• 응용통계연구
• /
• 제17권3호
• /
• pp.475-488
• /
• 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
• /
• 제12권2호
• /
• pp.413-423
• /
• 2005

Consider the problem of estimating the underlying regression function from a set of noisy data which is contaminated by a long tailed error distribution. There exist several robust smoothing techniques and these are turned out to be very useful to reduce the influence of outlying observations. However, no matter what kind of robust smoother we use, we should choose the smoothing parameter and relatively less attention has been made for the robust bandwidth selection method. In this paper, we adopt the idea of robust location parameter estimation technique and propose the robust cross validation score functions.

• Journal of applied mathematics & informatics
• /
• 제4권2호
• /
• pp.419-432
• /
• 1997

A robust estimator for interpolating spatially distributed regionalized variables is introduced. It reduces outlier effects on ob-taining correlation between spatial lags and the correlation between spatial lags and the corresponding semi-variances and produces a significaantly improved semivariogram com-pared with those of conventional estimators. This estimator is applied to a field experimental data set.

• 대한수학회논문집
• /
• 제20권1호
• /
• pp.145-159
• /
• 2005

In this paper we consider the problem of testing statistical hypotheses for unknown parameters in nonlinear regression models and propose three asymptotically equivalent tests based on regression quantiles estimators, which are Wald test, Lagrange Multiplier test and Likelihood Ratio test. We also derive the asymptotic distributions of the three test statistics both under the null hypotheses and under a sequence of local alternatives and verify that the asymptotic relative efficiency of the proposed test statistics with classical test based on least squares depends on the error distributions of the regression models. We give some examples to illustrate that the test based on the regression quantiles estimators performs better than the test based on the least squares estimators of the least absolute deviation estimators when the disturbance has asymmetric and heavy-tailed distribution.

• 한국통신학회논문지
• /
• 제38C권4호
• /
• pp.365-370
• /
• 2013

본 논문에서는 비정규 잡음에 강인한 직교 주파수 분할 다중화 (orthogonal frequency division multiplexing: OFDM) 블라인드 주파수 옵셋 추정기들을 제안한다. 먼저 복소 등방성 코시 과정으로 모델링 된 비정규 잡음 환경에서 최대 우도 (maximum likelihood: ML) 추정기를 제안한다. 또한, ML 기반의 보다 간단한 추정기를 제안한다. 모의실험을 통해 제안한 추정기들이 비정규 잡음에 강인하며 기존 추정기보다 우수한 주파수 옵셋 추정 성능을 가짐을 보인다.

• Journal of the Korean Statistical Society
• /
• 제24권2호
• /
• pp.349-360
• /
• 1995

This paper deals with the robustness properties of the minimum disparity estimation in linear regression models. The estimators defined as statistical quantities whcih minimize the blended weight Hellinger distance between a weighted kernel density estimator of the residuals and a smoothed model density of the residuals. It is shown that if the weights of the density estimator are appropriately chosen, the estimates of the regression parameters are robust.

• 품질경영학회지
• /
• 제25권2호
• /
• pp.47-59
• /
• 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.

• Journal of the Korean Data and Information Science Society
• /
• 제24권3호
• /
• pp.659-667
• /
• 2013

If the regression model or the propensity model is correct, the unbiasedness of the estimator using doubly robust imputation can be guaranteed. Using a neural network instead of a logistic regression model for the propensity model, the estimators using doubly robust imputation are approximately unbiased even though both assumed models fail. We also propose a doubly robust estimator of ratio form using population information of an auxiliary variable. We prove some properties of proposed theory by restricted simulations.

• Wind and Structures
• /
• 제26권6호
• /
• pp.383-395
• /
• 2018

An accurate determination of wind speed distribution is the basis for an evaluation of the wind energy potential required to design a wind turbine, so it is important to estimate unknown parameters of wind speed distribution. In this paper, Gumbel distribution is used in modelling wind speed data, and alternative robust estimation methods to estimate its parameters are considered. The methodologies used to obtain the estimators of the parameters are least absolute deviation, weighted least absolute deviation, median/MAD and least median of squares. The performances of the estimators are compared with traditional estimation methods (i.e., maximum likelihood and least squares) according to bias, mean square deviation and total mean square deviation criteria using a Monte-Carlo simulation study for the data with and without outliers. The simulation results show that least median of squares and median/MAD estimators are more efficient than others for data with outliers in many cases. However, median/MAD estimator is not consistent for location parameter of Gumbel distribution in all cases. In real data application, it is firstly demonstrated that Gumbel distribution fits the daily mean wind speed data well and is also better one to model the data than Weibull distribution with respect to the root mean square error and coefficient of determination criteria. Next, the wind data modified by outliers is analysed to show the performance of the proposed estimators by using numerical and graphical methods.

• 품질경영학회지
• /
• 제29권1호
• /
• pp.1-10
• /
• 2001

We consider the problem of detecting special variations in multivariate $T^2$-control chart when two or more multivariate outliers are present. Since a multivariate outlier may reflect slippage in mean, variance, or correlation, it can distort the sample mean vector and sample covariance matrix. Damaged sample mean vector and sample covariance matrix have difficulty in examining special variations clearly, An alternative to detection outliers or special variations is to use robust estimators of mean vector and covariance matrix that are less sensitive to extreme observations than are the standard estimators $\bar{x}$ and $\textbf{S}$. We applied popular minimum volume ellipsoid(MVE) and minimum covariance determinant(MCD) method to estimate mean vector and covariance matrix and compared its results with standard $T^2$-control chart using simulated multivariate data with outliers. We found that the modified $T^2$-control chart based on the above robust methods were more effective in detecting special variations clearly than the standard $T^2$-control chart.