• Communications for Statistical Applications and Methods
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• 제25권1호
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• pp.99-107
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• 2018

This article considers a robust Kalman filter from the M-estimation point of view. Pak (Journal of the Korean Statistical Society, 27, 507-514, 1998) proposed a particular M-estimating function which has the data-based shaping constants. The Kalman filter with the proposed M-estimating function is considered. The structure and the estimating algorithm of the Kalman filter accompanying the M-estimating function are mentioned. Kalman filter estimates by the proposed M-estimating function are shown to be well behaved even when data are contaminated.

• Journal of the Korean Statistical Society
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• 제36권3호
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• pp.387-395
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• 2007

We propose a doubly robustified estimating function for the estimation of parameters in the context of ARCH models. We investigate asymptotic properties of estimators obtained as solutions of robust estimating equations. A simulation study shows that robust estimator from specified doubly robustified estimating equation provides better performance than conventional robust estimators especially under heavy-tailed distributions of innovation errors.

• Journal of the Korean Data and Information Science Society
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• 제13권1호
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• pp.47-53
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• 2002

A new redescending M-estimating function is introduced. The estimators by this new redescending function attain the same level of robustness as the existing redescending M-estimators, but have less asymptotic variances than others except few cases. We have focused on estimating a location parameter, but the method can be extended for a scale estimation.

• Journal of the Korean Data and Information Science Society
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• 제16권1호
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• pp.33-40
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• 2005

The estimating function method was proposed by Godambe(1985) for parameter estimation under unknown distributions for errors in the models. Threshold Autoregressive Heteroscedastic (Threshold-ARCH) models have been developed by Zakoian(1994) and Li and Li(1996) for explaining the asymmetric properties in the financial time series data. In this paper, we apply the estimating function method to the Threshold-ARCH model and show that the proposed estimators perform better than the MLE under the heavy-tailed distributions.

• Journal of the Korean Statistical Society
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• 제32권4호
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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 Korean Statistical Society
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• 제35권2호
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• pp.213-224
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• 2006

We investigate the influence of subjects or observations on regression coefficients of generalized estimating equations using the influence function and the derivative influence measures. The influence function for regression coefficients is derived and its sample versions are used for influence analysis. The derivative influence measures under certain perturbation schemes are derived. It can be seen that the influence function method and the derivative influence measures yield the same influence information. An illustrative example in longitudinal data analysis is given and we compare the results provided by the influence function method and the derivative influence measures.

• Communications for Statistical Applications and Methods
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• 제7권3호
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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.

• 인터넷정보학회논문지
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• 제6권2호
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• pp.37-47
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• 2005

대부분 소프트웨어 규모 추정 기법들은 사용자에게 제공될 기능에 기반을 두고 있으며, 기능에 대한 점수를 부여하는 과정에서 복잡도를 함께 고려하고 있다. 완전기능점수 기법은 데이터 처리, 실시간 시스템과 알고리즘 소프트웨어 등 광범위한 분야에 적용되는 장점을 갖고 있는 반면에 규모를 추정하는데 필요한 기능 요소들에 대한 가중치를 부여하지 않는 단점도 갖고 있다. 본 논문은 신규로 개발되는 프로젝트와 유지보수 프로젝트들에 적용되는 완전기능점수 계산 방법에 각기능 요소들에 대한 복잡도를 고려하여 소프트웨어 규모를 추정할 수 있는 방법을 제안하였다. 이를 위해 기능 점수 기반으로 실측된 데이터를 이용하여 제안된 방법의 타당성을 검증하였다. 검증한 결과, 소프트웨어의 규모 추정에 사용되는 속성들인 기능 요소들에 다른 가중치를 적용하였을 경우 보다 좋은 규모 추정이 가능하였다.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송공학회 1999년도 KOBA 방송기술 워크샵 KOBA Broadcasting Technology Workshop
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• pp.57.2-62
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• 1999

Fractal coding is image compression techniques using one of image characteristics self-transformability. In fractal image coding, the encoding process is to select the domain block similar to a range block. The reconstructed image quality of fractal image coding depends on similitude between a range block and the selected domain block. Domain block similar to a range blocks. In fact, the error of the reconstructed image adds up the generated error in encoding process and the generated error in decoding process. But current domain block estimating function considered only the encoding error. We propose a domain block estimating function to consider not only the encoding error but also the decoding error. By computer simulation, it was verified to obtain the high quality reconstructed image.

• 한국임상약학회:학술대회논문집
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• 한국임상약학회 2007년도 추계학술대회
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• pp.185-186
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• 2007