• Proceedings of the Korean Association for Survey Research Conference
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• 2001.11a
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• pp.77-85
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• 2001

In this paper we study the calibration estimator and its variance estimator for the population total using a bootstrap method according to the levels of an auxiliary information having strong correlation with an interested variable in nonresponse situation. At this point, we find tire calibration estimator in case of auxiliary information for population and sample, and then we drive the bootstrap variance estimator of it. By simulation study we compare the efficiencies with the Taylor and Jackknife variance estimators.

• Communications for Statistical Applications and Methods
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• v.8 no.1
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• pp.127-135
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• 2001

In this paper, we consider a simple method for testing the assumption of independent censoring on the basis of a Cox proportional hazards regression model with a time-dependent covariate. This method involves a two-stage sampling in which a random subset of censored observations is selected and followed-up until their true survival times are observed. Lee and Wolfe(1998) proposed an adjusted estimate of the survivor function for the dependent censoring under a proportional hazards alternative. This paper extends their result to obtain a bootstrap confidence interval for the adjusted survivor function under the dependent censoring. The proposed procedure is illustrated with an example of a clinical trial for lung cancer analysed in Lee and Wolfe(1998).

• Journal of the Korean Data and Information Science Society
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• v.15 no.1
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• pp.41-47
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• 2004

DNA marker 95bp and 100bp are selected as major DNA markers of the BM4311 microsatellite locus in progeny test Hanwoo chromosome 6 linkage map. This document is tried to know whether DNA marker 95bp and 100bp are also major DNA markers in Hanwoo performance valuation in chromosome 6 linkage map. The bootstrap BCa method will be used to calculate confidence interval for DNA markers.

• Journal of the Korean Data and Information Science Society
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• v.24 no.1
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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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• v.25 no.3
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• pp.677-684
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• 2014

Support vector quantile regression (SVQR) is capable of providing more complete description of the linear and nonlinear relationships among random variables. To improve the estimation performance of SVQR we propose to use SVQR ensemble with bagging (bootstrap aggregating), in which SVQRs are trained independently using the training data sets sampled randomly via a bootstrap method. Then, they are aggregated to obtain the estimator of the quantile regression function using the penalized objective function composed of check functions. Experimental results are then presented, which illustrate the performance of SVQR ensemble with bagging.

• Communications for Statistical Applications and Methods
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• v.11 no.3
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• pp.643-655
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• 2004

L$_1$-estimator in the linear regression model is widely recognized to have superior robustness in the presence of vertical outliers. While the L$_1$-estimation procedures and algorithms have been developed quite well, less progress has been made with the hypothesis test in the multiple L$_1$-regression. This article suggests computer-intensive resampling approaches, jackknife and bootstrap methods, to estimating the variance of L$_1$-estimator and the scale parameter that are required to compute the test statistics. Monte Carlo simulation studies are performed to measure the power of tests in small samples. The simulation results indicate that bootstrap estimation method is the most powerful one when it is employed to the likelihood ratio test.

• Journal of Korean Society for Quality Management
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• v.30 no.4
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• pp.106-119
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• 2002

The fatigue curve with estimated parameters represents the estimate of the median or mean life at a given applied stress But, in order to assist a designer in making decisions regarding the fatigue failure mode, it is common practice to construct a design curve on the lower or safe side of data. In this study, to overcome the limitations(i.e., no runout, equal variance, and quality of the approximation, etc) of Shen, Wirsching, and Cashman's method which suggested the approximate design curve for nonlinear models using tolerance interval constructed by Owen's method, an algorithm to find design curves under the fatigue model using a parametric bootstrap method, is proposed and illustrated with multiple fatigue data sets.

• Journal of Korean Society for Quality Management
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• v.24 no.2
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• pp.77-86
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• 1996

In this paper, we consider the problem of testing independence in the absolutely continuous bivariate exponential distribution of Block and Basu(1974). We construct a bootstrap procedure for testing zero and non-zero values of the parameter ${\lambda}_3$ which measures the degree of dependence and compare the power of the bootstrap test with likelihood ratio test(LRT) by Gupta et al.(1984) and the test based on maximum likelihood estimator(MLE) $\hat{{\lambda}}_3$ by Hanagal and Kale(1991) for small and moderate sample sizes.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.3
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• pp.196-201
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• 2008

Statistical learning theory has three analytical tools which are support vector machine, support vector regression, and support vector clustering for classification, regression, and clustering respectively. In general, their performances are good because they are constructed by convex optimization. But, there are some problems in the methods. One of the problems is the subjective determination of the parameters for kernel function and regularization by the arts of researchers. Also, the results of the learning machines are depended on the selected parameters. In this paper, we propose an efficient method for objective determination of the parameters of support vector clustering which is the clustering method of statistical learning theory. Using evolutionary algorithm and bootstrap method, we select the parameters of kernel function and regularization constant objectively. To verify improved performances of proposed research, we compare our method with established learning algorithms using the data sets form ucr machine learning repository and synthetic data.

• Journal of Life Science
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• v.12 no.6
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• pp.821-834
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• 2002

In order to measure the inter- and intraspecific genetic divergences within the genus Alexandrium, the variations within the internal transcribed spacer (ITS1 and ITS2) regions and 5.85 ribosomal RNA gene of eight Alexandrium species were examined for 33 strains from diverse geographical locations by direct sequencing. Five isolates of A. tamarense (AT-2, AT-6, AT-10, AT-A and AT-B) from Jinhae Bay, Korea were found to be completely identical to a Japanese strain OFX151-A. The length of the amplified ITSI-5.85-ITS2 region varied from 481 nucleotides (in A. margalefi) to 528 nucleotides (in A. affine CU1-1). ITS1 and ITS2 nucleotide lengths were negatively correlated, whereas a positive correlation was found between their G＋C content. The degree of sequence divergence ranged from 0.3% (1 bp) to a maximum of 53% (305 Up). Pairwise sequence comparisons revealed a small degree of divergence between A. tamarense and A. Pundyense isolates (1.2 - 2.3% ＝ 6-12 bp), but a high degree of divergence between A. tamarense and A. catenella (19.8% ＝ 102 bp), and between A. catenella and A. Pundyense (19.7%). Although most nodes were weakly supported by bootstrap values, some types tend to form independent molecular groups. A. catenella isolates also formed an independent molecular sub-group, with relaticula strong bootstrap values (94% or 85% and 79% or 98%, respectively in PAUP and NJ trees). Interestingly, A. cohorticula and A. frateculus always clustered within the same sub-group, this result being supported by strong bootstrap values. Our results indicate that the ITS regions provide useful informations on hierarchical population genetic structure and a high phylogenetic resolution in intraspecific and interspecific Alexandrium population.