• Journal of the Korean Statistical Society
• /
• v.31 no.4
• /
• pp.533-543
• /
• 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.

• Journal of the Korean Statistical Society
• /
• v.19 no.1
• /
• pp.80-87
• /
• 1990

In reliability and life testing the mean residual life (MRL) of an item plays a significant role. While there has been a great deal of discussion on the theoretical aspects of the MRL, good estimators of MRL have been difficult to obtain. In this paper we propose a new estimator of the MRL of items at a given age, which is especially good for a small sample. The new estimator compares favorably with the empirical MRL estimator for small samples.

• Communications for Statistical Applications and Methods
• /
• v.1 no.1
• /
• pp.57-74
• /
• 1994

This paper deals with the estimation problems of a common mean of several independent normal populations with unknown variances, based on random samples of equal size. The authors suggest a promising approach and a new estimator to improve Graybill-Deal estimator further. By Monte Carlo simulation study, the efficiency of new estimator is compared with that of Graybill-Deal estimator.

• Communications for Statistical Applications and Methods
• /
• v.12 no.2
• /
• pp.463-472
• /
• 2005

In this paper, the problem of estimating a p-variate (p $\ge$4) normal mean vector is considered in decision-theoretic set up. Using a simple property of the noncentral chi-square distribution, a sequence of estimators dominating the Lindley type estimator with the cases of unknown covariance matrices has been produced and each improved estimator is better than previous one.

• Communications for Statistical Applications and Methods
• /
• v.3 no.2
• /
• pp.129-138
• /
• 1996

We propose a smoothing estimator of mean residual life function based on Ghorai and Susarla's (1990) smooth estimator of distribution function under random censorship model and provide the asymptotic properties of this estimator. The Monte Carlo simulation is performed to compare the proposed estimator with the other estimators and an exmple is also given using the real data.

• Communications for Statistical Applications and Methods
• /
• v.17 no.3
• /
• pp.451-457
• /
• 2010

This paper considers the problem of nonparametric deconvolution density estimation when sample observa-tions are contaminated by double exponentially distributed errors. Three different deconvolution density estima-tors are introduced: a weighted kernel density estimator, a kernel density estimator based on the support vector regression method in a RKHS, and a classical kernel density estimator. The performance of these deconvolution density estimators is compared by means of a simulation study.

• Journal of the Korean Statistical Society
• /
• v.25 no.4
• /
• pp.471-487
• /
• 1996

In this paper we propose an efficient scoring type one-step GM-estimator, which has a bounded influence function and a high break-down point. The main point of the estimator is in the weighting scheme of the GM-estimator. The weight function we used depends on both leverage points and residuals So we construct an estimator which does not downweight good leverage points Unider some regularity conditions, we compute the finite-sample breakdown point and prove asymptotic normality Some simulation results are also presented.

• Communications for Statistical Applications and Methods
• /
• v.23 no.1
• /
• pp.71-83
• /
• 2016

In this paper we discuss a deconvolution density estimator obtained using the support vector machines (SVM) and Tikhonov's regularization method solving ill-posed problems in reproducing kernel Hilbert space (RKHS). A remarkable property of SVM is that the SVM leads to sparse solutions, but the support vector deconvolution density estimator does not preserve sparsity as well as we expected. Thus, in section 3, we propose another support vector deconvolution estimator (method II) which leads to a very sparse solution. The performance of the deconvolution density estimators based on the support vector method is compared with the classical kernel deconvolution density estimator for important cases of Gaussian and Laplacian measurement error by means of a simulation study. In the case of Gaussian error, the proposed support vector deconvolution estimator shows the same performance as the classical kernel deconvolution density estimator.

• Journal of the Korean Statistical Society
• /
• v.27 no.3
• /
• pp.369-383
• /
• 1998

This paper deals with a robust regression estimator. We propose an efficient one-step GM-estimator, which has a bounded influence function and a high breakdown point. The main idea of this paper is to use the Mallows-type weights which depend on both the predictor variables and the residuals from a high breakdown initial estimator. The proposed weighting scheme severely downweights the bad leverage points and slightly downweights the good leverage points. Under some regularity conditions, we compute the finite-sample breakdown point and prove the asymptotic normality. Some simulation results and a numerical example are also presented.

• Communications for Statistical Applications and Methods
• /
• v.4 no.3
• /
• pp.903-909
• /
• 1997

The ordinary least squares estimator and the corresponding pivotal statistics have been widely used for the unit test. Recently several test criteria based on maximum likelihood estimators and weighted symmetric estimator have been proposed for testing the unit root hypothesis in the autoregressive processes. Pantula at el. (1994) showed that the weighted symmetric estimator has good power properties. In this article we use an adjusted estimator for mean in the model when we use weighted symmetric estimator. A simulation study shows that for the small samples, this new test criterion has better power properties than the weighted symmetric estimator.