• Journal of the Korean Data and Information Science Society
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• 제20권4호
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• pp.757-764
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• 2009

In discriminant analysis, we consider a special pattern which contains a block of missing observations. We assume that the two populations are equally likely and the costs of misclassification are equal. In this situation, we consider the bootstrap confidence intervals of the error rate in the circular models when the covariance matrices are equal and not equal.

• Communications for Statistical Applications and Methods
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• 제16권4호
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• pp.675-686
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• 2009

In this paper, it will be assumed that there are two distinct populations which are multivariate normal with equal covariance matrix. We also assume that the two populations are equally likely and the costs of misclassification are equal. The classification rule depends on the situation when the training samples include missing values or not. We consider the bootstrap confidence intervals for classification error rate when a block of observation is missing.

• 통합자연과학논문집
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• 제5권2호
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• pp.135-138
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• 2012

The reduction of efficiency of missing observations on complete diallel cross designs are examined. we studies robustness of optimal block designs for estimating general combining ability against loss of missing observations in diallel cross. A-efficiencies suggest that these designs are fairly robust. Simple g-inverses may be found for the information matrices of the line effects which allow evaluation of expressions for the variances of the differences between the pairs of line effects with missing observations. we numerically calculate the reduction of efficiency for estimating general combining ability against loss of missing observations in diallel cross.

• Journal of the Korean Data and Information Science Society
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• 제24권1호
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• pp.189-200
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• 2013

In this paper, it will be assumed that there are two distinct populations which are multivariate normal with equal covariance matrix. We also assume that the two populations are equally likely and the costs of misclassification are equal. The classification rule depends on the situation whether the training samples include missing values or not. We consider the conditional bootstrap confidence intervals for classification error rate when a block of observation is missing.

• Journal of the Korean Data and Information Science Society
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• 제21권2호
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• pp.231-240
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• 2010

4 $\times$ 4 균형불완전블럭모형에서 고정효과만이 존재하는 경우 주효과를 검정하기 위한 순위변환 통계량의 검정력은 적은 반복수에도 가장 높은 수준을 유지하며, 지수분포와 이중지수분포하에서는 모수적 통계량의 검정력보다 큰 격차의 상대적 우위를 보인다. 특히 전형적인 균형불완전블럭모형하에서 주인자는 고정이며 블럭인자는 랜덤인 경우의 순위변환 통계량의 검정력은 주효과의 효과크기 및 블럭효과의 모집단 분포와 모수크기에 상관없이 모든 상황에 걸쳐 현저하게 높은 우위성를 보인다. 또한 반복수가 증가함에따라 순위변환 통계량의 검정력은 빠른 속도로 증가한다. 전체적인 주효과의 순위변환 통계량의 검정력 우위는 하나의 주효과 및 블럭효과와 결측값이 존재하는 균형불완전블럭모형의 고유특성으로 말미암아 고정효과 및 표본의 작은 크기변화에 민감하게 반응하며 상대적 검정력 우위를 갖는다고 볼 수 있다.