• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.16 no.1
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• pp.1-13
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• 2012

In this paper we present an a posteriori error estimator for the stabilized P1 nonconforming finite element method of the linear elasticity problem based on a nonsymmetric H(div)-conforming approximation of the stress tensor in the first-order Raviart-Thomas space. By combining the equilibrated residual method and the hypercircle method, it is shown that the error estimator gives a fully computable upper bound on the actual error. Numerical results are provided to confirm the theory and illustrate the effectiveness of our error estimator.

• Journal of Korean Society for Quality Management
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• v.27 no.2
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• pp.152-162
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• 1999

In this paper we develope a new nonparametric estimator of the reliability in strength-stress model. This estimator is constructed using the maximum likelihood estimate of cumulative failure rate in the class of distributions which have piecewise linear failure rate functions between each pair of observations. Large sample properties of our estimator are examined. The proposed estimator is compared with previously known estimator by Monte Carlo study.

• Journal of the Korean Statistical Society
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• v.9 no.2
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• pp.181-193
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• 1980

A class of nonparametric estimators of the ratio of scale parameters is proposed. The estimators are based on the distribution-free rank-like test suggested by Fligner and Killeen (1976). An explicit form of the estimator is the median of the ratios of absolute deviations from the combined sample median. A small-sample Monte Carlo study shows that the proposed estimator is more efficient than the Bhattacharyya (1977) estimator. The proposed estimator is is reasonably insensitive to small failures in the assumption of equal medians. A modified estimator is also considered when the meidans are unequal.

• Journal of the Korean Data and Information Science Society
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• v.16 no.4
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• pp.959-968
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• 2005

The piecewise linear histogram can be used as a simple and efficient tool for the density estimator. But, this piecewise linear histogram is discontinuous function. We suppose the piecewise continuous linear histogram as a simple and efficient tool for the density estimator and the alternative of the piecewise linear histogram.

• Communications for Statistical Applications and Methods
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• v.8 no.1
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• pp.15-27
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• 2001

One proposal is made for constructing nonparametric estimator of slope parameters in a regression model under symmetric error distributions. This estimator is based on the use of idea of Johns for estimating the center of the symmetric distribution together with the idea of regression quantiles and regression trimmed mean. This nonparametric estimator and some other L-estimators are studied by Monte Carlo.

• Communications for Statistical Applications and Methods
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• v.2 no.1
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• pp.1-12
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• 1995

An estimator of coefficients of polynomial measurement error model with vector predictor and first-order interaction terms is derived using Hermite polynomial. Asymptotic normality of estimator is provided and some simulation study is performed to compare the small sample properties of derived estimator with those of OLS estimator.

• Journal of the Korean Operations Research and Management Science Society
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• v.16 no.1
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• pp.59-67
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• 1991

The hazard estimator is proposed for estimating system failure probability of a general network where all minimal cut sets are given. Theoretical variance of the hazard estimator is derived in a bridge system. It is demonstrated that variance of the hazard estimator is much smaller than that of the raw simulation estimator particularly for small arc failure probability.

• Survey Research
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• v.12 no.3
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• pp.49-62
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• 2011

In this paper we investigate design-based properties of both the ordinary least square estimator and the weighted least square estimator for regression coefficients in panel regression model. We derive formulas of approximate bias, variance and mean square error for the ordinary least square estimator and approximate variance for the weighted least square estimator after linearization of least square estimators. Also we compare their magnitudes each other numerically through a simulation study. We consider a three years data of Korean Welfare Panel Study as a finite population and take household income as a dependent variable and choose 7 exploratory variables related household as independent variables in panel regression model. Then we calculate approximate bias, variance, mean square error for the ordinary least square estimator and approximate variance for the weighted least square estimator based on several sample sizes from 50 to 1,000 by 50. Through the simulation study we found some tendencies as follows. First, the mean square error of the ordinary least square estimator is getting larger than the variance of the weighted least square estimator as sample sizes increase. Next, the magnitude of mean square error of the ordinary least square estimator is depending on the magnitude of the bias of the estimator, which is large when the bias is large. Finally, with regard to approximate variance, variances of the ordinary least square estimator are smaller than those of the weighted least square estimator in many cases in the simulation.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.6
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• pp.177-186
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• 2005

This paper introduces nonlinear estimators with reduced complexity, and proposes the Bussgang blind equalization algorithm employing the nonlinear estimators. The proposed algorithm utilized the facts that the Bayesian estimator is well approximated to the sigmoid estimator in initial stage of equalization with closed eye and is well approximated to the threshold estimator under open eye condition. The proposed method adopts selectively one of the two nonlinear estimators, i.e., the sigmoid estimator and the threshold estimator, according to channel distortion level at each iteration. As a result, by using the sigmoid estimator with reduced constellation, the proposed scheme, as it is applied to blind equalization of high-order QAM signals, simplifies the computational complexity extremely, and enhances the blind convergence capability and steady-state performance.

• The Transactions of the Korean Institute of Power Electronics
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• v.22 no.3
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• pp.216-222
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• 2017

The rotor position of a PM synchronous motor is commonly estimated from the mathematical model for the sensorless control without rotor position sensors. For the magnet flux-based rotor position estimator in the stationary reference frame, the magnet flux estimator for estimating rotor position and speed includes the integrator. The integrator in the magnet flux estimator may accumulate the offset of the current sensors and the voltage drift. This continuous accumulation of the offset may cause the drift and overflow in the integrator, such that the estimated rotor position and speed may fail to track the real rotor position and speed. In this paper, the magnet flux estimator without integrator is proposed to avoid overflow in the integrator. The proposed rotor position and speed estimator based on magnet flux estimator are verified through simulation and experiment.