The minimum weighted covariance determinant estimator
نویسندگان
چکیده
In this paper we introduce weighted estimators of the location and dispersion of a multivariate data set with weights based on the ranks of the Mahalanobis distances. We discuss some properties of the estimators like the breakdown point, influence function and asymptotic variance. The outlier detection capacities of different weight functions are compared. A simulation study is given to investigate the finitesample behavior of the estimators.
منابع مشابه
Outlier Detection for Support Vector Machine using Minimum Covariance Determinant Estimator
The purpose of this paper is to identify the effective points on the performance of one of the important algorithm of data mining namely support vector machine. The final classification decision has been made based on the small portion of data called support vectors. So, existence of the atypical observations in the aforementioned points, will result in deviation from the correct decision. Thus...
متن کاملInnuence Function and Eeciency of the Minimum Covariance Determinant Scatter Matrix Estimator
The Minimum Covariance Determinant (MCD) scatter estimator is a highly robust estimator for the dispersion matrix of a multivariate, elliptically symmetric distribution. It is relatively fast to compute and intuitively appealing. In this note we derive its innuence function and compute the asymptotic variances of its elements. A comparison with the one step reweighted MCD and with S-estimators ...
متن کاملRobustified distance based fuzzy membership function for support vector machine classification
Fuzzification of support vector machine has been utilized to deal with outlier and noise problem. This importance is achieved, by the means of fuzzy membership function, which is generally built based on the distance of the points to the class centroid. The focus of this research is twofold. Firstly, by taking the advantage of robust statistics in the fuzzy SVM, more emphasis on reducing the im...
متن کاملNonsingular Robust Covariance Estimation in Multivariate Outlier Detection
Rousseeuw’s minimum covariance determinant (MCD) method is a highly robust estimator of multivariate mean and covariance. In practice, the MCD covariance estimator may be singular. However, a nonsingular covariance estimator is required to calculate the Mahalanobis distance. In order to fix this singular problem, we propose an improved version of the MCD estimator, which is a combination of the...
متن کاملOn Multivariate Methods in Robust Econometrics
This work studies implicitly weighted robust statistical methods suitable for econometric problems. We study robust estimation mainly for the context of heteroscedasticity or high dimension, which are up-to-date topics of current econometrics. We describe a modifi cation of linear regression resistant to heteroscedasticity and study its computational aspects. For a robust version of the instrum...
متن کامل