Principal component regression for data containing outliers and missing elements

被引：23

作者：

Serneels, Sven ^{[2
]}

Verdonck, Tim ^{[1
]}

机构：

[1] Univ Antwerp, Dept Math & Comp Sci, Agoras Grp, B-2020 Antwerp, Belgium

[2] LS Serv & Consultancy, Edegem, Belgium

来源：

COMPUTATIONAL STATISTICS & DATA ANALYSIS | 2009年 / 53卷 / 11期

关键词：

MULTIVARIATE REGRESSION; ROBUST; ESTIMATOR; PROJECTION; INFERENCE;

D O I：

10.1016/j.csda.2009.04.008

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

A methodology is presented to construct an expectation robust algorithm for principal component regression. The presented method is the first multivariate regression method which can resist outliers and which can cope with missing elements in the data simultaneously. Simulations and an example illustrate the good statistical properties of the method. (C) 2009 Elsevier B.V. All rights reserved.

引用

页码：3855 / 3863

页数：9

共 50 条

[21] Empirical likelihood for semiparametric regression model with missing response data [J].

Xue, Liugen ;

Xue, Dong .

JOURNAL OF MULTIVARIATE ANALYSIS, 2011, 102 (04) :723-740

[22] QUANTILE REGRESSION FOR COMPETING RISKS DATA WITH MISSING CAUSE OF FAILURE [J].

Sun, Yanqing ;

Wang, Huixia Judy ;

Gilbert, Peter B. .

STATISTICA SINICA, 2012, 22 (02) :703-728

[23] Robust PLS approach for KPI-related prediction and diagnosis against outliers and missing data [J].

Yin, Shen ;

Wang, Guang ;

Yang, Xu .

INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE, 2014, 45 (07) :1375-1382

[24] Deconstructing principal component analysis using a data reconciliation perspective [J].

Narasimhan, Shankar ;

BhattSystems, Nirav .

COMPUTERS & CHEMICAL ENGINEERING, 2015, 77 :74-84

[25] PRINCIPAL COMPONENT ANALYSIS FOR FUNCTIONAL DATA ON RIEMANNIAN MANIFOLDS AND SPHERES [J].

Dai, Xiongtao ;

Mueller, Hans-Georg .

ANNALS OF STATISTICS, 2018, 46 (6B) :3334-3361

[26] Nonlinear α-Regression Quantile Using Kernel Principal Component Analysis and Adaptive Genetic Algorithm [J].

Wibowo, Antoni .

2018 INDONESIAN ASSOCIATION FOR PATTERN RECOGNITION INTERNATIONAL CONFERENCE (INAPR), 2018, :80-84

[27] A Study of Complex Industrial Systems Based on Revised Kernel Principal Component Regression Method [J].

Sun, Chengyuan ;

Ma, Hongjun .

IFAC PAPERSONLINE, 2020, 53 (02) :133-138

[28] Reconstruction of critical coalbed methane logs with principal component regression model: A case study [J].

Li, Wan ;

Chen, Tongjun ;

Song, Xiong ;

Gong, Tianqi ;

Liu, Mengyue .

ENERGY EXPLORATION & EXPLOITATION, 2020, 38 (04) :1178-1193

[29] Quality-related fault detection using linear and nonlinear principal component regression [J].

Wang, Guang ;

Luo, Hao ;

Peng, Kaixiang .

JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS, 2016, 353 (10) :2159-2177

[30] Quality-Related Process Monitoring Based on Improved Kernel Principal Component Regression [J].

Qi, Li ;

Yi, Xiaoyun ;

Yao, Lina ;

Fang, Yixian ;

Ren, Yuwei .

IEEE ACCESS, 2021, 9 :132733-132745

← 1 2 3 4 5 →