• Journal of the Korean Statistical Society
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• v.27 no.1
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• pp.113-132
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• 1998

In this paper, we consider a minimum distance (M.D.) estimation based on kernels for U-statistics. We use Cramer-von Mises type distance function which measures the discrepancy between U-empirical distribution function(d.f.) and modeled d.f. of kernel. In the distance function, we allow various integrating measures, which can be finite, $\sigma$-finite or discrete. Then we derive the asymptotic normality and study the qualitative robustness of M. D. estimates.

• International Journal of Reliability and Applications
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• v.9 no.1
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• pp.71-78
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• 2008

Based on the goodness of fit approach, a new test is presented for testing exponentiality versus exponential better (worse) than used (EBU (EWU)) class of life distributions. The new test is much simpler to compute, asymptotically normal, enjoys good power and performs better than previous tests in terms of power and Pitman asymptotic efficiencies for several alternatives.

• Journal of the Korea Society for Simulation
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• v.9 no.3
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• pp.43-51
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• 2000

In regression model, we estimate the unknown parameters by using various methods. There are the least squares method which is the most general, the least absolute deviation method, the regression quantile method and the asymmetric least squares method. In this paper, we will compare each others with two cases: firstly the theoretical comparison in the asymptotic sense and then the practical comparison using Monte Carlo simulation for a small sample size.

• ETRI Journal
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• v.18 no.3
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• pp.207-213
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• 1996

This paper proposes a new nonparametric test for testing the null hypothesis that the MRL is constant against the alternative hypothesis that the MRL is decreasing (increasing) for ramdomly censored data. The proposed test statistic is a L-statistic, and we use L-statistic theory to establish its asymptotic normality of the test statistic. We discuss the efficiency loss due to censoring and also calculate the asymptotic relative efficiencies of our test statistic with respect to the Chen, Hollander and Langberg's test for several alternatives.

• Journal of the Korean Data and Information Science Society
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• v.15 no.2
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• pp.347-353
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• 2004

We propose a simple nonparametric estimator of relative risk in the two sample case of the proportional hazards model for complete data. The asymptotic distribution of this estimator is derived using a functional equation. We obtain the asymptotic normality of the proposed estimator and compare with Begun's estimator by confidence interval through simulations.

• Journal of the Korean Data and Information Science Society
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• v.15 no.1
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• pp.273-284
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• 2004

Bayes estimation of parameters is considered for two independent exponential distributions with ordered means. Order restricted Bayes estimators for means are obtained with respect to inverted gamma, noninformative prior and uniform prior distributions, and their asymptotic properties are established. It is shown that the maximum likelihood estimator, restricted maximum likelihood estimator, unrestricted Bayes estimator, and restricted Bayes estimator of the mean are all consistent and have the same limiting distribution. These estimators are compared with the corresponding unrestricted Bayes estimators by Monte Carlo simulation.

• Proceedings of the Korea Society for Simulation Conference
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• 2000.11a
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• pp.6-10
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• 2000

In regression model we estimate the unknown parameters using various methods. There are the least squares method which is the most general, the least absolute deviation, the regression quantile and the asymmetric least squares method. In this paper, we will compare each others with two case: to begin with the theoretical comparison in the asymptotic sense, and then the practical comparison using Monte Carlo simulation for a small sample size.

• Proceedings of the Korean Society of Computational and Applied Mathematics Conference
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• 2003.09a
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• pp.14-14
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• 2003

Given a sequence ｛ $X_{n}$｝ of independent and identically distributed random variables with F, a sequential procedure for the p-th quantile ξ$_{P}$= $F^{-1}$ (P), 0$\beta$-protection. Some asymptotic properties for the proposed procedure and of an involved stopping time are proved: asymptotic consistency, asymptotic efficiency and asymptotic normality. From one of the results an effect of smoothing based on kernel functions is discussed. The results are also extended to the contaminated case.e.e.

• The Korean Journal of Applied Statistics
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• v.8 no.2
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• pp.65-75
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• 1995

For the determination of dimension of e.d.r. space, both of Sliced Inverse Regression (SIR) and Principal Hessian Directions (PHD) proposed asymptotic test. But the asymptotic test requires the normality and large samples of explanatory variables. Cook and Weisberg(1991) suggested permutation tests instead. In this study permutation tests are actually made, and the power of them is compared with asymptotic test in the case of SIR and PHD.

• Communications for Statistical Applications and Methods
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• v.9 no.1
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• pp.241-248
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• 2002

The problem of wavelet density estimation based on Shannon's wavelets is studied when the sample observations are contaminated with random noise. In this paper we will discuss the asymptotic normality for deconvolving wavelet density estimator of the unknown density f(x) when courier transform of random noise has polynomial descent.