• 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.

• The Journal of the Acoustical Society of Korea
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• v.15 no.4
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• pp.93-97
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• 1996

The Total Least Squares(TLS) method is an unbiased estimator for solving overdetermined sets of linear equations Ax${\simeq}$b when errors occur in all data. However, as well as Least Squares(LS) method it doesn't show robustness while the errors have a heavy tailed probability density function. In this paper we proposed a robust method of TLS (Robust TLS, ROTLS) based on the characteristics of TLS solution. And the ROTLS is verified by applying it to system identification problems.

• Communications for Statistical Applications and Methods
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• v.7 no.3
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• pp.731-739
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• 2000

Conditional least square estimators for the parameters of he NLAR(p) time series models are obtained. it is also shown that these estimators are consistent and asymptotically normal.

• Communications for Statistical Applications and Methods
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• v.19 no.3
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• pp.423-432
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• 2012

This paper is concerned with the detection of multiple change-points in linear regression models. The proposed procedure relies on the local estimation for global change-point estimation. We propose a multiple change-point estimator based on the local least squares estimators for the regression coefficients and the split measure when the number of change-points is unknown. Its statistical properties are shown and its performance is assessed by simulations and real data applications.

• Journal of the Korean Statistical Society
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• v.12 no.2
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• pp.81-90
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• 1983

The least squares estimator of disturbance variance in a regression model is biased under a serial correlation. Under the assumption of an AR(I), Theil(1971) crudely related the bias with the autocorrelation of the disturbances and the autocorrelation of the explanatory variable for a simple regression. In this paper we derive a relation which relates the bias with the autocorrelation of disturbances and the autocorrelation of explanatory variables for a multiple regression with improved precision.

• Communications for Statistical Applications and Methods
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• v.18 no.6
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• pp.817-835
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• 2011

The moment(MM) and least squares(LS) estimations of the parameters are derived for the Pareto distribution in the presence of outliers. Further, we have derived a mixture method(MIX) of estimations with MM and LS that shows that the MIX is more efficient. In the final section we have given an example of actual data from a medical insurance company.

• Communications for Statistical Applications and Methods
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• v.3 no.2
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• pp.299-307
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• 1996

This article is concerned with the algorithms for the least median of squares estimator. An algorithm based on the $L{\infty}$ .inf.-estimation procedure is proposed in an attempt to improve the optimality of the estimate. And it is shown that the proposed algorithm yields more optimal estimate than the traditional resampling algorithms. The proposed algorithm employs a linear scaling transformation at each iteration of the$L{\infty}$-algorithm to deal with its computational inefficiency problem.

• Proceedings of the KIEE Conference
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• 1992.07a
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• pp.319-322
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• 1992

This paper proposes indirect adaptive pole placement adaptive controller using a modified least squares method. If an adaptive controller has good performance, it is necessary that an estimator have fast convergence. This paper presents a modified least squares method which guarantees the stability of estimator and has fast convergence. In this algorithm, information on signal level is obtained from the determinent of covariance matrix and according to it, weighting factor is tuned.

• The Korean Journal of Applied Statistics
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• v.19 no.1
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• pp.97-110
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• 2006

Since it is well-established that even high quality data tend to contain outliers, one would expect fat? greater reliance on robust regression techniques than is actually observed. But most of all robust regression estimators suffers from the computational difficulties and the lower efficiency than the least squares under the normal error model. The weighted self-tuning estimator (WSTE) recently suggested by Lee (2004) has no more computational difficulty and it has the asymptotic normality and the high break-down point simultaneously. Although it has better properties than the other robust estimators, WSTE does not have full efficiency under the normal error model through the weighted least squares which is widely used. This paper introduces a new approach as called the reweighted WSTE (RWSTE), whose scale estimator is adaptively estimated by the self-tuning constant. A Monte Carlo study shows that new approach has better behavior than the general weighted least squares method under the normal model and the large data.

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.655-658
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• 1999

In this study, We derived Recursive Least Squares(RLS) algorithm with adaptive maximum -likelihood channel estimate for digital pulse amplitude modulated sequence in the presence of intersymbol interference and additive white Gaussian noise. RLS algorithms have better convergence characteristics than conventional algorithms, LMS Least Mean Squares) algorithms.