• 대한전자공학회:학술대회논문집
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• 대한전자공학회 1999년도 추계종합학술대회 논문집
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• pp.43-46
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• 1999

Blind equalizers recover the transmitted data using signal's statistical characteristics only. Because of its computational simplicity and fast convergence rate, CMA is widely used in practice. Blind equalizers, however, converge much slowly than conventional equalizers which use the training signals. In order to improve the convergence rate, many modified blind equalization algorithms have been proposed. Among those, Normalized CMA (NCMA) was applied to increase the convergence rate by using the large step size. Unfortunately it can only be applied for the constant modulus signal constellation scheme. this paper, we propose the Selective NCMA (SNCMA) that improve the convergence rate of blind equalization algorithms by using NCMA for non-constant modulus signalling method such as QAM constellation. We achieved fast start-up convergence rate and reduced steady-state residual error.

• 응용통계연구
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• 제6권2호
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• pp.319-328
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• 1993

뉴톤-랩슨 반복법이 최우추정량에 접근하는 비율이 초기값에 따라 가속화함을 보았다. 그러 므로 최우추정량을 구하기 어려운 경우에 통계적 목적 - Bahadur 효율, 콰지(Quasi) 우도비 검정 통계량의 점근분포, Bartlett 정정계수(correction factor)등 - 에 따라 뉴톤-랩슨 반복 의 횟수를 정하여 쓸 수 있다.

• Journal of the Korean Statistical Society
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• 제31권2호
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• pp.261-276
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• 2002

Probability density or its derivatives may have a discontinuity/change point at an unknown location. We propose a method of estimating the location and the jump size of the discontinuity point based on kernel type density or density derivatives estimators with one-sided equivalent kernels. The rates of convergence of the proposed estimators are derived, and the finite-sample performances of the methods are illustrated by simulated examples.

• Communications for Statistical Applications and Methods
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• 제3권3호
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• pp.271-282
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• 1996

this paper deal with the estimation of the power spectral density function of time series. A kernel estimator which is based on local average is defined and the rates of convergence of the pointwise, $$L_2$-norm; and; $L{\infty}$-norm associated with the estimator are investigated by restricting as to kernels with suitable assumptions. Under appropriate regularity conditions, it is shown that the optimal rate of convergence for 0$N^{-r}$ both in the pointwiseand $$L_2$-norm, while; $N^{r-1}(logN)^{-r}$is the optimal rate in the $L{\infty}-norm$. Some examples are given to illustrate the application of main results.

• 한국통계학회:학술대회논문집
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• 한국통계학회 2002년도 추계 학술발표회 논문집
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• pp.103-108
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• 2002

We consider an estimation of discontinuous variance function in nonparametric heteroscedastic random design regression model. We first propose estimators of a change point and jump size in variance function and then construct an estimator of entire variance function. We examine the rates of convergence of these estimators and give results on their asymptotics. Numerical work reveals that the effectiveness of change point analysis in variance function estimation is quite significant.

• Journal of the Korean Statistical Society
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• 제35권1호
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• pp.1-23
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• 2006

In this paper we consider an estimation of the discontinuous variance function in nonparametric heteroscedastic random design regression model. We first propose estimators of the change point in the variance function and then construct an estimator of the entire variance function. We examine the rates of convergence of these estimators and give results for their asymptotics. Numerical work reveals that using the proposed change point analysis in the variance function estimation is quite effective.

• Kyungpook Mathematical Journal
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• 제56권3호
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• pp.877-897
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• 2016

In the present article, we study some approximation properties of the Kantorovich type generalization of $Sz{\acute{a}}sz$ type operators involving Charlier polynomials introduced by S. Varma and F. Taşdelen (Math. Comput. Modelling, 56 (5-6) (2012) 108-112). First, we establish approximation in a Lipschitz type space, weighted approximation theorems and A-statistical convergence properties for these operators. Then, we obtain the rate of approximation of functions having derivatives of bounded variation.

• Journal of the Korean Statistical Society
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• 제19권2호
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• pp.105-112
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• 1990

Let (X, Y) be a pair random variables and let f denote the regression function of the response Y on the measurement variable X. Let K(f) denote a derivative of f. The least squares method is used to obtain a tensor spline estimator $\hat{f}$ of f based on a random sample of size n from the distribution of (X, Y). Under some mild conditions, it is shown that $K(\hat{f})$ achieves the optimal rate of convergence for the estimation of K(f) in $L_2$ and $L_{\infty}$ norms.

• Journal of the Korean Statistical Society
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• 제33권1호
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• pp.59-78
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• 2004

A regression function in generalized linear model may have a discontinuity/change point at unknown location. In order to estimate the location of the discontinuity point and its jump size, the strategy is to use a nonparametric approach based on one-sided kernel weighted local-likelihood functions. Weak convergences of the proposed estimators are established. The finite-sample performances of the proposed estimators with practical aspects are illustrated by simulated examples.

• Journal of the Korean Statistical Society
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• 제35권1호
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• pp.105-114
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• 2006

In this paper we propose a simple and computationally attractive difference-based variance estimator in nonparametric regression models with multivariate predictors. We show that the estimator achieves $n^{-1/2}$ rate of convergence for regression functions with only a first derivative when d, the dimension of the predictor, is less than or equal to 4. When d > 4, the rate turns out to be $n^{-4/(d+4)}$ under the first derivative condition for the regression functions. A numerical study suggests that the proposed estimator has a good finite sample performance.