• The Journal of the Acoustical Society of Korea
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• v.15 no.6
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• pp.104-109
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• 1996

In passive sonar system, frequency lines generated by underwater target are very important for detection, tracking and classification. In this paper, the extraction method of unstable frequency line from the time samples of the radiated noise of underwater target is studied. As unstable frequency line is time varying, an extended Kalman filter algorithm which is desirable for nonlinear system is applied to extract unstable frequency line. The proposed method shows good extraction of unstable frequency line by application of simulated signal and real target.

• The Korean Journal of Applied Statistics
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• v.9 no.1
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• pp.139-152
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• 1996

In this paper we propose nonparametric tests using optimal weights for umbrella alternatives in a randomized block design. We obtain the optimal weights by maximizing the asymptotic relative efficiency of the proposed test statistics with respect to Mack and Wolf(1981) type test statistic, and investigate asymptotic relative efficiencies of the proposed test statistics using these optimal weights relative to Mack and Wolfe type statistics and linear rank statistic. Throughout simulations for small samples, the proposed test statistic has good powers rather than the other two tests when the block sizes are different.

• Journal of the Korean Data and Information Science Society
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• v.22 no.5
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• pp.839-847
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• 2011

In this study, we derive an estimator based on autocovariance for the regression coefficients vector in the multiple linear regression model. This method is suggested by Park (2009), and although this method does not seem to be intuitively attractive, this estimator is unbiased for the regression coefficients vector. When the vectors of exploratory variables satisfy some regularity conditions, under mild conditions which are satisfied when errors are from autoregressive and moving average models, this estimator has asymptotically the same distribution as the least squares estimator and also converges in probability to the regression coefficients vector. Finally we provide a simulation study that the forementioned theoretical results hold for small sample cases.

• The Korean Journal of Applied Statistics
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• v.29 no.7
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• pp.1311-1327
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• 2016

We propose a multi-state model for analyzing semi-competing risks data with interval-censored or missing intermediate events. This model is an extension of the 'illness-death model', which composes three states, such as 'healthy', 'diseased', and 'dead'. The state of 'diseased' can be considered as an intermediate event. Two more states are added into the illness-death model to describe missing events caused by a loss of follow-up before the end of the study. One of them is a state of 'LTF', representing a lost-to-follow-up, and the other is an unobservable state that represents the intermediate event experienced after LTF occurred. Given covariates, we employ the Cox proportional hazards model with a normal frailty and construct a full likelihood to estimate transition intensities between states in the multi-state model. Marginalization of the full likelihood is completed using the adaptive Gaussian quadrature, and the optimal solution of the regression parameters is achieved through the iterative Newton-Raphson algorithm. Simulation studies are carried out to investigate the finite-sample performance of the proposed estimation procedure in terms of the empirical coverage probability of the true regression parameter. Our proposed method is also illustrated with the dataset adapted from Helmer et al. (2001).

• Journal of the Korean Data and Information Science Society
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• v.28 no.3
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• pp.515-524
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• 2017

In this study, a measure of discrepancy based on MV (margin of victory) has been suggested that might be useful in determining the size of random forest for classification. Here MV is a scaled difference in the votes, at infinite random forest, of two most popular classes of current random forest. More specifically, max(-MV,0) is proposed as a reasonable measure of discrepancy by noting that negative MV values mean a discrepancy in two most popular classes between the current and infinite random forests. We propose an appropriate diagnostic statistic based on this measure that might be useful for the determination of random forest size, and then we derive its asymptotic distribution. Finally, a simulation study has been conducted to compare the performances, in finite samples, between this proposed statistic and other recently proposed diagnostic statistics.

• The Korean Journal of Applied Statistics
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• v.30 no.4
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• pp.539-553
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• 2017

We propose a multi-state model to analyze semi-competing risks data with interval-censored or missing intermediate events. This model is an extension of the three states of the illness-death model: healthy, disease, and dead. The 'diseased' state can be considered as the intermediate event. Two more states are added into the illness-death model to incorporate the missing events, which are caused by a loss of follow-up before the end of a study. One of them is a state of the lost-to-follow-up (LTF), and the other is an unobservable state that represents an intermediate event experienced after the occurrence of LTF. Given covariates, we employ the Lin and Ying additive hazards model with log-normal frailty and construct a conditional likelihood to estimate transition intensities between states in the multi-state model. A marginalization of the full likelihood is completed using adaptive importance sampling, and the optimal solution of the regression parameters is achieved through an iterative quasi-Newton algorithm. Simulation studies are performed to investigate the finite-sample performance of the proposed estimation method in terms of empirical coverage probability of true regression parameters. Our proposed method is also illustrated with a dataset adapted from Helmer et al. (2001).

