• Journal of the Korean Data and Information Science Society
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• 제6권2호
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• pp.9-15
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• 1995

In discriminant analysis the procedures commonly used to estimate the dimensionality involve testing a sequence of dimensionality hypotheses. There is a problem with the size of the test since dimensionality hypotheses are tested sequentially and thus they are actually conditional tests. The focus of this paper is "How is the size of the test affected by viewing this sequence of tests as conditional tests?".

• Communications for Statistical Applications and Methods
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• 제3권3호
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• pp.25-30
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• 1996

In discriminant analysis the procedures commonly used to estimate the dimensionality involve testing a sequence of dimensionality hypotheses. There is a problem with the size of the test since dimensionality hypotheses are tested sequentially and thus they are actually conditional tests. The focus of this paper is to investigate in asymptotic sense what happens to the sequential testing procedure if the assumption of normality does not hold.

• 한국수학교육학회지시리즈B:순수및응용수학
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• 제3권2호
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• pp.141-154
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• 1996

A data-driven index of dimensionality for an educational or psychological test - DETECT, short for Dimensionality Evaluation To Enumerate Contributing Traits, is proposed in this paper. It is based on estimated conditional covariances of item pairs, given score on remaining test items. Its purpose is to detect whatever multidimensionality structure exists, especially in the case of approximate simple structure. It does so by assigning items to relatively dimensionally homogeneous clusters via attempted maximization of the DETECT over all possible item cluster partitions. The performance of DETECT is studied through real and simulated data analyses.

• Communications for Statistical Applications and Methods
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• 제13권3호
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• pp.467-477
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• 2006

A multidimensionality detecting procedure DETECT, based on conditional covariances between items, is extended and refined to deal with polytomous item data as well as binary one. A large body of simulation study shows extraordinary performance of DETECT in both enumerating degrees of multidimensionality in a test and discovering dimensionally distinctive item clusters. Real data study also provides very meaningful results, making DETECT a strong dimensionality assessment tool for the test data analysis.

• Communications for Statistical Applications and Methods
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• 제7권2호
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• pp.597-603
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• 2000

A versatile dimensionality assessment index DETECT has been developed for binary item response data by Kim (1994). The present paper extends the use of DETECT to the polytomously scored item data. A simulation study shows DETECT performs well in differentiating multidimensional data from unidimensional one by yielding a greater value of DETECT in the case of multidimensionality. An additional investigation is necessary for the dimensionally meaningful clustering methods, such as HAC for binary data, particularly sensitive to the polytomous data.

• Communications for Statistical Applications and Methods
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• 제11권1호
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• pp.49-58
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• 2004

In this talk we proposed an asymptotic test for dimensionality in the latent variable model for probabilistic principal component analysis with missing values at random. Proposed algorithm is a sequential likelihood ratio test for an appropriate Normal latent variable model for the principal component analysis. Modified EM-algorithm is used to find MLE for the model parameters. Results from simulations and real data sets give us promising evidences that the proposed method is useful in finding necessary number of components in the principal component analysis with missing values at random.

• 응용통계연구
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• 제18권2호
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• pp.381-393
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• 2005

회귀모형에서 필요한 설명변수들의 선형결합들을 탐색하기 위한 방법 중의 하나로 분할역회귀모형을 들 수 있다. 이러한 분할역회귀모형에서 모형에 필요한 설명변수들의 선형결합의 수, 즉 차원을 결정하기 위한 여러 가지의 검정법들이 소개 되었으나 설명변수들의 정규성 가정을 필요로 하거나 다른 제약이 있다. 본 논문에서는 주성분분석에 대한 확률모형을 이 용하여 정규성가정을 필요로하지 않으며 분할의 수에 로버스트한 검정법을 소개하고 모의실험과 실제자료에 대한 적용결과를 통하여 기존의 검정법과 비교하였다.

• Genomics & Informatics
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• 제14권4호
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• pp.166-172
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• 2016

Although a large number of genetic variants have been identified to be associated with common diseases through genome-wide association studies, there still exits limitations in explaining the missing heritability. One approach to solving this missing heritability problem is to investigate gene-gene interactions, rather than a single-locus approach. For gene-gene interaction analysis, the multifactor dimensionality reduction (MDR) method has been widely applied, since the constructive induction algorithm of MDR efficiently reduces high-order dimensions into one dimension by classifying multi-level genotypes into high- and low-risk groups. The MDR method has been extended to various phenotypes and has been improved to provide a significance test for gene-gene interactions. In this paper, we propose a simple method, called accelerated failure time (AFT) UM-MDR, in which the idea of a unified model-based MDR is extended to the survival phenotype by incorporating AFT-MDR into the classification step. The proposed AFT UM-MDR method is compared with AFT-MDR through simulation studies, and a short discussion is given.

• 한국의류학회지
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• 제36권6호
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• pp.601-615
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• 2012

In this study, two representative measures in the job-focused approach and the employee-focused approach of emotional labor are explored to examine dimensionality and the predictability of each emotional labor measure on key consequences that include job stress, customer orientation, and job satisfaction. Data obtained from 193 department store apparel saleswomen were submitted for analysis. The results show that the emotional dissonance and emotional effort of the Kruml and Geddes measure are good predictors for job stress, customer orientation, and job satisfaction. In a test of the Davies measure, job stress is predicted by emotional dissonance and frequency while customer orientation is predicted by duration, variety, and the frequency of emotional expression in jobs. Duration is also a key predictor for job satisfaction. The result confirm the dimensionality and predictability of two emotional labor measures as well as suggests the need for the careful selection and refinement of appropriate measures according to consequences.

• International Journal of Advanced Culture Technology
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• 제8권3호
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• pp.192-197
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• 2020

Foreign students in South Korea face important challenges when they try to maintain their health. As a measure of their motivation to actively build skills for overcoming those challenges, we evaluated the 10-item Consumer Health Activation Index (CHAI), testing its feasibility, dimensionality, and reliability. There were no missing data, there was no floor effect, and for the total scores the ceiling effect was trivial (< 2%). Results of the Kaiser-Meyer-Olkin test and Bartlett's test of sphericity indicated that the data were suitable for the detection of structure by factor analysis. The results of parallel analysis and the shape of the scree plot supported a two-factor solution. One factor had 3 items concerning "my doctor" and the other factor had the 7 remaining items. Reliability was high for the 10-item CHAI (alpha = 0.856), for the 3-item subscale (alpha = 0.838), and for the 7-item subscale (alpha = 0.857). Reliability could not be improved by deletion of any items. Use of the CHAI to gather data from these foreign students is feasible, and reliable results can be obtained whether one uses the total score from all 10 items or scores from the proposed 7-item and 3-item subscales.