Skip to main content
SpringerLink
Log in

Logistic Regression with Missing Values in the Covariates

  • Book
  • © 1994

Overview

Authors:
  1. Werner Vach

    1. Institut für Medizinische Biometrie Abteilung Medizinische Biometrie und Statistik, Klinikum der Albert-Ludwigs-Universität, Frieburg, Germany

    View author publications

    You can also search for this author in PubMed   Google Scholar

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

This is a preview of subscription content, log in via an institution to check access.

Access this book

Log in via an institution
eBook USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access

Licence this eBook for your library

Institutional subscriptions

Table of contents (13 chapters)

  1. Front Matter

    Pages i-x
    Download chapter PDF

  2. Logistic Regression With Two Categorical Covariates

    1. Introduction

      • Werner Vach
      Pages 1-3

  3. Logistic Regression With Two Categorical Covariates

    1. The Complete Data Case

      • Werner Vach
      Pages 4-5

    2. Missing Value Mechanisms

      • Werner Vach
      Pages 6-7

    3. Estimation Methods

      • Werner Vach
      Pages 8-25

    4. Quantitative Comparisons: Asymptotic Results

      • Werner Vach
      Pages 26-60

    5. Quantitative Comparisons: Results of Finite Sample Size Simulation Studies

      • Werner Vach
      Pages 61-72

    6. Examples

      • Werner Vach
      Pages 73-79

    7. Sensitivity Analysis

      • Werner Vach
      Pages 80-84

  4. Generalizations

    1. General Regression Models with Missing Values in One of Two Covariates

      • Werner Vach
      Pages 85-94

    2. Generalizations for More Than two Covariates

      • Werner Vach
      Pages 95-97

    3. Missing Values and Subsampling

      • Werner Vach
      Pages 98-102

    4. Further Examples

      • Werner Vach
      Pages 103-111

    5. Discussion

      • Werner Vach
      Pages 112-115

  5. Back Matter

    Pages 116-143
    Download chapter PDF
Back to top

Keywords

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.

Authors and Affiliations

  • Institut für Medizinische Biometrie Abteilung Medizinische Biometrie und Statistik, Klinikum der Albert-Ludwigs-Universität, Frieburg, Germany

    Werner Vach

Bibliographic Information

Publish with us

Policies and ethics

Back to top

Search

Navigation