• Journal of the Korean Statistical Society
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• 제24권1호
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• pp.183-195
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• 1995

For a distribution on the unit sphere, the set of eigenvectors of the second moment matrix is a conventional measure of orientation. Asymptotic confidence cones for eigenvector under the parametric assumptions for the underlying distributions and nonparametric confidence cones for eigenvector based on bootstrapping were proposed. In this paper, to reduce the level error of confidence cones for eigenvector, double bootstrap confidence cones based on prepivoting are considered, and the consistency of this method is discussed. We compare the perfomances of double bootstrap method with the others by Monte Carlo simulations.

• Journal of the Korean Statistical Society
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• 제34권3호
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• pp.235-244
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• 2005

In recent years, theoretical properties of Bayesian nonparametric survival models have been studied and the conclusion is that although there are pathological cases the popular prior processes have the desired asymptotic properties, namely, the posterior consistency and the Bernstein-von Mises theorem. In this study, through a simulation experiment, we study the finite sample properties of the Bayes estimator and compare it with the frequentist estimators. To our surprise, we conclude that in most situations except that the prior is highly concentrated at the true parameter value, the Bayes estimator performs worse than the frequentist estimators.

• Journal of applied mathematics & informatics
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• 제3권2호
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• pp.253-264
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• 1996

The generalized logit model of nominal type with random regressors is studied for bootstrapping. We assess the accuracy of some estimators for our generalized logit model using a Monte Carlo simu-lation. That is we study the finite sample properties containing the consistency and asymptotic normality of the maximum likelihood es-timators. Also we compare Newton Raphson algorithm with BHHH algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.1903-1906
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• 2005

We proposed a new variant of correlation approach for ARMA model. The proposed method is is intended to make the current prediction error uncorrelated with the past one. In the investigation of the properties, the uniqueness, consistency and asymptotic normality of the estimate are shown. Via simulation results, we show that the proposed method give good estimates for various systems.

• Journal of the Korean Data and Information Science Society
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• 제3권1호
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• pp.33-46
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• 1992

The set of eigenvectors of the second moment matrix and the mean vector are the measures of orientation for a distribution supported on the unit sphere. Bootstrap confidence cone for the eigenvector is constructed and the consistency of this method is discussed. The performance of our bootstrap cone for the eigenvector is compared with that of the asymptotic confidence cones for two measures under the parametric assumptions for the underlying distributions and that of the bootstrap cone for the mean vector by Monte Carlo simulation.

• Journal of the Korean Data and Information Science Society
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• 제3권1호
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• pp.79-90
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• 1992

We consider hazard ratio as a descriptive measure to compare the hazard experience of a treatment group with that of a control group with censored survival data. In this paper, we propose a kernel estimator of hazard ratio. The uniform consistency and asymptotic normality of a kernel estimator are proved by using counting process approach via martingale theory and stochastic integrals.

• Journal of the Korean Statistical Society
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• 제25권2호
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• pp.161-173
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• 1996

When we deal with a Hilbert space-valued Stochastic Differential Equation (SDE) (or Stochastic Partial Differential Equation (SPDE)), depending on some unknown parameters, the solution usually has a Fourier series expansion. In this situation we consider the maximum likelihood method for the statistical estimation problem and derive the asymptotic properties (consistency and normality) of the Maximum Likelihood Estimator (MLE).

• Journal of the Korean Statistical Society
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• 제31권4호
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• pp.533-543
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• 2002

In this paper we propose a change-point estimator with left and right regressions using the sample Fourier coefficients on the orthonormal bases. The window size is different according to the data in the left side and in the right side at each point. The asymptotic properties of the proposed change-point estimator are established. The limiting distribution and the consistency of the estimator are derived.

• 한국수학교육학회지시리즈A:수학교육
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• 제31권2호
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• pp.133-140
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• 1992

The problem of estimating a smooth distribution function F at a point $\tau$ based on randomly right censored data is treated under certain smoothness conditions on F . The asymptotic performance of a certain class of kernel estimators is compared to that of the Kap lan-Meier estimator of F($\tau$). It is shown that the .elative deficiency of the Kaplan-Meier estimate. of F($\tau$) with respect to the appropriately chosen kernel type estimate. tends to infinity as the sample size n increases to infinity. Strong uniform consistency and the weak convergence of the normalized process are also proved.

• Journal of the Korean Statistical Society
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• 제28권2호
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• pp.225-234
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• 1999

We consider the following semi-parametric non-linear mixed effect regression model : y\ulcorner=f($\chi$\ulcorner;$\beta$)+$\sigma$$\mu$($\chi$\ulcorner)+$\sigma$$\varepsilon$\ulcorner,i=1,…,n,y*=f($\chi$;$\beta$)+$\sigma$$\mu$($\chi$) where y'=(y\ulcorner,…,y\ulcorner) is a vector of n observations, y* is an unobserved new random variable of interest, f($\chi$;$\beta$) represents fixed effect of known functional form containing unknown parameter vector $\beta$\ulcorner=($\beta$$_1$,…,$\beta$\ulcorner), $\mu$($\chi$) is a random function of mean zero and the known covariance function r(.,.), $\varepsilon$'=($\varepsilon$$_1$,…,$\varepsilon$\ulcorner) is the set of uncorrelated measurement errors with zero mean and unit variance and $\sigma$ is an unknown dispersion(scale) parameter. On the basis of finite-sample, small-dispersion asymptotic framework, we derive an absolute lower bound for the asymptotic mean squared errors of prediction(AMSEP) of the regular-consistent non-linear predictors of the new random variable of interest y*. Then we construct an optimal predictor of y* which attains the lower bound irrespective of types of distributions of random effect $\mu$(.) and measurement errors $\varepsilon$.