Expected Information Matrix

The Expected Information Matrix is a theoretical construct in statistical inference that belongs to the class of information matrices and is specifically defined as the expected value of the second derivative (or Hessian) of the log-likelihood function with respect to the model parameters, serving to quantify the amount of information an observable random variable carries about the unknown parameters.

68

Publications

2.9K

Citations

111

Authors

86

Institutions

Publications per year

2017–2026

9

Variants

Fisher Information Matrix

Authors

111

  1. GM
    Gauss M. Cordeiro

    Universidade Federal de Pernambuco, Brazil

    8 publications · 293 citations

    8

    h-index

  2. AK
    André Klein

    University of Amsterdam, Netherlands

    5 publications · 83 citations

    5

    h-index

  3. PS
    Peter Spreij

    University of Amsterdam, Netherlands

    4 publications · 72 citations

    4

    h-index

  4. EM
    Edwin M. M. Ortega

    Universidade de São Paulo, Brazil

    3 publications · 158 citations

    3

    h-index

  5. AJ
    Artur J. Lemonte

    Universidade de São Paulo, Brazil

    3 publications · 95 citations

    3

    h-index

1–5 of 111

Venues

  1. Linear Algebra and its Applications

    5 publications · 83 citations

    5

    h-index

  2. The American Statistician

    5 publications · 135 citations

    5

    h-index

  3. Journal of Statistical Planning and Inference

    4 publications · 84 citations

    4

    h-index

  4. Structural Equation Modeling A Multidisciplinary Journal

    SEM · 3 publications · 502 citations

    3

    h-index

  5. Journal of Econometrics

    3 publications · 201 citations

    3

    h-index

1–5