PROJECTED PRINCIPAL COMPONENT ANALYSIS IN FACTOR MODELS

This paper introduces a Projected Principal Component Analysis (Projected-PCA), which employees principal component analysis to the projected (smoothed) data matrix onto a given linear space spanned by covariates. When it applies to high-dimensional factor analysis, the projection removes noise comp...

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Bibliographic Details

Published in:	The Annals of statistics Vol. 44; no. 1; p. 219
Main Authors:	Fan, Jianqing, Liao, Yuan, Wang, Weichen
Format:	Journal Article
Language:	English
Published:	United States 01.02.2016
Subjects:	loading matrix modeling sieve approximation high dimensionality rates of covergence semi-parametric factor models
ISSN:	0090-5364
Online Access:	Get more information
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