• Journal of the Korean Statistical Society
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• v.32 no.3
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• pp.213-224
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• 2003

We give an overview of results which have appeared or will appear elsewhere demonstrating that by suitably modifying the bootstrap principle, its applicability can be greatly enhanced. Although we state our results for the iid case, extensions are, at least heuristically, easy.

• The Korean Journal of Applied Statistics
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• v.6 no.2
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• pp.227-235
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• 1993

Calibration relates the estimation of independent variable which rquires more effort or expense than dependent variable does. It would be provided with high accuracy because a little change of the result of independent variable cn cause a serious effect to the human being. Usual statistical analysis assumes the normality of error distribution or linearity of data. It is desirable to analyze the data without those assumptions for the accuracy of the calibration. In this paper, we calibrated the data nonparametrically without those assumptions and derived confidence interval estimate for the independent variable. As a method, we used kernel method which is popular in modern statistical branch. We derived bootstrap confidence interval estimate from the bootstrap confidence band.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.1-4
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• 2001

The classification performance of Artificial Neural Network (ANN) and RBF-NN was compared for Landsat TM image. The RBF-NN was validated for three unique landuse types (e.g. Mixed landuse area, Cultivated area, Urban area), different input band combinations and classification class. The bootstrap resampling technique was employed to estimate the confidence intervals and distribution for unit load, The pollutant generation was varied significantly according to the classification accuracy and percentile unit load applied. Especially in urban area, where mixed landuse is dominant, the difference of estimated pollutant load is largely varied.