• The Korean Journal of Applied Statistics
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• v.32 no.1
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• pp.99-109
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• 2019

We compared the confidence intervals of estimators using various bootstrap methods for a Random Coefficient Autoregressive(RCA) model. We consider a Quasi score estimator and M-Quasi score estimator using Huber, Tukey, Andrew and Hempel functions as bounded functions, that do not have required assumption of distribution. A standard bootstrap method, percentile bootstrap method, studentized bootstrap method and hybrid bootstrap method were proposed for the estimations, respectively. In a simulation study, we compared the asymptotic confidence intervals of the Quasi score and M-Quasi score estimator with the bootstrap confidence intervals using the four bootstrap methods when the underlying distribution of the error term of the RCA model follows the normal distribution, the contaminated normal distribution and the double exponential distribution, respectively.

• Communications for Statistical Applications and Methods
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• v.3 no.3
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• pp.11-23
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• 1996

본 연구에서는 선형회귀분석에서 Hadi와 Simonoff의 다중이상점 식별방법을 수정하여 새로운 알고리즘을 제시하였다. Hadi와 Simonoff의 알고리즘 첫 단계에서 이상점일 가능성이 없는 점들의 집합을 추출할 때 가장효과와 편승효과에 영향을 받을 수 있음으로, 이 첫 단계를 수정하였다. 우리는 잔차가 일정한 분산을 갖는 정규분포에 다르다는 가정하에서 잔차의 신뢰구간을 생각하고, 이 구간안에서 잔차의 MAD가 최소인 새로운 모형을 탐색하고, 이를 이상점일 가능성이 없는 점들의 집합을 추출하는데 일용하는 새로운 알로리즘을 제시하였다. 제시된 방법은 실제자료에서 다른 방법에 비해 효율적으로 이상점을 식별할 수 있었다.

• Proceedings of the Korea Multimedia Society Conference
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• 2003.05b
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• pp.362-365
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• 2003

본 논문은 DCT기반에서 AC 및 DC 성분에 워터마크를 삽입하는 워터마킹 방법을 제안하였다. DCT는 원영상을 8×8 블록으로 나누어서 행하였으며, 삽입한 워터마크 데이터는 평균 0, 분산 1, 길이가 1000인 정규분포 랜덤시퀀스를 사용하였다. 이 워터마크 데이터의 크기에 따라 DCT의 DC 및 AC 성분에 삽입한다. 제안한 워터마킹 알고리듬의 성능을 확인하기 위해서 컴퓨터 시뮬레이션을 수행한 결과, 제안 방법이 기존의 워터마크 방법보다 비가시성 및 견고성 면에서 우수함을 확인하였다

• The Korean Journal of Applied Statistics
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• v.15 no.2
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• pp.223-232
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• 2002

The present paper develops a test of the multivariate normality based on nonlinear transformations and the likelihood function. For checking the normality, we test the shape parameter which indexes the family of transformations. A score test and a parametric bootstrap test are used to evaluate the discrepancy between the data and a multivariate normal distribution. In order to compare the performance of our test with the existing tests, a simulation study was carried out for several situations where nuisance parameters have to be estimated. The results showed that the proposed method is superior to the existing methods.

• Korean Journal of Food Science and Technology
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• v.22 no.3
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• pp.300-306
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• 1990

The microstrutural changes of mayonnaise during storage were examined by light microscopy (LM) and scanning electron microscopy (SEM). Fresh mayonnaise was composed of heterogenous population of dispersed spherical oil droplets and droplet size was normally distributed with one mode. During storage at $60^{\circ}C\;and\;-10^{\circ}C$, a shift in droplet size distribution toward larger droplets was observed, as a result of coalescence of lipid droplets. Turbidimetric study also confirmed that coalescence was occurring during this accelerated aging treatments. Measurements obtained from SEM microgrphs provided better determination of smaller droplets and resulted in lower mean diameter of droplets than those obtained from LM. From these results, SEM was found to be an advantageous method of examining emulsion products as compared to LM, providing a better resolution of small droplets and a more representative view of droplet distribution, as dilution of the sample was avoided.

• The Journal of the Acoustical Society of Korea
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• v.16 no.2
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• pp.71-79
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• 1997

In this paper, a codeword-dependent distance normalization(CDDN) and an instar-formed fuzzy smoothing of output distribution are proposed for robust estimation of output probabilities in the FVQ(fuzzy vector quantization)-DHMM(discrete hidden Markov model). The FVQ-DHMM is a variant of DHMM in which the state output probability is estimated by the sum oft he product of the output probability and its weighting factor for each codeword on an input vector. As the performance of the FVQ-DHMM is influenced by weighting factor and output distribution from a state, it is required to get a method to get robust estimation of weighting factors and output distribution for each state. From experimental results, the proposed CDDN method has reduced 24% of error rate over the conventional FVQ-DHMM, and also reduced 79% of error rate when the smoothing of output distribution is also applied to the computation of an output probability. These results indicate that the use of CDDN and the fuzzy smoothing of output distribution to the FVQ-DHMM lead to improved recognition, and therefore it may be used as an alternative to the robust estimation of output probabilities for HMMs.

• 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.

• Transactions of the Korean Society of Mechanical Engineers
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• v.12 no.4
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• pp.790-805
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• 1988

Static strength and fatigue life scattering of glass fiber reinforced epoxy composite materials has been studied. Normal, lognormal, two-parameter and three-parameter Weibull distribution functions are used for strength and one-stress fatigue life distribution. The value of mean fatigue life is analysed using mean fatigue life, mean log fatigue life and expected value of 2 and 3-parameter Weibull distribution functions. Modification on non-statistical cumulative damage models is made in order to interpret the result of two-stress level fatigue life scattering. The comparison results show that 3-parameter Weibull distribution has better predictions in static strength and one-stress level fatigue life distributions. However, no advantage of 3-parameter Weibll distribution is found over 2-parameter Weibull distribution in two-stress level fatigue life predictions. It is found that two-stress level fatigue life prediction by the expanded equal rank assumption is close to the experimental data.

• The Korean Journal of Applied Statistics
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• v.26 no.6
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• pp.903-913
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• 2013

Social network services generate a considerable amount of social data every day on personal feelings or thoughts. This social data provides changing patterns of information production and consumption but are also a tool that reflects social phenomenon. We analyze negative emotional words from daily blogs to detect the change in blooger sentiment using multivariate control charts. We used the all the blogs produced between 1 January 2008 and 31 December 2009. Hotelling's T-square control chart control chart is commonly used to monitor multivariate quality characteristics; however, it assumes that quality characteristics follow multivariate normal distribution. The performance of a multivariate control chart is affected by this assumption; consequently, we introduce the support vector data description and its extension (K-control chart) suggested by Sun and Tsung (2003) and they are applied to detect the chage in blogger sentiment.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.1
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• pp.90-95
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• 2009

This paper presents a new feature extraction algorithm which can deal with the problems of linear discriminant analysis, widely used for linear dimensionality reduction. The scatter matrices included in linear discriminant analysis are defined by the distances between each datum and its class mean, and those between class means and mean of whole data. Use of these scatter matrices can cause computational problems and the limitation on the number of features. In addition, these definition assumes that the data distribution is unimodal and normal, for the cases not satisfying this assumption the appropriate features are not achieved. In this paper we define a new scatter matrix which is based on the differently weighted distances between individual data, and presents a feature extraction algorithm using this scatter matrix. With this new method. the mentioned problems of linear discriminant analysis can be avoided, and the features appropriate for discriminating data can be achieved. The performance of this new method is shown by experiments.