Logistic Regression With Missing Values In The Covariates
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Logistic Regression with Missing Values in the Covariates
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.
Multiple Imputation of Missing Data Using SAS
Written for users with an intermediate background in SAS programming and statistics, this book is an excellent resource for anyone seeking guidance on multiple imputation. It provides both theoretical background and practical solutions for those working with incomplete data sets in an engaging example-driven format.
Statistical Modeling for Biomedical Researchers
Author: William D. Dupont
language: en
Publisher: Cambridge University Press
Release Date: 2009-02-12
A second edition of the easy-to-use standard text guiding biomedical researchers in the use of advanced statistical methods.