• Communications for Statistical Applications and Methods
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• v.2 no.2
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• pp.231-242
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• 1995

In this paper we consider kernel type estimator of the spectral density at a point in the analysis of stationary time series data. The kernel entails choice of smoothing parameter called bandwidth. A data-based bandwidth choice is proposed, and it is obtained by solving an equation similar to Sheather(1986) which relates to the probability density estimation. A Monte Carlo study is done. It reveals that the spectral density estimates using the data-based bandwidths show comparatively good performance.

• Communications for Statistical Applications and Methods
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• v.4 no.3
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• pp.833-845
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• 1997

Fourier expansion is considered for the deconvolution problem of estimating a probability density function when the sample observations are contaminated with random noise. In the log-Fourier method of density estimation for data without noise, the logarithm of the unknown density function is approximated by a trigonometric function, the unknown parameters of which are estimated by maximum likelihood. The log-Fourier density estimation method, which has been considered theoretically by Koo and Chung (1997), is studied for the finite-sample case with noise. Numerical examples using simulated data are given to show the performance of the log-Fourier deconvolution.

• Proceedings of the Korean Statistical Society Conference
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• 2000.11a
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• pp.65-73
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• 2000

Kernel estimator is very popular in nonparametric density estimation. In this paper we propose an estimator which reduces the bias to the fourth power of the bandwidth, while the variance of the estimator increases only by at most moderate constant factor. The estimator is fully nonparametric in the sense of convex combination of three kernel estimators, and has good numerical properties.

• Communications for Statistical Applications and Methods
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• v.18 no.4
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• pp.457-465
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• 2011

Sample entropy (Vasicek, 1976) has poor performance, and several nonparametric entropy estimators have been proposed as alternatives. In this paper, we consider a piecewise uniform density function based on quantiles, which enables us to evaluate entropy in each interval, and study the poor performance of the sample entropy in terms of the poor estimation of lower and upper quantiles. Then we propose some improved entropy estimators by simply modifying the quantile estimators, and compare their performances with some existing estimators.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.9 no.4
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• pp.163-169
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• 1984

We propose a new parameter estimation algorithm that converges with probability one and in mean square, if the mean is the function of parameter of the probability density function. This recursive algorithm is applicable also even though the parameters we estimate are multiparameter case. And the results are shown by the computer simulation.

• Korean Journal of Mathematics
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• v.26 no.2
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• pp.155-166
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• 2018

Consider FARIMA time series with innovations that have infinite variances. We are interested in the estimation of self-similarities $H_n$ of FARIMA(0, d, 0) by using modified R/S statistic. We can confirm that the $H_n$ converges to Hurst parameter $H=d+\frac{1}{2}$. Finally, we figure out ARMA and minimum variance power spectrum density of FARIMA processes.

• Journal of the Korean Statistical Society
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• v.33 no.3
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• pp.255-269
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• 2004

This paper develops log-density estimation for directional data. The methodology is to use expansions with respect to spherical harmonics followed by estimating the unknown parameters by maximum likelihood. Minimax rates of convergence in terms of the Kullback-Leibler information divergence are obtained.

• Communications for Statistical Applications and Methods
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• v.4 no.3
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• pp.891-901
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• 1997

The objective of this research is to investigate the problem of goodness of fit testing based on nonparametric density estimation with a data-driven smoothing parameter. The small and large smaple properties of the proposed test statistic $Z_{mn}$ are investigated with the minimizer $\widehat{m}$ of the estimated mean integrated squared error by the Diggle and Hall (1986) method.

• Journal of the Korean Statistical Society
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• v.10
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• pp.140-144
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• 1981

This paper approximates to a kernel density estimate by a truncated series of expansion involving Hermite polynomials, since this could ease the computing burden involved in the kernel-based density estimation. However, this truncated series may give a multimodal estimate when we are estiamting unimodal density. In this paper we will show a way to insure the truncated series to be positive and unimodal so that the approximation to a kernel density estimator would be maeningful.

• Journal of Environmental Science International
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• v.18 no.8
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• pp.885-897
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• 2009

In order to make sure the impact of spatial resolution of wind energy map on the estimation of wind power density in the Korean Peninsula, the comparison studies on the characteristics of wind energy map with three different spatial resolutions were carried out. Numerical model used in the establishment of wind map is MM5 (5th generation Mesoscale Model) with RBAPS (Regional Data Assimilation and Prediction System) as initial and boundary data. Analyzed Period are four months (March, August, October, and December), which are representative of four seasons. Since high spatial resolution of wind map make the undulation of topography be clear, wind pattern in high resolution wind map is correspond well with topography pattern and maximum value of wind speed is also increase. Indication of island and mountains in wind energy map depends on the its spatial resolution, so wind patterns in Heuksan island and Jiri mountains are clearly different in high and low resolutions. And area averaged power density can be changed by estimation method of wind speed for unit area in the numerical model and by treatment of air density. Therefore the studiable resolution for the topography should be evaluated and set before the estimation of wind resources in the Korean Peninsula.