• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.9C
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• pp.735-742
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• 2010

In this paper, we propose adaptive algorithms for interference cancellation in RF repeaters with multiple transmit and receive antennas. When multiple antennas are used in a repeater, the imperfect isolation between transmit and receive antennas causes the feedback interference which is modeled as multi-input multi-output (MIMO) channel. To remove the feedback interference, we derive the least mean square (LMS) algorithm and the recursive least squares (RLS) algorithm for interference cancellation based on adaptive signal processing techniques. Through computer simulations for the proposed algorithms, we analyze the convergence characteristics and compare the steady-state performance for interference cancellation.

• Proceedings of the IEEK Conference
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• 2000.06a
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• pp.45-48
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• 2000

The use of multi-level modulation scheme in the wireless LAN(Local Area Networks) system requires an accurate channel estimation. In this paper, we present sequential least squares(LS) channel estimation scheme based on decision-directed channel tracking scheme. The proposed scheme improves the performance of the conventional LS estimator for wireless LAN. In addition, its structure is suitable for the high-rate wireless LAN. Simulation results show that the proposed scheme achieves about IdB Packet Error Rate(PER) gain compared to the LS scheme in a frequency selective channel.

• Journal of the Korean Data and Information Science Society
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• v.17 no.2
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• pp.401-409
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• 2006

We consider a generalized partly-parametric additive risk model which generalizes the partly parametric additive risk model suggested by McKeague and Sasieni (1994). As an estimation method of this model, we propose to use the weighted least square estimation, suggested by Huffer and McKeague (1991), for Aalen's additive risk model by a piecewise constant risk. We provide an illustrative example as well as a simulation study that compares the performance of our method with the ordinary least squares method.

• The Korean Journal of Applied Statistics
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• v.6 no.1
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• pp.79-93
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• 1993

We consider adaptive L-estimation of estimating slope parameter in regression model. The proposed estimator is simple extension of trimmed least squares estimator proposed by ruppert and carroll. The efficiency of the proposed estimator is especially well compared with usual least squares estimator, least absolute value estimator, and M-estimators designed for asymmetric distributions under asymmetric error distributions.

• Journal of the Korean Data and Information Science Society
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• v.22 no.3
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• pp.613-618
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• 2011

The proposed method is based on a penalized log partial likelihood of Cox proportional hazard model with L1-penalty. We use the iteratively reweighted least squares procedure to solve L1 penalized log partial likelihood function of Cox proportional hazard model. It provide the ecient computation including variable selection and leads to the generalized cross validation function for the model selection. Experimental results are then presented to indicate the performance of the proposed procedure.

• 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.

• Journal of the Korea Society for Simulation
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• v.9 no.3
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• pp.43-51
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• 2000

In regression model, we estimate the unknown parameters by using various methods. There are the least squares method which is the most general, the least absolute deviation method, the regression quantile method and the asymmetric least squares method. In this paper, we will compare each others with two cases: firstly the theoretical comparison in the asymptotic sense and then the practical comparison using Monte Carlo simulation for a small sample size.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.5
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• pp.571-575
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• 2003

In this paper, we describe a method for fuzzy polynomial regression analysis for fuzzy input--output data using shape preserving operations for least-squares fitting. Shape preserving operations simplifies the computation of fuzzy arithmetic operations. We derive the solution using mixed nonlinear program.

• Proceedings of the Korea Society for Simulation Conference
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• 2000.11a
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• pp.6-10
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• 2000

In regression model we estimate the unknown parameters using various methods. There are the least squares method which is the most general, the least absolute deviation, the regression quantile and the asymmetric least squares method. In this paper, we will compare each others with two case: to begin with the theoretical comparison in the asymptotic sense, and then the practical comparison using Monte Carlo simulation for a small sample size.

• Communications of the Korean Mathematical Society
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• v.18 no.4
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• pp.703-712
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• 2003

This paper deals with the strong consistency of the asymmetric least squares for the nonlinear censored regression models which includes dependent variables cut off midway by any of external conditions, and provide the sufficient conditions which ensure the strong consistency of proposed estimators of the censored regression models. One example is given to illustrate the application of the main result.