• Journal of Korea Water Resources Association
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• v.55 no.3
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• pp.177-189
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• 2022

The purpose of this study is to apply the d4PDF (Data for Policy Decision Making for Future Change) constructed from a large-scale ensemble climate simulation to estimate the probable rainfall with low frequency and high intensity. In addition, this study analyzes the uncertainty caused by the application of the frequency analysis by comparing the probable rainfall estimated using the d4PDF with that estimated using the observed data and frequency analysis at Geunsam, Imsil, Jeonju, and Jangsu stations. The d4PDF data consists of a total of 50 ensembles, and one ensemble provides climate and weather data for 60 years such as rainfall and temperature. Thus, it was possible to collect 3,000 annual maximum daily rainfall for each station. By using these characteristics, this study does not apply the frequency analysis for estimating the probability rainfall, and we estimated the probability rainfall with a return period of 10 to 1000 years by distributing 3,000 rainfall by the magnitude based on a non-parametric approach. Then, the estimated probability rainfall using d4PDF was compared with those estimated using the Gumbel or GEV distribution and the observed rainfall, and the deviation between two probability rainfall was estimated. As a result, this deviation increased as the difference between the return period and the observation period increased. Meanwhile, the d4PDF reasonably suggested the probability rainfall with a low frequency and high intensity by minimizing the uncertainty occurred by applying the frequency analysis and the observed data with the short data period.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.39 no.3
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• pp.201-210
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• 2002

This paper describes a 3.3 (V) low power 4 digit CMOS quaternary to analog converter (QAC) designed with a neuron MOS($\upsilon$MOS) down literal circuit block and cascode current mirror source block. The neuron MOS down literal architecture allows the designed QAC to accept not only 4 level voltage inputs, but also a high speed sampling rate quaternary voltage source LSB. Fast settling time and low power consumption of the QAC are achieved by utilizing the proposed architecture. The simulation results of the designed 4 digit QAC show a sampling rate of 6(MHz) and a power dissipation of 24.5 (mW) with a single power supply of 3.3 (V) for a CMOS 0.35${\mu}{\textrm}{m}$ n-well technology.

• Journal of the Korean Data and Information Science Society
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• v.27 no.4
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• pp.959-967
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• 2016

Distributional assumptions on equity returns play a key role in valuation theories for derivative securities. Elberlein and Keller (1995) investigated the distributional form of compound returns and found that some of standard assumptions can not be justified. Instead, Generalized Hyperbolic (GH) distribution fit the empirical returns with high accuracy. Hu and Kercheval (2007) also show that the normal distribution leads to VaR (Value at Risk) estimate that significantly underestimate the realized empirical values, while the GH distributions do not. We consider saddlepoint approximations to estimate the VaR and the ES (Expected Shortfall) which frequently encountered in finance and insurance as measures of risk management. We supposed GH distributions instead of normal ones, as underlying distribution of linear portfolios. Simulation results show the saddlepoint approximations are very accurate than normal ones.

• Journal of the Korean Data and Information Science Society
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• v.25 no.6
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• pp.1499-1506
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• 2014

Random walk is used for describing random phenomenon in various areas but tests for random walk developed so far are known to suffer from size distortion and low power. Kim et al. (2014) proposed a sign test for unit root (${\rho}=1$) hypothesis based on slopes. This article proposes a Wilcoxon signed rank test based on slopes for unit root hypothesis, and compares it with the augmented Dickey-Fuller test and the sign test by a simulation study. Our results confirm that the nonparametric tests are better than ADF test for small samples like n = 30. The results also show that the sign test is better than the Wilcoxon signed rank test and that for 0 < ${\rho}$ < 1 (-1 < ${\rho}$ < 0), the nonparametric tests suffer from power loss (improvement) as normal error changes to double exponential error.