• Journal of the Korean Data and Information Science Society
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• 제19권2호
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• pp.577-585
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• 2008

This paper suggests a marginal probability model for analyzing repeated polytomous response data when some factors are nested in others in treatment structures on a larger experimental unit. As a repeated measures factor, time is considered on a smaller experimental unit. So, two different experiment sizes are considered. Each size of experimental unit has its own design structure and treatment structure, and the marginal probability model can be constructed from the structures for each size of experimental unit. Weighted least squares(WLS) methods are used for estimating fixed effects in the suggested model.

• Communications for Statistical Applications and Methods
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• 제19권3호
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• pp.423-432
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• 2012

This paper is concerned with the detection of multiple change-points in linear regression models. The proposed procedure relies on the local estimation for global change-point estimation. We propose a multiple change-point estimator based on the local least squares estimators for the regression coefficients and the split measure when the number of change-points is unknown. Its statistical properties are shown and its performance is assessed by simulations and real data applications.

• Communications for Statistical Applications and Methods
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• 제14권1호
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• pp.183-193
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• 2007

This paper shows that a response function for the center of fuzzy output nay not be the same as that for the spread in a fuzzy linear regression model and then suggests a separate fuzzy regression model makes a distinction between response functions of the center and the spread of fuzzy output. Also we use a least squares method to estimate the separate fuzzy regression model and compare an accuracy of proposed model with another fuzzy regression model developed by Diamond (1988) and Kao and Chyu (2003).

• Communications for Statistical Applications and Methods
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• 제4권2호
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• pp.353-358
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• 1997

When the disturbances in the linear repression medel are generated by a seasonal autoregressive scheme the Cochrane Orcutt transformation estimator (COTE) is a well known alternative to Generalized Least Squares estimator (GLSE). In this paper it is analyzed in which situation the Ordinary Least Squares estimator (OLSE) is always better than COTE for positive autocorrelation in terms of efficiency which is here defined as the ratio of the total variances.

• Communications for Statistical Applications and Methods
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• 제4권1호
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• pp.21-32
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• 1997

Robust regression based on M-estimator reduces and/or bounds the influence of outliers in the y-direction only. Therefore, when several influential observations exist, diagnostics in the robust regression is required in order to detect them. In this paper, we propose influence diagnostics in the robust regression based on M-estimator and its one-step version. Noting that M-estimator can be obtained through iterative weighted least squares regression by using internal weights, we apply the weighted least squares (WLS) regression diagnostics to robust regression.

• Communications for Statistical Applications and Methods
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• 제18권6호
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• pp.817-835
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• 2011

The moment(MM) and least squares(LS) estimations of the parameters are derived for the Pareto distribution in the presence of outliers. Further, we have derived a mixture method(MIX) of estimations with MM and LS that shows that the MIX is more efficient. In the final section we have given an example of actual data from a medical insurance company.

• Journal of the Korean Statistical Society
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• 제34권1호
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• pp.61-71
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• 2005

A class of asymmetric ARCH processes is proposed via binary random power transformations. This class accommodates traditional nonlinear models such as threshold ARCH (Rabemanjara and Zacoian (1993)) and Box-Cox type ARCH models(Higgins and Bera (1992)). Stationarity condition of the model is addressed. Iterative least squares(ILS) and pseudo maximum like-lihood(PML) methods are discussed for estimating parameters and related algorithms are presented. Illustrative analysis for Korea Stock Prices Index (KOSPI) data is conducted.

• Journal of the Korean Data and Information Science Society
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• 제15권2호
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• pp.307-316
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• 2004

Principal Component Regression(PCR) and Partial Least Squares Regression(PLSR) are the two most popular regression techniques in chemometrics. In the field of chemometrics usually the number of regressor variables greatly exceeds the number of observation. So we have to reduce the number of regressors to avoid the identifiability problem. In this paper we compare PCR and PLSR techniques combined with various robust regression methods including regression depth estimation. We compare the efficiency, goodness-of-fit and robustness of each estimators under several contamination schemes.

• Communications for Statistical Applications and Methods
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• 제3권2호
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• pp.299-307
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• 1996

This article is concerned with the algorithms for the least median of squares estimator. An algorithm based on the $L{\infty}$ .inf.-estimation procedure is proposed in an attempt to improve the optimality of the estimate. And it is shown that the proposed algorithm yields more optimal estimate than the traditional resampling algorithms. The proposed algorithm employs a linear scaling transformation at each iteration of the$L{\infty}$-algorithm to deal with its computational inefficiency problem.

• 전자공학회논문지
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• 제50권6호
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• pp.246-253
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• 2013

위상 펼침 (phase unwrapping)과 최소자승(least-squares) 기법들을 이용한 기존 디지털 주파수 변별기 (Digital Frequency Discriminator: DFD) 설계를 바탕으로, 본 논문에서는 주파수 판별 대역이 4배 확장한 새로운 DFD 설계를 제안한다. 구체적으로, 주파수 판별 대역을 기존 2-6GHz에서 2-18GHz로 4배 확장함에 따라 주파수 판별 정확도를 높이기 위해 DFD 내의 지연선 수가 증가되고, 이에 따른 주파수 추정 연산량이 증가되는데, 본 논문에서는 이러한 2-18GHz 대역 주파수 판별을 위해 보다 효율적인 주파수 추정 알고리즘을 제안한다. 특히, 제안하는 주파수 추정 방법에서는 기존 방법인 위상 펼침 기법을 기반으로 펼친 위상의 후보군을 만들되, 각 지연선에서 발생할 수 있는 위상 잡음을 미리 추정하여, 적절한 펼친 위상 후보군을 선택하는 새로운 주파수 후보군 선택 방법을 제안한다. 이렇게 선택된 위상 후보군만을 최소자승 기법에 적용하여 주파수를 추정함으로써, 결과적으로 기존 DFD의 주파수 추정에 비해 연산량을 줄일 수 있다. 끝으로, 제안한 DFD에 대한 주파수 변별 방법을 비교 분석하고, 시뮬레이션을 통해 제안된 방법의 주파수 판별 성능을 검증한다.