Multivariate Process Analysis

Multivariate Process Analysis is a statistical methodology that distinguishes itself from univariate or bivariate approaches by simultaneously analyzing multiple interrelated process variables to identify complex patterns, correlations, and causal relationships within dynamic systems, particularly for monitoring, controlling, or optimizing critical process parameters in industrial and scientific applications.

573

Publications

35.9K

Citations

1.2K

Authors

448

Institutions

Publications per year

2017–2026

158

Authors

1.2K

  1. JF
    John F. MacGregor

    McMaster University, Canada

    20 publications · 5.5K citations

    16

    h-index

  2. XY
    Xuefeng Yan

    East China University of Science and Technology, China

    14 publications · 1.3K citations

    14

    h-index

  3. FG
    Furong Gao

    Hong Kong University of Science and Technology, Hong Kong SAR China

    14 publications · 902 citations

    13

    h-index

  4. CZ
    Chunhui Zhao

    State Key Laboratory of Industrial Control Technology

    13 publications · 724 citations

    13

    h-index

  5. BH
    Biao Huang

    University of Alberta, Canada

    12 publications · 1.3K citations

    12

    h-index

1–5 of 1.2K

Venues

  1. Chemometrics and Intelligent Laboratory Systems

    32 publications · 3.1K citations

    27

    h-index

  2. Journal of Process Control

    40 publications · 2.6K citations

    25

    h-index

  3. Industrial & Engineering Chemistry Research

    45 publications · 2.1K citations

    25

    h-index

  4. Journal of Quality Technology

    26 publications · 2.9K citations

    20

    h-index

  5. AIChE Journal

    20 publications · 3.7K citations

    18

    h-index

1–5