• Communications for Statistical Applications and Methods
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• v.7 no.3
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• pp.837-843
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• 2000

Kreiss and Franke(192) and Allen and Datta(1999) proposed bootstrapping the M-estimators in ARMA models. In this paper, we introduce the robust estimating function and investigate the bootstrap approximations of the M-estimators which are solutions of the estimating equations in TAR models. A number of simulation results are presented to estimate the sampling distribution of the M-estimators, and asymptotic validity of the bootstrap for the M-estimators is established.

• Journal of the Korean Statistical Society
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• v.34 no.2
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• pp.99-107
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• 2005

In this paper we consider the asymptotic mean squared error of positive part James-Stein estimators. In the normal-normal example, estimators of the mean squared error of these estimators are provided which are correct asymptotically up to O($m^{-l}$). Asymptotic estimators of the MSE's which correct up to O($m^{-l}$) are also provide. Here, m denotes the number of strata. A simulation study is undertaken to evaluate the performance of these estimators.

• The Korean Journal of Applied Statistics
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• v.6 no.2
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• pp.265-275
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• 1993

This paper deals with the M-estimators in regression model. The class of M-estimators is defined on nonlinear regression model and the conditions to hold the consistency of the considered estimators are suggested when the parameter space of the model is compact.

• Journal of the Korean Statistical Society
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• v.26 no.3
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• pp.333-354
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• 1997

Computational algorithms to calculate M-estimators and rank estimators of regression parameters from left-truncated and right-censored data are developed herein. In the case of M-estimators, new statistical methods are also introduced to incorporate leverage assements and concomitant scale estimation in the presence of left truncation and right censoring on the observed response. Furthermore, graphical methods to examine the residuals from these data are presented. Two real data sets are used for illustration.

• Journal of the Korean Statistical Society
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• v.32 no.4
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• pp.385-399
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• 2003

We first introduce the quasi-score estimating function and applied the quasi-score estimating function to nonlinear time series models. We proposed the M quasi-score estimating functions bounded functions for the quasi-score estimating functions. Also, we investigated the asymptotic properties of quasi-likelihood estimators and M quasi-likelihood estimators. Simulation results show that the M quasi-likelihood estimators work better than the least squares estimators under the heavy-tailed distributions

• Journal of the Korea Society of Computer and Information
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• v.10 no.2 s.34
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• pp.21-30
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• 2005

In general, the robust estimation method is well known for a good statistical estimator that is insensitive to small departures from the idealized assumptions for which the estimation is optimized. While there are many existing robust estimation techniques that have been proposed in the literature, two main techniques used in computer vision are M-estimators and least-median of squares (LMS). Among these. we utilized the M-estimators since they are known to provide an optimal estimation of affine motion parameters. The M-estimators have higher statistical efficiency but tolerate much lower percentages of outliers unless properly initialized. To resolve these problems, we proposed an adaptive M-estimators algorithm that effectively separates outliers from non-outliers and estimate affine model parameters, using a continuous sigmoid weight function. The experimental results show the superiority of our method.

• Journal of applied mathematics & informatics
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• v.18 no.1_2
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• pp.591-601
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• 2005

The results of fuzzy linear regression are very sensitive to irregular data. When this points exist in a set of data, a fuzzy linear regression model can be incorrectly interpreted. The purpose of this paper is to detect irregular data and to propose robust fuzzy linear regression based on M-estimators with triangular fuzzy regression coefficients for crisp input-output data. Numerical example shows that irregular data can be detected by using the residuals based on M-estimators, and the proposed robust fuzzy linear regression is very resistant to this points.

• Journal of Korean Society for Quality Management
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• v.25 no.2
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• pp.47-59
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• 1997

In this paper we develop computational algorithms to calculate M-estimators of regression parameters from right-censored data that are naturally collected in quality control. In the case of M-estimators, a new statistical method is also introduced to incorporate concomitant scale estimation in the presence of right censoring on the observed responses. Furthermore, we illustrate this by simulations.

• The Korean Journal of Applied Statistics
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• v.25 no.2
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• pp.301-309
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• 2012

This paper proposes some alternative classes of shrinkage estimators and analyzes their properties. In particular, some new shrinkage estimators are identified and compared with Pandey (1983), Pandey and Srivastav (1985) and Jani (1991) estimators. Numerical illustrations are also provided.

• Journal of the Korean Statistical Society
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• v.29 no.2
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• pp.169-185
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• 2000

Given a random sample of size N (unknown) with density f(x│$\theta$), suppose that only n observations which lie outside a region r are recorded. On the basis of n observation, the Bayes estimators of $\theta$ and N are considered and their asymptotic expansions are developed to find the third order asymptotic properties with those of the maximum likelihood estimators and the Bayes modal estimators. The asymptotic m.s.e.'s of these estimators are expressed. An example is given to illustrate the results obtained.