• The Korean Journal of Applied Statistics
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• v.26 no.3
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• pp.483-494
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• 2013

The importance of financial risk management has been highlighted after several recent incidences of global financial crisis. One of the issues in financial risk management is how to measure the risk; currently, the most widely used risk measure is the Value at Risk(VaR). We can consider to estimate VaR using extreme value theory if the financial data have heavy tails as the recent market trend. In this paper, we study estimations of VaR using Peaks over Threshold(POT), which is a common method of modeling fat-tailed data using extreme value theory. To use POT, we first estimate parameters of the Generalized Pareto Distribution(GPD). Here, we compare three different methods of estimating parameters of GPD by comparing the performance of the estimated VaR based on KOSPI 5 minute-data. In addition, we simulate data from normal inverse Gaussian distributions and examine two parameter estimation methods of GPD. We find that the recent methods of parameter estimation of GPD work better than the maximum likelihood estimation when the kurtosis of the return distribution of KOSPI is very high and the simulation experiment shows similar results.

• The Journal of the Acoustical Society of Korea
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• v.21 no.4E
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• pp.156-163
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• 2002

We apply independent component analysis (ICA) for extracting an optimal basis to the problem of finding efficient features for representing speech signals of a given speaker The speech segments are assumed to be generated by a linear combination of the basis functions, thus the distribution of speech segments of a speaker is modeled by adapting the basis functions so that each source component is statistically independent. The learned basis functions are oriented and localized in both space and frequency, bearing a resemblance to Gabor wavelets. These features are speaker dependent characteristics and to assess their efficiency we performed speaker identification experiments and compared our results with the conventional Fourier-basis. Our results show that the proposed method is more efficient than the conventional Fourier-based features in that they can obtain a higher speaker identification rate.

• The Journal of the Acoustical Society of Korea
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• v.21 no.4
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• pp.156-156
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• 2002

We apply independent component analysis (ICA) for extracting an optimal basis to the problem of finding efficient features for representing speech signals of a given speaker The speech segments are assumed to be generated by a linear combination of the basis functions, thus the distribution of speech segments of a speaker is modeled by adapting the basis functions so that each source component is statistically independent. The learned basis functions are oriented and localized in both space and frequency, bearing a resemblance to Gabor wavelets. These features are speaker dependent characteristics and to assess their efficiency we performed speaker identification experiments and compared our results with the conventional Fourier-basis. Our results show that the proposed method is more efficient than the conventional Fourier-based features in that they can obtain a higher speaker identification rate.

• Proceedings of the Acoustical Society of Korea Conference
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• 1994.06a
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• pp.698-703
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• 1994

In the actual sound environment, the random signal often shows a complex fluctuation pattern apart from a standard Gaussian distribution. In this study, an evaluation method for the sound environmnetal system is proposed in the generalized form applicable to the actual stochastic phenomena, by introducing two type information processing methods based on the regression model of expansion series type and the Fuzzy probability. The effectiveness of the proposed method are confirmed experimentally too by applying it to the observed data in the actual noise environment.

• The Journal of the Acoustical Society of Korea
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• v.25 no.2
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• pp.56-60
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• 2006

Based on the fact that audio signals in the time domain have the generalized Gaussian distribution. an optimal parameter estimation of the ML (maximum likelihood) test based audio watermark decoder. which leads to the minimal bit error rate, is Proposed. Its superiority of performance over the existing estimation and the conventional correlation based decoder is demonstrated experimentally.

• Progress in Superconductivity and Cryogenics
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• v.17 no.2
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• pp.41-44
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• 2015

Critical current of high-temperature superconducting (HTS) coil is influenced by its own self magnetic field. Direction and density distribution of the magnetic field around the coil are fixed after the shape of the coil is decided. If the entire part of the HTS tape has homogeneous $I_c$ distribution characteristic, quench would be initiated in fixed location on the coil. However, the actual HTS tape has inhomogeneous $I_c$ distribution along the length. If the $I_c$ distribution of the HTS tape is known, we can expect the spot within the HTS coil that has the highest probability to initiate the quench. In this paper, $I_c$ distribution within the HTS coil under self-field effect is simulated by MATLAB. In the simulation procedure, $I_c$ distribution of the entire part of the HTS tape is assume d to follow Gaussian-distribution by central limit theorem. The HTS coil model is divided into several segments, and the critical current of each segment is calculated based on the-generalized Kim model. Single pancake model is simulated and self-field of HTS coil is calculated by Biot-Savart's law. As a result of simulation, quench-initiating spot in the actual HTS coil can be predicted statistically. And that statistical analysis can help detect or protect the quench of the HTS coil.

