initial clean subset in the stepwise forward search scheme. The performance of the proposed algorithm is evaluated and compared with the existing competitor via an extensive Monte Carlo simulation. The algorithm appears to be superior to the competitor for the most of scenarios explored in the simulation study. Particularly it copes with the masking problem quite well. In addition, the orthogonal decomposition and Its updating techniques are considered to improve the computational efficiency and numerical stability of the algorithm.

• Proceedings of the IEEK Conference
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• 2005.11a
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• pp.77-80
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• 2005

In this paper, we introduce the efficient carrier phase estimating algorithm collaborate with the channel decoder of turbo coded QPSK modulation for mobile DVB-RCS systems. At low SNR, the phase estimation using soft information of turbo decoder is able to improve power efficiency because of achieving the good synchronization. We investigate performance of external single estimator and internal multiple estimator in the PSP (Per Survivor Processing) manner over AWGN channel. For phase estimation, the LMS (Least Mean Square) scheme is considered. Three different APP-based methods are also proposed.

• Journal of the Korean Data and Information Science Society
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• v.20 no.2
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• pp.349-356
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• 2009

In this study, we suggest the estimation method of the relative risk for the unemployment statistics of a small area such as si, gun, gu in Korea. The considered method are the usual pooled estimator, weighted estimator with the inverse of log-variance as weights, and the Jackknife estimator. And we compare with the efficiency of the three estimators by estimating the bias and mean square errors using real data from the 2002 Economically Active Population Survey of Gyeonggi-do. We compute the unemployed rate of male and female in small areas, and then estimate the common relative risk for the unemployed rate between male and female. Also, the stability and reliability of the three estimators for the common relative risk was evaluated using the RB(relative bias) and the RRMSE(relative root mean square error) of these estimators. Finally, the Jackknife estimator turned out to be much more efficient than the other estimators.

• The Korean Journal of Applied Statistics
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• v.17 no.2
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• pp.303-310
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• 2004

We suggest a method to procure information from the sensitive population which combine a direct survey method, BB and an indirect survey one, RRT, and a combined estimator that uses the stratified double sampling to estimate the sensitive parameter. We compare the efficiency of our estimator with that of Mangat and Singh model.

• The Korean Journal of Applied Statistics
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• v.27 no.3
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• pp.397-406
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• 2014

The minimum density power divergence estimation has been a popular topic in the field of robust estimation for since Basu et al. (1988). The minimum density power divergence estimator has strong robustness properties with the little loss in asymptotic efficiency relative to the maximum likelihood estimator under model conditions. However, a limitation in applying this estimation method is the algebraic difficulty on an integral involved in an estimation function. This paper considers a minimum density power divergence estimation method with approximated divergence avoiding such difficulty. As an example, we consider the normal-exponential convolution model introduced by Bolstad (2004). The estimated divergence in this case is too complicated; consequently, a Laplace approximation is employed to obtain a manageable form. Simulations and an empirical study show that the minimum density power divergence estimators based on an approximated estimated divergence for the normal-exponential model perform adequately in terms of bias and efficiency.

• Journal of the Korean Data and Information Science Society
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• v.13 no.2
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• pp.365-379
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• 2002

We apply the techniques of interpolation and extrapolation to derive a new estimator based on centered modified systematic sampling for the mean of a population which has a linear trend. The efficiency of the proposed estimation method is compared with that of various existing methods. An illustrative numerical example is given.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.24 no.67
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• pp.11-18
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• 2001

The efficiency and robustness of the scale estimator based on the Gini's mean difference are well known in Nam et al.(2000). In this paper we propose use of robust control limits based on the Gini's mean difference for the control of the process deviation. To compare the performances of the proposed control chart with the existing R-chart or S-chart, some Monte Carlo simulations are performed. The simulation results show that the use of the Gini's mean difference in construction of the control limits has good performance.

• Journal of the Korean Statistical Society
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• v.30 no.2
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• pp.265-280
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• 2001

Estimation of the edge of a distribution has many important applications. It is related to classification, cluster analysis, neural network, and statistical image recovering. The problem also arises in measuring production efficiency in economic systems. Three most promising nonparametric estimators in the existing literature are introduced. Their statistical properties are provided, some of which are new. Themes of future study are also discussed.