• Communications for Statistical Applications and Methods
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• v.3 no.3
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• pp.75-81
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• 1996

Polynomial measurement error model(MEM) with one predictor is considered. It is briefly mentioned that Chan and Mak's generalized least squares estimator(GLSE) can be derived more easily if Hermite polynomial concept is applied. It is proved that GLSE derived using new procedure is equivalent to the estimator obtained from corrected score function. Finally, much simpler proof of the asymptotic behavior of GLSE than that of Chan and Mak is provided. Much simpler formula of asymptotic covariance matrix of GLSE is a part of that proof.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.643-646
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• 1994

A method of trajectory error estimation of a hypersonic vehicle, by a covariance analysis technique is presented and discussed. The method itself is a wellkown technique, however, the thema has been rarely treated. As the importance is increasing, it is explained here and some of our newly deviced techniques are also presented.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.10
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• pp.32-43
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• 1994

This paper presents new QRD-based recursive least squares algorithms for bilinear lattice filter. Bilinear recursive least square lattice algorithms are derived by using the QR decomposition for minimization covariance matrix of predication error by applying Givens rotation to the bilinear recursive least squares lattics algorithms. The proposed algorithms are applied to the bilinear system identification to evaluate the performance of algoithms. Computer simulations show that the convergence properties of the proposed algorithms are superior to that of the algorithms proposed by Baik when signal includes the measurement noise.

• 한국데이터정보과학회:학술대회논문집
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• 2006.04a
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• pp.171-179
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• 2006

For the problem of variable selection in linear models, we consider the errors are correlated with V covariance matrix. Hocking's theorems on the effects of the overfitting and the underfitting in linear model are extended to the less than full rank and correlated error model, and to the ANCOVA model.

• Proceedings of the KIEE Conference
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• 2007.04a
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• pp.3-5
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• 2007

This paper presents decision feedback equalizers using a recurrent neural network trained algorithm using extended Kalman filter(EKF) and sigma-point Kalman filter(SPKF). EKF is propagated, analytically through the first-order linearization of the nonlinear system. This can introduce large errors in the true posterior mean and covariance of the Gaussian random variable. The SPKF addresses this problem by using a deterministic sampling approach. The features of the proposed recurrent neural equalizer And we investigate the bit error rate(BER) between EKF and SPKF.

• Proceedings of the Korean Statistical Society Conference
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• 2005.11a
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• pp.187-192
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• 2005

For the problem of variable selection in linear models, we consider the errors are correlated with V covariance matrix. Hocking's theorems on the effects of the overfitting and the undefitting in linear model are extended to the less than full rank and correlated error model, and to the ANCOVA model

• Communications for Statistical Applications and Methods
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• v.10 no.1
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• pp.233-237
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• 2003

An asymptotic property of the ordinary least squares estimator of the disturbance variance is considered in the regression model with correlated errors. It is shown that the convergence in probability of S$^2$ is equivalent to the asymptotic unbiasedness. Beyond the assumption on the design matrix or the variance-covariance matrix of disturbances error, the result is quite general and simplify the earlier results.

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 1983.10a
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• pp.88-92
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• 1983

The performance of noncoherent fast frequency-hopped spread spectrum multiple access communication systems with square-law combining over fading channel is presented. The expression for the probalility of error as a performance measure is derived by means of momenting function of the dedecision variables, and the cross-covariance of the fading process the ambiguty function of the transmitted signals.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2003.06a
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• pp.238-241
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• 2003

This paper presents a nonlinear least squares decision feedback equalizer Bilinear systems are attractive because of the ability to approximate a large class of nonlinear systems efficiently. The nonlinearity of channel is modeled using a bilinear system. The algorithms are derived by using the QR decomposition for minimization covariance matrix of prediction error by applying Givens rotation to the bilinear model. Result of computer simulation experiments that compare the performance of the bilinear DFE to two other DFE's in eliminating the intersymbol interference caused by a nonlinear channel are presented In the paper.

• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2082-2084
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• 2003

A practical real-time LOS rate estimator is proposed to handle the time-varying measurement noise statistics. To calculate the optimal Kalman gain, the algebraic transformation method is taken into account. By using the algebraic transformation, the differential algebraic Riccati equation(DARE) regarding estimation error covariance is replaced by the simple algebraic Riccati equation(ARE). The proposed LOS estimation filter gain is only a function of relative range. Consequently, the proposed method is computationally very efficient and suitable for embedded environment.