• Journal of Environmental Health Sciences
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• v.49 no.3
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• pp.149-158
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• 2023

Background: Injury is one of the major health problems in South Korea. Few studies have evaluated both intentional and unintentional injury when investigating the association between exposure to air pollutants and injury. Objectives: We aimed to explore the association between short-term exposure to ambient air pollution and years of life lost (YLLs) due to injury. Methods: Data on daily YLLs for 2002~2019 were obtained from the the Death Statistics Database of the Korean National Statistical Office. This study estimated short-term exposure to particulate matter with an aerodynamic diameter of <10 ㎛ (PM₁₀), particulate matter with an aerodynamic diameter of <2.5 ㎛ (PM_2.5), sulfur dioxide (SO₂), nitrogen dioxide (NO₂), carbon monoxide (CO), and ozone (O₃). This time series study was conducted using a generalized additive model (GAM) assuming a Gaussian distribution. We also evaluated a delayed effect of ambient air pollution by constructing a lag structure up to seven days. The best-fitting lag was selected based on smallest generalized cross validation (GCV) value. To explore effect modification by intentionality of injury (i.e., intentional injury [self-harm, assault] and unintentional injury), we conducted stratified subgroup analyses. Additionally, we stratified unintentional injury by mechanism (traffic accident, fall, etc.). Results: During the study period, the average daily YLLs due to injury was 307.5 years. In the intentional injury, YLLs due to self-harm and assault showed positive association with air pollutants. In the unintentional injury, YLLs due to fall, electric current, fire and poisoning showed positive association with air pollutants, whereas YLLs due to traffic accident, mechanical force and drowning/submersion showed negative associations with air pollutants. Conclusions: Injury is recognized as preventable, and effective strategies to create a safe society are important. Therefore, we need to establish strategies to prevent injury and consider air pollutants in this regard.

• ETRI Journal
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• v.27 no.6
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• pp.747-758
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• 2005

The problem of selection among competing models has been a fundamental issue in statistical data analysis. Good fits to data can be misleading since they can result from properties of the model that have nothing to do with it being a close approximation to the source distribution of interest (for example, overfitting). In this study we focus on the preference among models from a family of polynomial regressors. Three decades of research has spawned a number of plausible techniques for the selection of models, namely, Akaike's Finite Prediction Error (FPE) and Information Criterion (AIC), Schwartz's criterion (SCH), Generalized Cross Validation (GCV), Wallace's Minimum Message Length (MML), Minimum Description Length (MDL), and Vapnik's Structural Risk Minimization (SRM). The fundamental similarity between all these principles is their attempt to define an appropriate balance between the complexity of models and their ability to explain the data. This paper presents an empirical study of the above principles in the context of model selection, where the models under consideration are univariate polynomials. The paper includes a detailed empirical evaluation of the model selection methods on six target functions, with varying sample sizes and added Gaussian noise. The results from the study appear to provide strong evidence in support of the MML- and SRM- based methods over the other standard approaches (FPE, AIC, SCH and GCV).

• Wind and Structures
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• v.35 no.1
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• pp.21-34
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• 2022

Gust factor is an important parameter for the conversion between peak gust wind and mean wind speed used for the structural design and wind-related hazard mitigation. The gust factor of typhoon wind is observed to show a significant dispersion and some differences with large-scale weather systems, e.g., monsoons and extratropical cyclones. In this study, insitu measurement data captured by 13 meteorological towers during a strong typhoon Morakot are collected to investigate the statistical characteristics, height and wind speed dependency of the gust factor. Onshore off-sea and off-land winds are comparatively studied, respectively to characterize the underlying terrain effects on the gust factor. The theoretical method of peak factor based on Gaussian assumption is then introduced to compare the gust factor profiles observed in this study and given in some building codes and standards. The results show that the probability distributions of gust factor for both off-sea winds and off-land winds can be well described using the generalized extreme value (GEV) distribution model. Compared with the off-land winds, the off-sea gust factors are relatively smaller, and the probability distribution is more leptokurtic with longer tails. With the increase of height, especially for off-sea winds, the probability distributions of gust factor are more peaked and right-tailed. The scatters of gust factor decrease with the mean wind speed and height. AS/NZ's suggestions are nearly parallel with the measured gust factor profiles below 80m, while the fitting curve of off-sea data below 120m is more similar to AIJ, ASCE and EU.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.1
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• pp.76-84
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• 2007

The goal of this paper is to improve the watermarking performance using the following two methods; watermark estimation and low density parity check (LDPC) codes. For a blind watermark decoding, the power of a host image, which is hundreds times greater than the watermark power, is the main noise source. Therefore, a technique that can reduce the effect of the power of the host image to the detector is required. To this end, we need to estimate watermark from the watermarked image. In this paper, the watermark estimation is done by an adaptive estimation method with the generalized Gaussian distribution modeling of sub-band coefficients in the wavelet domain. Since the watermark capacity as well as the error rate can be improved by adopting optimum decoding principles and error correcting codes (ECC), we employ the LDPC codes for the decoding of the estimated watermark. Also, in LDPC codes, the knowledge about the noise power can improve the error correction capability. Simulation results demonstrate the superior performance of the proposed algorithm comparing to LDPC decoding with other estimation-based watermarking algorithms.