Vach W. Logistic Regression with Missing Values in the Covariates

New York: Springer, 1994. — 151 p. — ISBN: 9780387942636.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.The Complete Data Case
Missing Value Mechanisms
Estimation Methods
Quantitative Comparisons: Asymptotic Results
Quantitative Comparisons: Results of Finite Sample Size Simulation Studies
Examples
Sensitivity Analysis
General Regression Models with Missing Values in One of Two Covariates
Generalizations for More Than two Covariates
Missing Values and Subsampling
Further Examples
Discussion