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Accurate Regression Parameters and Summary Statistics Estimation in Data with Censored Missing Values Yuliya Karpievitch Imputation And Censored Values Yuliya Karpievitch Missing Values and Imputation Some algorithms expect a


  1. Accurate Regression Parameters and Summary Statistics Estimation in Data with Censored Missing Values Yuliya Karpievitch

  2. Imputation And Censored Values Yuliya Karpievitch

  3. Missing Values and Imputation • Some algorithms expect a ‘complete’ matrix • Differential Expression Analysis is affected by missing values • Censored values are lower than the observed ones • Simple imputation methods are not good enough

  4. Example Protein

  5. Simple Imputation – Detection Limit

  6. Simple Imputation - Mean

  7. Model-based Imputation

  8. ROC Curve for Numerical Estimation

  9. Half of the Detection Limit

  10. NORM

  11. NORM Squeezed

  12. Estimating Regression Coefficients

  13. Estimating the Mean

  14. Collaborators: Firouzeh Noghrehchi John McKinlay

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