• Communications for Statistical Applications and Methods
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• 제28권5호
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• pp.511-520
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• 2021

Over the last decade, Bitcoin has attracted a great deal of public interest and Bitcoin market has grown rapidly. One of the main characteristics of the market is that it often undergoes some events or incidents that cause outlying observations. To obtain reliable results in the statistical analysis of Bitcoin data, these outlying observations need to be carefully treated. In this study, we are interested in change point analysis for Bitcoin return series having such outlying observations. Since these outlying observations can affect change point analysis undesirably, we use a robust test for parameter change to locate change points. We report some significant change points that are not detected by the existing tests and demonstrate that the model allowing for parameter changes is better fitted to the data. Finally, we show that the model with parameter change can improve the forecasting performance of Value-at-Risk.

• 품질경영학회지
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• 제25권1호
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• pp.100-115
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• 1997

Shewhart control chart is a basic technique to monitor the state of a process. We observe samples of size four or five and plot some statistic(e.g., mean or range) of each sample on the chart. When setting up the chart, we need to obtain u, pp.r and lower control limits. It is common practice that those limits are calculated from the preliminary 20-40 samples presumed to be homogeneous. However, it may ha, pp.n in practice that the samples are contaminated by outlying observations caused by various reasons. The presence of outlying observations make the control limits wider and hence decrease the sensitivity of the charts. In this paper, we introduce robust control charts with tighter control limits when outlying observations are present in the preliminary samples. Examples will be given via simulation study.

• Communications for Statistical Applications and Methods
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• 제9권1호
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• pp.197-212
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• 2002

When assessing goodness-of-fit of a model, a small subset of deviating observations can give rise to a significant lack of fit. It is therefore important to identify such observations and to assess their effects on various aspects of analysis. A Cook's distance measure is usually used to detect influential observation. But it sometimes is not fully effective in identifying truly influential set of observations because there may exist masking or swamping effects. In this paper we confine our attention to influential subset In GLMs such as logistic regression models and loglinear models. We modify a backwards stepping algorithm, which was originally suggested for detecting outlying cells in contingency tables, to detect influential observations in GLMs. The algorithm consists of two steps, the identification step and the testing step. In identification step we Identify influential observations based on influencial measures such as Cook's distances. On the other hand in testing step we test the subset of identified observations to be significant or not Finally we explain the proposed method through two types of dataset related to logistic regression model and loglinear model, respectively.

• Communications for Statistical Applications and Methods
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• 제16권5호
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• pp.745-751
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• 2009

생물학적 동등성 시험에서 피험자로부터 얻어진 생체 이용률 자료들 중 하나 혹은 일부가 다른 자료에 비해 크게 차이가 나는 자료를 이상치라 정의한다. 이러한 이상치의 존재는 소수의 피험자로 이루어지는 생물학적 동등성 시험의 결과에 큰 영향을 주는 것으로 알려져 있다. 생물학적 생동성 시험에서 이상치를 판별하는 통계적 방법 중 우도거리 혹은 추정량거리를 비교하는 통계적 검정이 많이 활용되고 있는데 본 연구논문에서는 이러한 통계적 방법을 보다 일반화하여 이상치를 판단하는데 보다 효율적인 검정 방법을 제안한다. 제안된 방법은 예제를 통해 자세하게 논의된다.

• Journal of the Korean Statistical Society
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• 제12권1호
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• pp.24-35
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• 1983

In fitting models to data, selection of the most desirable estimation method and determination of the adequacy of fitted model are the central issues. This paper compares the maximum likelihood estimators and the minimum logit chi-square estimators, both being best asymptotically normal, when logit models are fitted to infant mortality data. Chi-square goodness-of-fit test and likelihood ratio one are also compared. The analysis infant mortality data shows that the outlying observations do not necessarily result in the same impact on goodness-of-fit measures.

• Communications for Statistical Applications and Methods
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• 제12권2호
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• pp.413-423
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• 2005

Consider the problem of estimating the underlying regression function from a set of noisy data which is contaminated by a long tailed error distribution. There exist several robust smoothing techniques and these are turned out to be very useful to reduce the influence of outlying observations. However, no matter what kind of robust smoother we use, we should choose the smoothing parameter and relatively less attention has been made for the robust bandwidth selection method. In this paper, we adopt the idea of robust location parameter estimation technique and propose the robust cross validation score functions.

• Communications for Statistical Applications and Methods
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• 제24권6호
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• pp.583-604
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• 2017

The most popular regression model for the analysis of time-to-event data is the Cox proportional hazards model. While the model specifies a parametric relationship between the hazard function and the predictor variables, there is no specification regarding the form of the baseline hazard function. A critical assumption of the Cox model, however, is the proportional hazards assumption: when the predictor variables do not vary over time, the hazard ratio comparing any two observations is constant with respect to time. Therefore, to perform credible estimation and inference, one must first assess whether the proportional hazards assumption is reasonable. As with other regression techniques, it is also essential to examine whether appropriate functional forms of the predictor variables have been used, and whether there are any outlying or influential observations. This article reviews diagnostic methods for assessing goodness-of-fit for the Cox proportional hazards model. We illustrate these methods with a case-study using available R functions, and provide complete R code for a simulated example as a supplement.

• 응용통계연구
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• 제31권1호
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• pp.155-165
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• 2018

특이점 및 영향점은 자료분석을 하는 데 사용되는 수치적 측도들을 왜곡한다. 각종 자료분석에 있어서의 특이점 검색을 위한 검정 통계량이나 그림도구에 관한 연구는 꾸준히 전개되어 왔다. Jang과 Anderson-Cook (Quality and Reliability Engineering International, 30, 1409-1425, 2014)은 불꽃그림이란 이름을 붙인 그림도구를 발표하였는데 특이점이나 영향점이 일변량/이변량 자료분석 및 회귀분석에 어떠한 영향을 미치는지 알기 위하여 3-D 불꽃그림 및 불꽃그림 행렬을 제시하였다. 본 연구에서는 이러한 불꽃그림이 통계적 추론에서 특이점을 찾아내고 이러한 특이점의 영향을 평가하기 위한 탐색적 자료분석 그림도구로서 사용될 수 있음을 보였다.

• 응용통계연구
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• 제27권2호
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• pp.307-315
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• 2014

이상치 후보군을 검정할 때 일반적으로 정확한 검정 통계량의 분포가 존재하지 않는다. 이에 따라 전체 관찰치군에 대한 검정대신 개별 관찰치에 대한 검정을 수행하거나 실험에 의해 계산된 유의값을 사용하여 이상치 가설검정을 수행한다. 본 연구에서는 임의의 관찰치 집단 또는 이상치 탐지절차에 따라 이상치 후보로 탐지된 특정 관찰치 집단의 이상치 여부를 검정하는 방법을 제시한다. 제시된 방법은 기존의 이상치 탐지기법에서 사용되는 검정방법과 모의실험을 통해 검정력을 비교한다.

• Journal of the Korean Statistical Society
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• 제25권1호
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• pp.49-66
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• 1996

The singular value decomposition is one of the most useful methods in the area of matrix computation. It gives dimension reduction which is the centeral idea in many multivariate analyses. But this method is not resistant, i.e., it is very sensitive to small changes in the input data. In this article, we derive the resistant version of singular value decomposition for principal component analysis. And we give its statistical applications to biplot which is similar to principal component analysis in aspects of the dimension reduction of an n x p data matrix. Therefore, we derive the resistant principal component analysis and biplot based on the resistant singular value decomposition. They provide graphical multivariate data analyses relatively little influenced by outlying observations.