• Journal of the Korean Statistical Society
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• 제16권1호
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• pp.21-25
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• 1987

The combined estimator of inter- and intra-block estimators in incomplete block designs can be expressed as a weighted average of two location estimators. The weight should be between 0 and 1. However, the negative variance component estimate could result in the weight being negative or larger than 1. In this paper, we show that if two location estimators have symmetric unimodal distributions, truncating the weight to 0 or 1 accordingly improves the combined estimator under an arbitrary convex loss function.

• Journal of the Korean Statistical Society
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• 제35권1호
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• pp.63-77
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• 2006

Maximum product of spacings estimator is proposed in this paper as a competent alternative of maximum likelihood estimator for the parameters of exponentiated-Weibull distribution, which does work even when the maximum likelihood estimator does not exist. In addition, a Bayes type estimator known as generalized maximum likelihood estimator is also obtained for both of the shape parameters of the aforesaid distribution. Though, the closed form solutions for these proposed estimators do not exist yet these can be obtained by simple appropriate numerical techniques. The relative performances of estimators are compared on the basis of their relative risk efficiencies obtained under symmetric and asymmetric losses. An example based on simulated data is considered for illustration.

• 응용통계연구
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• 제17권3호
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• pp.475-488
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• 2004

위치추정량에서 로버스트한 추정기법 중의 하나로 알려진 데이터 뎁스(depth)를 회귀추정에 적용한 회귀뎁스(regression depth)는 Rousseeuw and Hubert(1999)에 의하여 제안되었다. 이 이외의 회귀뎁스 추정량으로는 심플리셜(simplicial) 뎁스와 사영(projection) 개념의 뎁스 회귀추정량들이 있다. 본 논문에서는 뎁스 기반 회귀추정량들의 성능에 대한 모의실험을 여러 오염 조건에서 행하여 비교하였으며 기존의 우수한 로버스트성을 지니는 추정량으로 최근에 제안된 HBR추정량(Chang et al., 1999)들과의 비교연구도 하였다. 2차원 공간에서의 실험은 전반적으로 사영뎁스기반 회귀추정량이 좋은 결과를 보여주었다.

• Communications for Statistical Applications and Methods
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• 제10권1호
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• pp.145-151
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• 2003

We shall propose several estimators for the scale parameter in the uniform distribution when the parameter is functions of a known exposure level, and obtain expectations and variances for their proposed estimators. And we shall compare numerically relative efficiencies for proposed estimators of the scale parameter in the small sample sizes.

• Journal of applied mathematics & informatics
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• 제3권2호
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• pp.183-192
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• 1996

The least squares cross validated bandwidth is the mini-mizer of the corss validation function for choosing the smooth parame-ter of a kernel density estimator. It is a completely automatic method but it requires inordinate amounts of computational time. We present a convenient formula for calculation of the cross validation function when the kernel function is a symmetric polynomial with finite sup-port. Also we suggest an algorithm for finding global minima of the crass validation function.

• Journal of the Korean Data and Information Science Society
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• 제3권1호
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• pp.17-24
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• 1992

The problem of estimating symmetric probability density with high kurtosis is considered. Such densities are often estimated poorly by a global bandwidth kernel estimation since good estimation of the peak of the distribution leads to unsatisfactory estimation of the tails and vice versa. In this paper, we propose a transformation technique before using a global bandwidth kernel estimator. Performance of density estimator based on proposed transformation is investigated through simulation study. It is observed that our method offers a substantial improvement for the densities with high kurtosis. However, its performance is a little worse than that of ordinary kernel estimator in the situation where the kurtosis is not high.

• Communications for Statistical Applications and Methods
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• 제20권5호
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• pp.405-416
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• 2013

This paper develops a maximum profile likelihood estimator of unknown parameters of the exponentiated Gumbel distribution based on upper record values. We propose an approximate maximum profile likelihood estimator for a scale parameter. In addition, we derive Bayes estimators of unknown parameters of the exponentiated Gumbel distribution using Lindley's approximation under symmetric and asymmetric loss functions. We assess the validity of the proposed method by using real data and compare these estimators based on estimated risk through a Monte Carlo simulation.

• Communications for Statistical Applications and Methods
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• 제10권3호
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• pp.859-871
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• 2003

In this paper we introduce some adaptive M-estimators using selector statistics to estimate the slope of regression model under the symmetric and continuous underlying error distributions. This selector statistics is based on the residuals after the preliminary fit L$_1$ (least absolute estimator) and the idea of Hogg(1983) and Hogg et. al. (1988) who used averages of some order statistics to discriminate underlying symmetric distributions in the location model. If we use L$_1$ as a preliminary fit to get residuals, we find the asymptotic distribution of sample quantiles of residual are slightly different from that of sample quantiles in the location model. If we use the functions of sample quantiles of residuals as selector statistics, we find the suitable quantile points of residual based on maximizing the asymptotic distance index to discriminate distributions under consideration. In Monte Carlo study, this adaptive M-estimation method using selector statistics works pretty good in wide range of underlying error distributions.

• 한국통계학회:학술대회논문집
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• 한국통계학회 2005년도 춘계 학술발표회 논문집
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• pp.109-112
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• 2005

The robust quasi-likelihood (RQL) proposed by Cantoni & Ronchetti (2001) is a robust version of quasi-likelihood. They adopted Huber function to increase the resistance of the RQL estimator to the outliers. They considered the Huber function only of symmetric type. We extend the class of Huber function to include asymmetric types, and derived a method to find the optimal asymmetric one.

• 통합자연과학논문집
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• 제11권3호
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• pp.154-160
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• 2018

Consider the problem of estimating a $p{\times}1$ mean vector ${\theta}(p-r{\geq}3)$, r = rank(K) with a projection matrix K under the quadratic loss, based on a sample $Y_1$, $Y_2$, ${\cdots}$, $Y_n$. In this paper a James-Stein type estimator with shrinkage form is given when it's variance distribution is specified and when the norm ${\parallel}{\theta}-K{\theta}{\parallel}$ is constrain, where K is an idempotent and symmetric matrix and rank(K) = r. It is characterized a minimal complete class of James-Stein type estimators in this case. And the subclass of James-Stein type estimators that dominate the sample mean is derived.