Cover Image

Logistic Regression with Missing Values in the Covariates / by Werner Vach.

Show other versions (1)

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 missi...

Full description

Saved in:
Bibliographic Details
Online Access: Full Text (via Springer)
Main Author: Vach, Werner
Format: eBook
Language:English
Published: New York, NY : Springer New York, 1994.
Series:Lecture notes in statistics (Springer-Verlag) ; 86.
Subjects:
Mathematics.
Statistics.
Table of Contents:
  • 1. Introduction
  • I: Logistic Regression with Two Categorical Covariates
  • 2. The complete data case
  • 3. Missing value mechanisms
  • 4. Estimation methods
  • 5. Quantitative comparisons: Asymptotic results
  • 6. Quantitative comparisons: Results from finite sample size simulation studies
  • 7. Examples
  • 8. Sensitivity analysis
  • II: Generalizations
  • 9. General regression models with missing values in one of two covariates
  • 10. Generalizations for more than two covariates
  • 11. Missing values and subsampling
  • 12. Further Examples
  • 13. Discussion
  • Appendices
  • A. 1 ML Estimation in the presence of missing values A.2 The EM algorithm
  • B. 1 Explicit representation of the score function of ML Estimation and the information matrix in the complete data case
  • B. 2 Explicit representation of the score function of ML Estimation and the information matrix
  • B. 3 Explicit representation of the quantities used for the asymptotic variance of the PML estimates
  • B. 4 Explicit representation of the quantities used for the asymptotic variance of the estimates of the Filling method
  • References
  • Notation Index.

Similar Items