• Journal of the Korean Institute of Telematics and Electronics A
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• v.33A no.2
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• pp.205-216
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• 1996

In this paper we propose an efficient robust path delay fault test genration algorithm for detection of path delay faluts in combinational ligic circuits. In the proposed robust test genration approach, the testability measure is computed for all gates in the circuit under test and these computed values are used to genrate weighted random delay test vetors for detection of path delay faults. For genrated robust test vectors, we perform fault simulation on ISCAS '85 benchmark circuits using parallel pattern technqieus. The results indicate that the proposed test genration method not only increases the number of detected robust path delay faults but also reduces the time taen to genrate robust tests.

• Journal of the Korean Data and Information Science Society
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• v.11 no.2
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• pp.201-215
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• 2000

The classical method for regression analysis is the least squares method. However, if the data contain significant outliers, the least squares estimator can be broken down by outliers. To remedy this problem, the robust methods are important complement to the least squares method. Robust methods down weighs or completely ignore the outliers. This is not always best because the outliers can contain some very important information about the population. If they can be detected, the outliers can be further inspected and appropriate action can be taken based on the results. In this paper, I propose a sequential outlier test to identify outliers. It is based on the nonrobust estimate and the robust estimate of scatter of a robust regression residuals and is applied in forward procedure, removing the most extreme data at each step, until the test fails to detect outliers. Unlike other forward procedures, the present one is unaffected by swamping or masking effects because the statistics is based on the robust regression residuals. I show the asymptotic distribution of the test statistics and apply the test to several real data and simulated data for the test to be shown to perform fairly well.

• Transactions of the Korean Society of Automotive Engineers
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• v.12 no.2
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• pp.131-138
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• 2004

In the proto design stage of a new car, the performances of an occupant protection system can be evaluated by CAE even though the real test should be carried out. The number of the real test is reduced by the exact predictions followed by the appropriate design recommendation. However, the existing researches using CAE in predicting the performances do not consider the uncertainties of parameters. That often leads to inconsistency between test and CAE. In this research, the robust design of a protection system such as airbag and load limiter is suggested considering the frontal crash. The parameter design scheme of the Taguchi method is introduced to obtain the robust design of arbitrary airbag and load limiter. It is performed based on the frontal crash test condition of US-NCAP with an arbitrary passenger car. The variances of the performances such as HIC, chest acceleration and probability of combined injury are calculated by the outer array and the Taylor series expansion. Through the analysis of the Taguchi method, the robust optimum is determined.

• Journal of the Korean Data and Information Science Society
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• v.26 no.6
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• pp.1565-1572
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• 2015

In this paper, we analyze the polio incidence data based on the Poisson autoregressive models, focusing particularly on change-point detection. Since the data include some strongly deviating observations, we employ the robust cumulative sum (CUSUM) test proposed by Kang and Song (2015) to perform the test for parameter change. Contrary to the result of Kang and Lee (2014), our data analysis indicates that there is no significant change in the case of the CUSUM test with strong robustness and the same result is obtained after ridding the polio data of outliers. We additionally consider the comparison of the forecasting performance. All the results demonstrate that the robust CUSUM test performs adequately in the presence of seemingly outliers.

• Journal of the Korean Data and Information Science Society
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• v.15 no.2
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• pp.441-447
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• 2004

Li and Mak (1994) developed a test statistic for detecting the non-linearity and the heteroscedasticity of the time series data. But it is well known that the test statistic may be very sensitive in case of heavy-tailed distributions of the errors. Jiang et al.(2001) suggested the robust method for ARCH models but the calculation procedures for the estimation are very complicated. We suggested the robust method based on Huber's function and our method works quite well rater than the Li and Mak(1994). Also our method is relatively easy to calculate the test statistic.

• Communications for Statistical Applications and Methods
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• v.6 no.3
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• pp.967-976
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• 1999

In this paper we study the Hellinger distance based goodness-of-fit tests that are analogs of likelihood ratio tests. The minimum Hellinger distance estimator (MHDE) in normal models provides an excellent robust alternative to the usual maximum likelihood estimator. Our simulation results show that the Hellinger deviance test (Simpson 1989) based goodness-of-fit test is robust when data contain outliers. The proposed hellinger deviance test(Simpson 1989) is a more direcct method for obtaining robust inferences than an automated outlier screen method used before the likelihood ratio test data analysis.

• Journal of the Korean Statistical Society
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• v.36 no.1
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• pp.149-156
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• 2007

This study suggests a new method for testing seasonal unit roots in a momentum threshold autoregressive (MTAR) process. This sign test is robust against heteroscedastic or heavy tailed errors and is invariant to monotone data transformation. The proposed test is a seasonal extension of the sign test of Park and Shin (2006). In the case of partial seasonal unit root in an MTAR model, a Monte-Carlo study shows that the proposed test has better power than the seasonal sign test developed for AR model.

• ETRI Journal
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• v.28 no.4
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• pp.502-505
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• 2006

A new class-based histogram equalization method is proposed for robust speech recognition. The proposed method aims at not only compensating the acoustic mismatch between training and test environments, but also at reducing the discrepancy between the phonetic distributions of training and test speech data. The algorithm utilizes multiple class-specific reference and test cumulative distribution functions, classifies the noisy test features into their corresponding classes, and equalizes the features by using their corresponding class-specific reference and test distributions. Experiments on the Aurora 2 database proved the effectiveness of the proposed method by reducing relative errors by 18.74%, 17.52%, and 23.45% over the conventional histogram equalization method and by 59.43%, 66.00%, and 50.50% over mel-cepstral-based features for test sets A, B, and C, respectively.

• Communications for Statistical Applications and Methods
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• v.25 no.5
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• pp.559-568
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• 2018

Gene classification can involve complex order-restricted inference. Examining gene expression pattern across groups with order-restriction makes standard statistical inference ineffective and thus, requires different methods. For this problem, Roy's union-intersection principle has some merit. The M-estimator adjusting for outlier arrays in a microarray study produces a robust test statistic with distribution-insensitive clustering of genes. The M-estimator in conjunction with a union-intersection principle provides a nonstandard robust procedure. By exact permutation distribution theory, a conditionally distribution-free test based on the proposed test statistic generates corresponding p-values in a small sample size setup. We apply a false discovery rate (FDR) as a multiple testing procedure to p-values in simulated data and real microarray data. FDR procedure for proposed test statistics controls the FDR at all levels of ${\alpha}$ and ${\pi}_0$ (the proportion of true null); however, the FDR procedure for test statistics based upon normal theory (ANOVA) fails to control FDR.

• Proceedings of the Korean Statistical Society Conference
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• 2003.05a
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• pp.287-293
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• 2003

In this paper we consider the problem of parameter change based on the cusum test proposed by Lee et al. (2003). The cusum test statistic is constructed utilizing the estimator minimizing density-based divergence measures. It is shown that under regularity conditions, the test statistic has the limiting distribution of the sup of standard Brownian bridge. Simulation results demonstrate that the cusum test is robust when there arc outliers.