© 1994

Logistic Regression with Missing Values in the Covariates


Part of the Lecture Notes in Statistics book series (LNS, volume 86)

Table of contents

  1. Front Matter
    Pages i-x
  2. Logistic Regression With Two Categorical Covariates

    1. Werner Vach
      Pages 1-3
  3. Logistic Regression With Two Categorical Covariates

    1. Werner Vach
      Pages 4-5
    2. Werner Vach
      Pages 6-7
    3. Werner Vach
      Pages 8-25
    4. Werner Vach
      Pages 73-79
    5. Werner Vach
      Pages 80-84
  4. Generalizations

    1. Werner Vach
      Pages 98-102
    2. Werner Vach
      Pages 103-111
    3. Werner Vach
      Pages 112-115
  5. Back Matter
    Pages 116-143

About this book


In many areas of science a basic task is to assess the influence of several factors on a quantity of interest. If this quantity is binary logistic, regression models provide a powerful tool for this purpose. This monograph presents an account of the use of logistic regression in the case where missing values in the variables prevent the use of standard techniques. Such situations occur frequently across a wide range of statistical applications.
The emphasis of this book is on methods related to the classical maximum likelihood principle. The author reviews the essentials of logistic regression and discusses the variety of mechanisms which might cause missing values while the rest of the book covers the methods which may be used to deal with missing values and their effectiveness. Researchers across a range of disciplines and graduate students in statistics and biostatistics will find this a readable account of this.


Conditional probability Finite Likelihood Logistic Regression Variance expectation–maximization algorithm function variable

Authors and affiliations

  1. 1.Institut für Medizinische Biometrie Abteilung Medizinische Biometrie und StatistikKlinikum der Albert-Ludwigs-UniversitätFrieburgGermany

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