PROJECTED PRINCIPAL COMPONENT ANALYSIS IN FACTOR MODELS

• 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

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...