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.

1.3K

Publications

84.9K

Citations

3.9K

Authors

1.4K

Institutions

Publications per year

2017–2026

314

Authors

3.9K

  1. JH
    John H. Kalivas

    Idaho State University, United States

    15 publications · 647 citations

    13

    h-index

  2. TN
    Tormod Næs

    Nofima, Norway

    10 publications · 1.3K citations

    10

    h-index

  3. AR
    Abdul Rohman

    Universitas Gadjah Mada, Indonesia

    9 publications · 302 citations

    9

    h-index

  4. CW
    Chris W. Brown

    University of Rhode Island, United States

    8 publications · 215 citations

    8

    h-index

  5. BH
    Bahram Hemmateenejad

    Shiraz University, Iran

    7 publications · 176 citations

    7

    h-index

1–5 of 3.9K

Venues

  1. Journal of Chemometrics

    59 publications · 3.5K citations

    31

    h-index

  2. Applied Spectroscopy

    53 publications · 2.4K citations

    26

    h-index

  3. The Analyst

    25 publications · 1.4K citations

    21

    h-index

  4. 17

    h-index

  5. Technometrics

    17 publications · 3.7K citations

    14

    h-index

1–5