Principal Component Regression
Principal Component Regression is a regression technique that performs dimensionality reduction via principal component analysis on the predictor variables before applying linear regression, thereby distinguishing itself from standard linear regression by using orthogonal components to mitigate multicollinearity and reduce overfitting.
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2017–2026
314
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A Statistical View of Some Chemometrics Regression Tools
lldiko E. Frank, Jerome H. Friedman · Technometrics · 1993 · 2.2K citations
Regression modelling strategies for improved prognostic prediction
Frank E. Harrell, Kerry L. Lee, Robert M. Califf et al. · Statistics in Medicine · 1984 · 1.8K citations
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University of Washington
Seattle, United States
15 h-index
28 publications
3.5K citations
Columbia University
New York, United States
13 h-index
28 publications
1.8K citations
Idaho State University
Pocatello, United States
13 h-index
29 publications
1.3K citations

Cairo University
Giza, Egypt
11 h-index
35 publications
650 citations
University of North Carolina at Chapel Hill
Chapel Hill, United States
10 h-index
35 publications
6.3K citations

Universitas Syiah Kuala
Banda Aceh, Indonesia
9 h-index
26 publications
646 citations

University of Alberta
Edmonton, Canada
9 h-index
16 publications
895 citations
University of Rhode Island
Kingston, United States
9 h-index
18 publications
499 citations
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59 publications · 3.5K citations
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53 publications · 2.4K citations
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20 publications · 1K citations
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