• Journal of the Korean Statistical Society
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• v.23 no.1
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• pp.33-38
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• 1994

The OLS-estimator of the distribance variance in the linear regression model is shown to be asymptotically unbiased when the disturbances are MA(1)-process or particular s-th autocorrelated AR(s)-process.

• Journal of the Korean Statistical Society
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• v.23 no.1
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• pp.115-134
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• 1994

Polynomial errors-in-variables model with one predictor variable and one response variable is defined and an estimator of model is derived following the Booth's linear model estimation procedure. Since polynomial model is nonlinear function of the unknown regression coefficients and error-free predictors, it is nonlinear model in errors-in-variables model. As a result of applying linear model estimation method to nonlinear model, some additional assumptions are necessary. Hence, an estimator is derived under the assumption that the error variances are decrasing as sample size increases. Asymptotic propoerties of the derived estimator are provided. A simulation study is presented to compare the small sample properties of the derived estimator with those of OLS estimator.

• Communications for Statistical Applications and Methods
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• v.24 no.6
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• pp.673-683
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• 2017

The least absolute shrinkage and selection operator (LASSO) has been a popular regression estimator with simultaneous variable selection. However, LASSO does not have the oracle property and its robust version is needed in the case of heavy-tailed errors or serious outliers. We propose a robust penalized regression estimator which provide a simultaneous variable selection and estimator. It is based on the rank regression and the non-convex penalty function, the smoothly clipped absolute deviation (SCAD) function which has the oracle property. The proposed method combines the robustness of the rank regression and the oracle property of the SCAD penalty. We develop an efficient algorithm to compute the proposed estimator that includes a SCAD estimate based on the local linear approximation and the tuning parameter of the penalty function. Our estimate can be obtained by the least absolute deviation method. We used an optimal tuning parameter based on the Bayesian information criterion and the cross validation method. Numerical simulation shows that the proposed estimator is robust and effective to analyze contaminated data.

• Communications for Statistical Applications and Methods
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• v.28 no.4
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• pp.315-327
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• 2021

The Spatial Autoregressive (SAR) models have drawn considerable attention in recent econometrics literature because of their capability to model the spatial spill overs in a feasible way. While considering the Bayesian analysis of these models, one may face the problem of lack of robustness with respect to underlying prior assumptions. The generalized Bayes estimators provide a viable alternative to incorporate prior belief and are more robust with respect to underlying prior assumptions. The present paper considers the SAR model with a set of linear restrictions binding the regression coefficients and derives restricted generalized Bayes estimator for the coefficients vector. The minimaxity of the restricted generalized Bayes estimator has been established. Using a simulation study, it has been demonstrated that the estimator dominates the restricted least squares as well as restricted Stein rule estimators.

• 제어로봇시스템학회:학술대회논문집
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• 1988.10b
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• pp.1041-1044
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• 1988

This paper considers the design problem of adaptive filters based an the state-space models for linear discrete-time stationary stochastic signal processes. The adaptive state estimator consists of both the predictor and the sequential prediction error estimator. The discrete Chandrasakhar filter developed by author is employed as the predictor and the nonlinear least-squares estimator is used as the sequential prediction error estimator. Two models are presented for calculating the parameter sensitivity functions in the adaptive filter. One is the exact model called the linear innovations model and the other is the simplified model obtained by neglecting the sensitivities of the Chandrasekhar X and Y functions with respect to the unknown parameters in the exact model.

• Journal of Korean Society for Quality Management
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• v.23 no.3
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• pp.113-125
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• 1995

This paper describes the 2-stage randomized response model in the Bayesian view point. The classical Bayesian analysis needs the complete information for a prior density, but the Bayes linear estimator needs only the first and second moments. Therefore, it is convenient to find the estimator and this estimator robusts to a prior density. We show that MSE's of the Bayes linear estimators for the 2-stage randomized response models are smaller than those of the MLE's for the 2-stage randomized response models.

• Journal of international Conference on Electrical Machines and Systems
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• v.3 no.3
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• pp.252-255
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• 2014

In the case of long-stator linear drives, unlike rotative drives for which speed or position sensors are a single unit attached to the shaft, these sensors extend along the guideway. The position signal transmitted from maglev vehicle can't meet the need of the real-time propulsion control. In this paper the position estimator for propulsion inverter driving long stator linear synchronous motor (LSLSM) in high speed maglev train is proposed. In order to get the higher resolution of the position information transmitted from vehicle, Full order state observer is proposed for position estimator.

• Communications for Statistical Applications and Methods
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• v.4 no.2
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• pp.353-358
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• 1997

When the disturbances in the linear repression medel are generated by a seasonal autoregressive scheme the Cochrane Orcutt transformation estimator (COTE) is a well known alternative to Generalized Least Squares estimator (GLSE). In this paper it is analyzed in which situation the Ordinary Least Squares estimator (OLSE) is always better than COTE for positive autocorrelation in terms of efficiency which is here defined as the ratio of the total variances.

• Communications for Statistical Applications and Methods
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• v.4 no.1
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• pp.111-117
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• 1997

The ordinary least squares estimator $S^2$ for the variance of the disturbances is considered in the linear regression model with sutocorrelated disturbances. It is proved that the OLS-estimator of disturbance variance is asymptotically unbiased and weakly consistent, when the distrubances are generated by an MA(q) process. In particular, the asymptotic unbiasedness and consistency of $S^2$ is satisfied without any restriction on the regressor matrix.

• Journal of the Korean Statistical Society
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• v.30 no.4
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• pp.637-644
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• 2001

We investigated two kinds of mean square error (MSE) estimator of homogeneous linear estimator (HLE) for the population total under stratified multi-stage sampling. One is studied when the second stage variance component is estimable and the other is found in cafe it is not estimable. The proposed estimators are necessary forms of non-negative unbiased MSE estimators of HLE.