Answers.Body: <p>MICE is a Multiple imputation method, it contains three phases:</p>
  <ul>
  <li>Imputation phase: The missing data are estimated M times from a specific model to obtain M complete and potentially different data sets.</li>
  <li>Separate analysis phase: The selected analysis is performed separately on each of m = 1, ..., M sets of imputed data to obtain estimates (central tendency and variance).</li>
  <li>Combined analysis phase: The results obtained  are combined according to the rules established by Rubin to obtain a single final estimate.</li>
  </ul>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2021-02-28T10:38:33.243Z
Answers.LastActivityDate: 2021-02-28T10:38:33.243Z
Answers.UserDisplayName: souum
Body: <p>I'm trying to understand <em>Multiple Imputation with Chained Equation (MICE)</em> imputation process (a statistical method for imputing missing data). I have read some articles and I have understood how the imputation happens, but I didn't get the &quot;pooling&quot; step.</p>
  <p>After analyzing the resulting datasets with Rubin's rules, how to pool these datasets? How to get only one dataset?</p>
  <p>In the end, do I combine all these datasets? If yes, how? Or do I compare every dataset's estimators with Rubin's estimators and choose one dataset?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-03-03T09:52:18.607Z
Comments.Text: I have edited your post to clarify what your question seemed to be. You were using the term "pooling", but I'm not sure this is the right term for what you were asking. Pooling is more commonly used in the context of CNNs, so it can be a misleading term. Please, make sure that the question in the title was your main specific question. If not, feel free to edit your post again to put in the title your main **specific** question.
Comments.UserDisplayName: nbro
Tags: <machine-learning><datasets>
Title: How exactly does MICE imputation combine multiple datasets into one?
UserDisplayName: souum
ThreadText: # How exactly does MICE imputation combine multiple datasets into one?
  
  (Asked by: username_0 on 2020-01-08)
  
  I'm trying to understand *Multiple Imputation with Chained Equation (MICE)* imputation process (a statistical method for imputing missing data). I have read some articles and I have understood how the imputation happens, but I didn't get the "pooling" step.
  
  After analyzing the resulting datasets with Rubin's rules, how to pool these datasets? How to get only one dataset?
  
  In the end, do I combine all these datasets? If yes, how? Or do I compare every dataset's estimators with Rubin's estimators and choose one dataset?
  
  **Tags:** machine-learning, datasets
  > **Comments:**
  > **username_1**: I have edited your post to clarify what your question seemed to be. You were using the term "pooling", but I'm not sure this is the right term for what you were asking. Pooling is more commonly used in the context of CNNs, so it can be a misleading term. Please, make sure that the question in the title was your main specific question. If not, feel free to edit your post again to put in the title your main **specific** question.  
  
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  ## 1 answers
  
  ### Answer by username_0 on 2021-02-28. Score: 0
  
  MICE is a Multiple imputation method, it contains three phases:
  
  * Imputation phase: The missing data are estimated M times from a specific model to obtain M complete and potentially different data sets.
  * Separate analysis phase: The selected analysis is performed separately on each of m = 1, ..., M sets of imputed data to obtain estimates (central tendency and variance).
  * Combined analysis phase: The results obtained are combined according to the rules established by Rubin to obtain a single final estimate.
  
  
  ---
  
  