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@@ -99,7 +99,7 @@ We compute the associated complete dataset :math:`\hat{X}^{(k)}` for the partial
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Evaluating the imputers requires to generate holes that are representative of the holes at hand.
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The missingness mechanisms have been classified by Rubin [1] into MCAR, MAR and MNAR.
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The missingness mechanisms have been classified by :ref:`Rubin [1]<rubin-article>` into MCAR, MAR and MNAR.
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Suppose we have :math:`X_{obs}`, a subset of a complete data model :math:`X = (X_{obs}, X_{mis})`, which is not fully observable (:math:`X_{mis}` is the missing part).
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We define the matrix :math:`M` such that :math:`M_{ij}=1` if :math:`X_{ij}` is missing, and 0 otherwise, and we assume distribution of :math:`M` is parametrised by :math:`\psi`.
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Finally, the observations are said to be Missing Not at Random (MNAR) in all other cases, i.e. if P(M | X_{obs}, X_{mis}, \psi) does not simplify.
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Finally, the observations are said to be Missing Not at Random (MNAR) in all other cases, i.e. if :math:`P(M | X_{obs}, X_{mis}, \psi)` does not simplify.
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Qolmat allows to generate new missing values on a an existing dataset, but only in the MCAR case.
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@@ -140,4 +140,7 @@ Qolmat can be used to search for hyperparameters in imputation functions. Let sa
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References
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[1] Rubin, Donald B. `Inference and missing data. <https://www.math.wsu.edu/faculty/xchen/stat115/lectureNotes3/Rubin%20Inference%20and%20Missing%20Data.pdf>`_ Biometrika 63.3 (1976): 581-592.
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.. _rubin-article:
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[1] Rubin, Donald B. `Inference and missing data. <https://www.math.wsu.edu/faculty/xchen/stat115/lectureNotes3/Rubin%20Inference%20and%20Missing%20Data.pdf>`_ Biometrika 63.3 (1976): 581-592.
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