Multivariate Analysis Of Variance

Multivariate Analysis of Variance is a statistical method that extends univariate analysis of variance by simultaneously testing the effects of one or more independent variables on two or more dependent variables under the assumption of multivariate normality and homogeneity of covariance matrices.

1K

Publications

214.5K

Citations

1.5K

Authors

614

Institutions

Publications per year

2017–2026

58

Authors

1.5K

  1. TW
    T. W. Anderson

    Stanford University, United States

    15 publications · 14.3K citations

    14

    h-index

  2. MS
    Muni S. Srivastava

    University of Toronto, Canada

    12 publications · 1.2K citations

    12

    h-index

  3. SR
    Sasanka Roy

    Indian Statistical Institute, India

    12 publications · 2.7K citations

    11

    h-index

  4. PK
    Pranab Kumar Sen

    University of North Carolina at Chapel Hill, United States

    10 publications · 1.5K citations

    10

    h-index

  5. JP
    James P. Stevens

    Children's Healthcare of Atlanta, United States

    9 publications · 5.5K citations

    9

    h-index

1–5 of 1.5K

Venues

  1. Technometrics

    73 publications · 16.9K citations

    45

    h-index

  2. Journal of the American Statistical Association

    82 publications · 13.4K citations

    41

    h-index

  3. Biometrika

    43 publications · 8.9K citations

    28

    h-index

  4. Biometrics

    40 publications · 18K citations

    26

    h-index

  5. Journal of Multivariate Analysis

    44 publications · 2.2K citations

    25

    h-index

1–5