• Journal of the Chungcheong Mathematical Society
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• v.22 no.2
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• pp.177-186
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• 2009

The global least squares method (Gl-LSQR) is a generalization of LSQR method for solving linear system with multiple right hand sides. In this paper, we present how to apply this algorithm for solving the image restoration problem and illustrate the usefulness and effectiveness of this method from numerical experiments.

• Journal of the Korean Data and Information Science Society
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• v.16 no.4
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• pp.1087-1094
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• 2005

e-insensitive support vector regression(e-SVR) is capable of providing more complete description of the linear and nonlinear relationships among random variables. In this paper we propose an iterative reweighted least squares(IRWLS) procedure to solve the quadratic problem of e-SVR with a modified loss function. Furthermore, we introduce the generalized approximate cross validation function to select the hyperparameters which affect the performance of e-SVR. Experimental results are then presented which illustrate the performance of the IRWLS procedure for e-SVR.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.1
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• pp.44-53
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• 2009

In this paper, Recursive Least Squares (RLS) and Fourier Transform Regression (FTR) methods for estimating stability and control derivatives of small unmanned helicopter are evaluated together with MMLE technique. Flight data simulated by using a commercial small-scale helicopter model are exploited to estimate the parameters with accuracies for hover and cruise modes. The performances of the system identification methods are also compared by analyzing the responses of the reconstructed systems using estimated derivatives.

• 한국가스학회:학술대회논문집
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• 2005.05a
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• pp.212-212
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• 2005

• Journal of applied mathematics & informatics
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• v.27 no.3_4
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• pp.903-908
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• 2009

The shrink parameter in ridge regression may be contaminated by outlying points. We propose robust cross validation scores in ridge regression instead of classical cross validation. We use robust location estimators such as median, least trimmed squares, absolute mean for robust cross validation scores. The robust scores have global robustness. Simulations are performed to show the effectiveness of the proposed estimators.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.146-146
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• 2000

This paper presents field test results of the GPS precise positioning using carrier phase observable. The Least-squares AMBiguity Decorrelation Adjustment(LAMBDA) method is implemented to resolve integer ambiguity problem for two epoch Ll carrier phase measurement data. Field test results show that the GPS precise positioning of cm-level accuracy is obtainable with conventional low cost, single frequency C/A code GPS receivers.

• 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.

• Journal of the Korean Statistical Society
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• v.34 no.1
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• pp.61-71
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• 2005

A class of asymmetric ARCH processes is proposed via binary random power transformations. This class accommodates traditional nonlinear models such as threshold ARCH (Rabemanjara and Zacoian (1993)) and Box-Cox type ARCH models(Higgins and Bera (1992)). Stationarity condition of the model is addressed. Iterative least squares(ILS) and pseudo maximum like-lihood(PML) methods are discussed for estimating parameters and related algorithms are presented. Illustrative analysis for Korea Stock Prices Index (KOSPI) data is conducted.

• Journal of the Korean Statistical Society
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• v.33 no.4
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• pp.401-410
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• 2004

We address the problem of parameter estimation in multivariate distributions under ignorable non-monotone missing data. The factoring likelihood method for monotone missing data, termed by Rubin (1974), is extended to a more general case of non-monotone missing data. The proposed method is algebraically equivalent to the Newton-Raphson method for the observed likelihood, but avoids the burden of computing the first and the second partial derivatives of the observed likelihood. Instead, the maximum likelihood estimates and their information matrices for each partition of the data set are computed separately and combined naturally using the generalized least squares method.