Minimum covariance determinant and extensions
نویسندگان
چکیده
منابع مشابه
The minimum weighted covariance determinant estimator
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 investi...
متن کاملOutlier 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 کاملA Fast Algorithm for the Minimum Covariance Determinant Estimator
The minimum covariance determinant (MCD) method of Rousseeuw (1984) is a highly robust estimator of multivariate location and scatter. Its objective is to nd h observations (out of n) whose covariance matrix has the lowest determinant. Until now applications of the MCD were hampered by the computation time of existing algorithms, which were limited to a few hundred objects in a few dimensions. ...
متن کاملRelaxMCD: Smooth optimisation for the Minimum Covariance Determinant estimator
The Minimum Covariance Determinant (MCD) estimator is a highly robust procedure for estimating the centre and shape of a high dimensional data set. It consists of determining a subsample of h points out of nwhichminimises the generalised variance. By definition, the computation of this estimator gives rise to a combinatorial optimisation problem, forwhich several approximate algorithms have bee...
متن کاملAsymptotic expansion of the minimum covariance determinant estimators
In Cator and Lopuhaä [3] an asymptotic expansion for the MCD estimators is established in a very general framework. This expansion requires the existence and non-singularity of the derivative in a first-order Taylor expansion. In this paper, we prove the existence of this derivative for multivariate distributions that have a density and provide an explicit expression. Moreover, under suitable s...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: WIREs Computational Statistics
سال: 2017
ISSN: 1939-5108,1939-0068
DOI: 10.1002/wics.1421