• Communications for Statistical Applications and Methods
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• v.24 no.1
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• pp.1-13
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• 2017

This manuscript summarized advances in bootstrap methods for long-range dependent time series data. The stationary linear long-memory process is briefly described, which is a target process for bootstrap methodologies on time-domain and frequency-domain in this review. We illustrate time-domain bootstrap under long-range dependence, moving or non-overlapping block bootstraps, and the autoregressive-sieve bootstrap. In particular, block bootstrap methodologies need an adjustment factor for the distribution estimation of the sample mean in contrast to applications to weak dependent time processes. However, the autoregressive-sieve bootstrap does not need any other modification for application to long-memory. The frequency domain bootstrap for Whittle estimation is provided using parametric spectral density estimates because there is no current nonparametric spectral density estimation method using a kernel function for the linear long-range dependent time process.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.28 no.3
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• pp.171-176
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• 2016

The distribution shapes of air and water temperatures are basic and essential information, which determine the frequency patterns of their occurrence. It is also very useful to understand the changes in long-term air and water temperatures with respect to climate change. The typical distribution shapes of air and water temperatures cannot be well fitted using widely used/accepted normal distributions because their shapes show multimodal distributions. In this study, Gaussian mixture distributions and kernel distributions are suggested as the more suitable models to fit their distribution shapes. Based on the results, the tail shape exhibits different patterns. The tail is long in higher temperature regions of water temperature distribution and in lower temperature regions of air temperature distribution. These types of shape comparisons can be useful to identify the patterns of long-term air and water temperature changes and the relationship between air and water temperatures. It is nearly impossible to identify change patterns using only mean-temperatures and normal distributions.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.24 no.1
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• pp.1-9
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• 2012

Tidal residual is being an important factor by the influence of the climate change in terms of the coastal safety and defense. It is one of the most important factor for the determination of the reference sea level in order to check the safety and performance of the coastal structures in company with the typhoon intensity variation. The probability density function (pdf) of tidal residuals in the Korean coasts have a non-ignorable skewness and high kurtosis. It is highly restricted to the application of the normal pdf assumption as an approximated pdf of tidal residuals. In this study, the pdf of tidal residuals estimated using the Kernel function is suggested as a more reliable and accurate pdf of tidal residuals than the normal function. This suggested pdf shows a good agreement with the empirical cumulative distribution function and histogram. It also gives the more accurate estimation result on the extreme values in comparison with the results based on the normal pdf assumption.

• Journal of the Korean Data and Information Science Society
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• v.27 no.1
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• pp.111-120
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• 2016

In usual, the discontinuous variance function was estimated nonparametrically using a kernel type estimator with data sets split by an estimated location of the change point. Kang et al. (2000) proposed the Gasser-$M{\ddot{u}}ller$ type kernel estimator of the discontinuous regression function using the adjusted observations of response variable by the estimated jump size of the change point in $M{\ddot{u}}ller$ (1992). The adjusted observations might be a random sample coming from a continuous regression function. In this paper, we estimate the variance function using the Nadaraya-Watson kernel type estimator using the adjusted squared residuals by the estimated location of the change point in the discontinuous variance function like Kang et al. (2000) did. The rate of convergence of integrated squared error of the proposed variance estimator is derived and numerical work demonstrates the improved performance of the method over the exist one with simulated examples.

• The Journal of the Acoustical Society of Korea
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• v.36 no.4
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• pp.261-266
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• 2017

In this paper, we propose a vocal and nonvocal separation method which uses a combination of kernel model and LSTM (Long-Short Term Memory) networks. Conventional vocal and nonvocal separation methods estimate the vocal component even in sections where only non-vocal components exist. This causes a problem of the source estimation error. Therefore we combine the existing kernel based separation method with the vocal/nonvocal classification based on LSTM networks in order to overcome the limitation of the existing separation methods. We propose a parallel combined separation algorithm and series combined separation algorithm as combination structures. The experimental results verify that the proposed method achieves better separation performance than the conventional approaches.

• Korean Journal of Environment and Ecology
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• v.28 no.6
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• pp.642-649
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• 2014

Mallard (Anas platyrhynchos) is the abundant winter visitor in South Korea. Mallard migrates long distances between Russian Siberia and Korea. This species prefers a rice paddy area as their winter habitat. We captured birds using cannon-net, and attached the GPS-Mobile phone based Telemetry(WT-200) on Seven Mallards in the winter of 2011~2013. We were monitored wintering home-range and movement distance. We analyzed the tracking location data using ArcGIS 9.0 and calculated Kernel Density Estimation (KDE) and Minimum Convex Polygon (MCP). The average home-range in the wintering ground by MCP was $118.8km^2$(SD=70.1, n=7)and the maximum home-rang was $221.8km^2$ and the minimum was $27.7km^2$. Extents of home-range by KDE were $60.0km^2$(KDE 90%), $23.0km^2$(KDE 70%) and $11.6km^2$(KDE 50%). Mallard moved an average of 19.4 km from start site(attach to WT-200 site), maximum moved was 33.2 km and minimum moved was 9.4 km. The average distance of 0.8 km between GPS fixed point(range 0.2~1.6 km), maximum moved was 19.7 km. Mallard moved a very short distance in wintering season and showed a very high water-dependent trends in wintering site.

• Journal of the Korean Data and Information Science Society
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• v.22 no.1
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• pp.99-105
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• 2011

We propose a semi-supervised learning algorithm based on a form of regularization that incorporates similarity and dissimilarity penalty terms. Our approach uses a graph-based encoding of similarity and dissimilarity. We also present a model-selection method which employs cross-validation techniques to choose hyperparameters which affect the performance of the proposed method. Simulations using two types of dat sets demonstrate that the proposed method is promising.

• Journal of the Korean Data and Information Science Society
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• v.13 no.2
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• pp.251-260
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• 2002

The aim of this paper is to consider of detecting the location, the jump size and the number of change-points in regression functions by using the local linear fit which is one of nonparametric regression techniques. It is obtained the asymptotic properties of the change points and the jump sizes. and the correspondin grates of convergence for change-point estimators.

• Communications for Statistical Applications and Methods
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• v.8 no.2
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• pp.483-497
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• 2001

When one fits a parametric density function to a data set, it is usually advisable to test the goodness of the postulated model. In this paper we study the nonparametric tests for testing the null hypothesis against general alternatives, when the null hypothesis specifies the density function up to unknown parameters. We modify the test statistic which was proposed by the first author and his colleagues. Asymptotic distribution of the modified statistic is derived and its performance is compared with some other tests through simulation.

• Journal of the Korean Statistical Society
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• v.18 no.2
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• pp.107-117
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• 1989

A stronger result than that of Park and Marron (1994) is proved here on the asymptotic distribution of the plug-in bandwidth selector. The new result is that the plug-in bandwidth selector may have the rate of convergence ($n^{-4/13}$ with less smoothness conditions on the unknown density functions than as described in Park and Marron's paper. Together with this, a class of various plug-in bandwidth selectors are considered and their asymptotic distributions are given. Finally, some ideas of possible improvements on those plug-in bandwidth selectors are provided.