Multivariate and functional robust fusion methods for structured Big Data

被引：4

作者：

Aaron, Catherine ^{[1
]}

Cholaquidis, Alejandro ^{[2
,3
]}

Fraiman, Ricardo ^{[2
,3
,4
]}

Ghattas, Badih ^{[5
]}

机构：

[1] Univ Clermont Auvergne, Campus Univ Cezeaux, Aubiere, France

[2] Univ Republica, CABIDA, Montevideo, Uruguay

[3] Univ Republica, Ctr Matemat, Montevideo, Uruguay

[4] Inst Pasteur Montevideo, Montevideo, Uruguay

[5] Aix Marseille Univ, CNRS, Cent Marseille, I2M,UMR 7373, F-13453 Marseille, France

来源：

JOURNAL OF MULTIVARIATE ANALYSIS | 2019年 / 170卷

关键词：

Big data; Clustering; Functional data; Robustness; QUANTILES;

D O I：

10.1016/j.jmva.2018.06.012

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

We address one of the important problems in Big Data, namely how to combine estimators from different subsamples by robust fusion procedures, when we are unable to deal with the whole sample. We propose a general framework based on the classic idea of 'divide and conquer'. In particular we address in some detail the case of a multivariate location and scatter matrix, the covariance operator for functional data, and clustering problems. (C) 2018 Elsevier Inc. All rights reserved.

引用

页码：149 / 161

页数：13