• Communications for Statistical Applications and Methods
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• v.4 no.2
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• pp.377-384
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• 1997

본 논문은 지분구조를 갖는 범주형 자료가 명목상의 다가자료일 때, 지분구조의 각 단계에서 정의될 수 있는 지분변수들의 유형과 지분변수들의 관심확률들에 영향을 미치는 변수들을 고려한 자료분석 모형들을 제시하고 있다.

• 한국데이터정보과학회:학술대회논문집
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• 2004.04a
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• pp.183-194
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• 2004

본 연구에서는 공간 검색 통계량(spatial scan statistics)과 에셜론 해석법을 이용한 범주형 자료분석을 다룬다. 이를 위해 우선, 에셜론 덴드로그램을 이용하여 주어진 분활표의 계층적 구조(hierarchical structure)를 결정하고서 이로부터 핫스팟(hotspot)의 후보를 검출한다. 다음으로 우도비(likelihood ratio)를 기초로 유의하게 높거나 낮게 나타나는 지역에 대한 공간 검색 통계량을 산출한다. 마지막으로, 이 통계량을 바탕으로 핫스팟을 검출한다.

• The Korean Journal of Applied Statistics
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• v.23 no.4
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• pp.683-692
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• 2010

This short article is concerned with a categorical time series obtained after clipping a heteroscedastic GARCH process. Estimation methods are discussed for the model parameters appearing both in the original process and in the resulting binary time series from a clipping (cf. Zhen and Basawa, 2009). Assuming AR-GARCH model for heteroscedastic time series, three data sets from Korean stock market are analyzed and illustrated with applications to calculating certain probabilities associated with the AR-GARCH process.

• The Korean Journal of Applied Statistics
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• v.14 no.1
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• pp.39-57
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• 2001

Categorical data has sometimes misclassification errors. If this data will be analyzed, then estimated cell probabilities could be biased and the standard Pearson X2 tests may have inflated true type I error rates. On the other hand, if we regard wellclassified data with misclassified one, then we might spend lots of cost and time on adjustment of misclassification. It is a necessary and important step to ask whether categorical data is misclassified before analyzing data. In this paper, when data is misclassified at one of two variables for two-dimensional contingency table and marginal sums of a well-classified variable are fixed. We explore to partition marginal sums into each cells via the concepts of Bound and Collapse of Sebastiani and Ramoni (1997). The double sampling scheme (Tenenbein 1970) is used to obtain informations of misclassification. We propose test statistics in order to solve misclassification problems and examine behaviors of the statistics by simulation studies.

• The Korean Journal of Applied Statistics
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• v.31 no.1
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• pp.67-75
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• 2018

Multidimensional scaling (MDS) is an exploratory analysis of multivariate data to represent the dissimilarity among objects in the geometric low-dimensional space. However, a general MDS map only shows the information of objects without any information about variables. In this study, we used MDS based on the algorithm of Torgerson (Theory and Methods of Scaling, Wiley, 1958) to visualize some clusters of objects in categorical data. For this, we convert given data into a multiple indicator matrix. Additionally, we added the information of levels for each categorical variable on the MDS map by applying the partition method of Shin et al. (Korean Journal of Applied Statistics, 28, 1171-1180, 2015). Therefore, we can find information on the similarity among objects as well as find associations among categorical variables using the proposed MDS map.

• The Korean Journal of Applied Statistics
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• v.11 no.1
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• pp.83-95
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• 1998

Statistical models are proposed for analyzing categorical data in the presence of missing observations or nonresponses which might occur in the sampling surveys and polls. As an illustration, we analyzed real polling data of the pre-presidential election in the USA, 1948, It had been predicted that Dewey would win the election. However, Truman won in the actual election.

• The Korean Journal of Applied Statistics
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• v.15 no.2
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• pp.381-393
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• 2002

K-modes algorithm and ROCK(RObust Clustering using linKs) algorithm we useful clustering methods for large categorical data. In the paper, we investigate these algorithms and propose improved algorithms of them to correct their weakness. A simulation study shows that the proposed algorithms could increase the performance of data clustering.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1999.04a
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• pp.228-228
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• 1999

• The Korean Journal of Applied Statistics
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• v.12 no.2
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• pp.433-447
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• 1999

검사관련 능력과 문항점수사이의 관계를 모형화하기 위해 사용한 순환모형에서 관측불능인 능력상대변수를 비롯한 모든 변수들이 범주형 변수라 가정하자. 이 범주형 자료를 위한 모수추정문제를 다루기 위해 EM 방법을 이용했는데, EM 방법은 사용하기에 편리하지만 순환모형에 대한 추정값이 적절하지 않는 경우가 발생한다. 그 주된 원인중의 하나로 초기값 선정의 잘못을 들 수 있는데, 본 논문에서는 이 외에 구조상의 결함도 그 원인이 됨을 경험적으로 보았다. 따라서 구조적 결함을 먼저 해결하면 보다 효과적인 초기값을 선정할 수 있으리가 기대한다.

• The Korean Journal of Applied Statistics
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• v.19 no.2
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• pp.271-282
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• 2006

The aim of this study is to propose a tree-structured clustering for mixed data. We suggest a scaling method to reduce the variable selection bias among categorical variables. In numerical examples such as credit data, German credit data, we note several differences between tree-structured clustering and K-means clustering.