• Journal of the Korean Statistical Society
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• v.31 no.2
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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.

• Journal of the Korean Data and Information Science Society
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• v.17 no.1
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• pp.213-220
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• 2006

Since Beran (1977) developed the minimum Hellinger distance estimation, this method has been a popular topic in the field of robust estimation. In the process of defining a distance, a kernel density estimator has been widely used as a density estimator. In this article, however, we show that a combination of a kernel density estimator and an empirical density could result a smaller bias of the minimum Hellinger distance estimator than using just a kernel density estimator for a location parameter.

• Journal of the Korean Statistical Society
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• v.27 no.1
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• pp.91-100
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• 1998

A method of estimating probability density using regression tools is presented here. It is based on equal-length binning and locally weighted approximate likelihood for bin counts. The method is particularly useful for densities with bounded supports, where it automatically corrects edge effects without using boundary kernels.

• Journal of the Korean Data and Information Science Society
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• v.3 no.1
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• pp.17-24
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• 1992

The problem of estimating symmetric probability density with high kurtosis is considered. Such densities are often estimated poorly by a global bandwidth kernel estimation since good estimation of the peak of the distribution leads to unsatisfactory estimation of the tails and vice versa. In this paper, we propose a transformation technique before using a global bandwidth kernel estimator. Performance of density estimator based on proposed transformation is investigated through simulation study. It is observed that our method offers a substantial improvement for the densities with high kurtosis. However, its performance is a little worse than that of ordinary kernel estimator in the situation where the kurtosis is not high.

• Communications for Statistical Applications and Methods
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• v.7 no.1
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• pp.13-22
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• 2000

We consider local likelihood method with a smoothed version of the model density in stead of an original model density. For simplicity a model is assumed as the log-linear density then we were able to show that the proposed local density estimator is less affected by changes among observations but its bias increases little bit more than that of the currently used local density estimator. Hence if we use the existing method and the proposed method in a proper way we would derive the local density estimator fitting the data in a better way.

• The Transactions of the Korea Information Processing Society
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• v.6 no.3
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• pp.766-772
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• 1999

With the increase of portable system and high-density IC, power consumption of VLSI circuits is very important factor in design process. Power estimation is required in order to estimate the power consumption. A simple and correct solution of power estimation is to use circuit simulation. But it is very time consuming and inefficient way. Probabilistic method has been proposed to overcome this problem. Transition density using probability was an efficient method to estimate power consumption using BDD and Boolean difference. But it is difficult to build the BDD and compute complex Boolean difference. In this paper, we proposed Propowest. Propowest is building a digraph of circuit, and easy and fast in computing transition density by using modified COP algorithm. Propowest provides an efficient way for power estimation.

• The KIPS Transactions:PartB
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• v.13B no.1 s.104
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• pp.27-34
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• 2006

In this study, we propose this new algorithm that generates score function in ICA(Independent Component Analysis) using entropy theory. To generate score function, estimation of probability density function about original signals are certainly necessary and density function should be differentiated. Therefore, we used kernel density estimation method in order to derive differential equation of score function by original signal. After changing formula to convolution form to increase speed of density estimation, we used FFT algorithm that can calculate convolution faster. Proposed score function generation method reduces the errors, it is density difference of recovered signals and originals signals. In the result of computer simulation, we estimate density function more similar to original signals compared with Extended Infomax and Fixed Point ICA in blind source separation problem and get improved performance at the SNR(Signal to Noise Ratio) between recovered signals and original signal.

• Journal of the Korean Operations Research and Management Science Society
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• v.38 no.1
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• pp.201-216
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• 2013

Diffusion is a basic mathematical tool for modern financial engineering. The theory of the estimation methods for diffusion models is an important topic of the financial engineering. Many researches have been tried to apply the likelihood estimation method for estimating diffusion models. However, the likelihood estimation method for diffusion is complicated and needs much amount of computing. In this paper we develop the estimation methods which are simple enough to be compared to the Euler approximation method, and efficient enough statistically to be compared to the likelihood estimation method. We devise pseudo-likelihood and propose the maximum pseudo-likelihood estimation methods. The pseudo-likelihoods are obtained by approximating the transition density with normal distributions. The means and the variances of the distributions are obtained from the delta expansion suggested by Lee, Song and Lee (2012). We compare the newly suggested estimators with other existing estimators by simulation study. From the simulation study we find the maximum pseudo-likelihood estimator has very similar properties with the maximum likelihood estimator. Also the maximum pseudo-likelihood estimator is easy to apply to general diffusion models, and can be obtained by simple numerical steps.

• Communications for Statistical Applications and Methods
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• v.17 no.3
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• pp.451-457
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• 2010

This paper considers the problem of nonparametric deconvolution density estimation when sample observa-tions are contaminated by double exponentially distributed errors. Three different deconvolution density estima-tors are introduced: a weighted kernel density estimator, a kernel density estimator based on the support vector regression method in a RKHS, and a classical kernel density estimator. The performance of these deconvolution density estimators is compared by means of a simulation study.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.9
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• pp.2170-2190
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• 2012

There are nowadays strong demands for intelligent surveillance systems, which can infer or understand more complex behavior. The application of crowd density estimation methods could lead to a better understanding of crowd behavior, improved design of the built environment, and increased pedestrian safety. In this paper, we propose a new crowd density estimation method, which aims at estimating not only a moving crowd, but also a stationary crowd, using images captured from surveillance cameras situated in various public locations. The crowd density of the moving people is measured, based on the moving area during a specified time period. The moving area is defined as the area where the magnitude of the accumulated optical flow exceeds a predefined threshold. In contrast, the stationary crowd density is estimated from the coarseness of textures, under the assumption that each person can be regarded as a textural unit. A multilayer neural network is designed, to classify crowd density levels into 5 classes. Finally, the proposed method is experimented with PETS 2009 and the platform of Gangnam subway station image sequences.