• The Korean Journal of Applied Statistics
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• v.6 no.2
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• pp.227-235
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• 1993

Calibration relates the estimation of independent variable which rquires more effort or expense than dependent variable does. It would be provided with high accuracy because a little change of the result of independent variable cn cause a serious effect to the human being. Usual statistical analysis assumes the normality of error distribution or linearity of data. It is desirable to analyze the data without those assumptions for the accuracy of the calibration. In this paper, we calibrated the data nonparametrically without those assumptions and derived confidence interval estimate for the independent variable. As a method, we used kernel method which is popular in modern statistical branch. We derived bootstrap confidence interval estimate from the bootstrap confidence band.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.36 no.11
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• pp.1479-1485
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• 2012

The development of a technique for the real-time sensing of a curve section is very important for active rolling-stocks in order to improve the curving performance. However, conventional methods using expensive track inspection equipment or various complex sensors are not practicable to be applied to commercial vehicles. Therefore, we have proposed a new method to estimate the curve radius of a curve section. This method uses the relative displacements occurring between the body and the bogie when the rolling-stock is running on a curve. To verify the validity of this method, we conducted a vehicle dynamics simulation and test using a real vehicle on a test line. The results confirmed the validity of the proposed method. We expect that this method will be effectively applied in studies of active rolling-stocks to increase the curving performance using active control technology.

• The Korean Journal of Applied Statistics
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• v.15 no.2
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• pp.355-366
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• 2002

Nonparametric statistical inference for the parameter of logit model were examined. Usually nonparametric approach is milder than parametric approach based on normal theory assumption. We compared the two nonparametric methods for legit model, the bootstrap and random permutation in the sense of coverage probability. Monte Carlo simulation is conducted for small sample cases. Empirical power of hypothesis test and coverage probability for confidence interval estimation were presented for simple and multiple legit model respectively. An example were also introduced.

• The Korean Journal of Applied Statistics
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• v.19 no.2
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• pp.349-360
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• 2006

PROC MIXED in SAS can be utilized to make inferences on parameters in a mixed model by use of Restricted Maximum Likelihood Estimation Method or Maximum Likelihood Estimation Method which has more merits than ANOVA method. A regression model with unbalanced nested error structure that belongs to a mixed model is used to construct confidence intervals on variances among groups, within groups, and regression coefficients in the model. PROC MIXED is applied to three different sample sizes for simulation. As a result of the simulation study, PROC MIXED generates confidence intervals on parameters that maintain the stated confidence coefficient in a large sample size. However, it does not generate confidence intervals that maintain the stated confidence coefficient for variance components among groups and intercept in a small sample size.

• Journal of the Korean Data and Information Science Society
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• v.5 no.2
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• pp.45-57
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• 1994

이 연구에서는 Hjort(1991)에의해 제안된 누적위험률함수의 비모수적 추정량을 이용하여 무한인 구간까지 정의된 평균잔여수명함수의 추정량을 제안하고 제안된 추정량의 일치성과 점근적 정규성을 밝히고, 모의실험을 통하여 다른 추정량들과 비교하고자 한다.

• Journal of Korean Society of Transportation
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• v.17 no.5
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• pp.137-149
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• 1999

For decades, many traffic flow studies on the analysis and determination of level of service (LOS) for the weaving sections have been made to Provide several regression equations. Weaving and non-weaving speeds were dependent variables for the equations, with independent variables being weaving length, number of lanes, and weaving ratios. One of the difficulties in developing the equations was that the weaving areas were rare in Korea, so the statistical analyses for calibrating the equation parameter could not be performed in a desirable manner. In this regard, a new and stochastic methodology for predicting the weaving and non-weaving speeds within the weaving sections was required. In this study the following design variables were developed; influence area of the weaving section. headway distribution within the weaving section, maximum weaving volume of the weaving section, length of the ideal weaving section, and speed estimations for the weaving and non-weaving flows. The evaluation of the new model was made comparing the delay in the weaving section with the one in the freeway basic section.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.8
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• pp.708-715
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• 2014

In this paper, we suggest a method of realtime confidence interval estimation to detect abnormal states of sensor data. For realtime confidence interval estimation, the mean square errors of the exponential smoothing method and moving average method, two of the time series analysis method, where compared, and the moving average method with less errors was applied. When the sensor data passes the bounds of the confidence interval estimation, the administrator is notified through alarming. As the suggested method is for realtime anomaly detection in a ship, an Android terminal was adopted for better communication between the wireless sensor network and users. For safe navigation, an administrator can make decisions promptly and accurately upon emergency situation in a ship by referring to the anomaly detection information through realtime confidence interval estimation.

• Proceedings of the KAIS Fall Conference
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• 2010.05a
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• pp.119-123
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• 2010

본 논문에서는 2단계 잡음제거 방법의 이득함수를 이용한 고조파 복원 잡음제거 방법의 이득함수를 조정하여 기존의 방법보다 음성개선을 향상시켰고, 제안한 방법으로 개선된 음성을 음성인식 기술에 적용하였다. 본 논문에서는 기존 방법으로 음성개선 결과 묵음구간에서 음성구간으로 변화는 구간에서 이전 프레임의 추정된 음성신호로 스펙트럼의 이득함수가 구해져서 음성이 발생하는 구간에서 왜곡이 발생한다. 따라서 본 논문에서는 이러한 현상을 개선시키기 위해 2단계 잡음제거 방법의 이득함수를 추정된 a priori SNR과 비교하여 이득함수를 조정하고, 2단계 잡음제거 방법의 이득함수를 고조파 복원 방법의 이득함수와 비교하여 이득함수를 조정하여 음성을 개선하는 방법을 제안하였다. 그리고 음성인식을 위한 특징벡터 추출을 위해 제안한 방법으로 개선된 음성의 대수 에너지를 정규화 하는 대수 에너지 정규화 방법(Log Energy Normalization)을 음성인식 방법에 적용하였다.

• Communications for Statistical Applications and Methods
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• v.18 no.4
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• pp.445-455
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• 2011

Although misclassified binary data occur frequently in practice, the statistical methodology available for the data is rather limited. In particular, the interval estimation of population proportion has relied on the classical Wald method. Recently, Lee and Choi (2009) developed a new confidence interval by applying the Agresti-Coull's approach and showed the efficiency of their proposed confidence interval numerically, but a theoretical justification has not been explored yet. Therefore, a Bayesian model for the misclassified binary data is developed to consider the Agresti-Coull confidence interval from a theoretical point of view. It is shown that the Agresti-Coull confidence interval is essentially a Bayesian confidence interval.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.33 no.6
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• pp.333-344
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• 2021

Technique for the long-gap filling that occur frequently in ocean monitoring data is developed. The method estimates the unknown values of the long-gap by the summation of the estimated trend and selected residual components of the given missing intervals. The method was used to impute the data of the long-term missing interval of about 1 month, such as temperature and water temperature of the Ulleungdo ocean buoy data. The imputed data showed differences depending on the monitoring parameters, but it was found that the variation pattern was appropriately reproduced. Although this method causes bias and variance errors due to trend and residual components estimation, it was found that the bias error of statistical measure estimation due to long-term missing is greatly reduced. The mean, and the 90% confidence intervals of the gap-filling model's RMS errors are 0.93 and 0.35~1.95, respectively.