Abstract
Abstract : The problem that a relatively simple analysis is changed into a complex one just because some of the information is missing, is one which faces most practicing statisticians at some point in their career. Obviously the best way to treat missing information problems is not to have them. Unfortunately circumstances arise in which information is missing and nothing can be done to replace it for one reason or another.
Keywords
Related Publications
MCMC Methods for Multi-Response Generalized Linear Mixed Models: The<b>MCMCglmm</b><i>R</i>Package
Generalized linear mixed models provide a flexible framework for modeling a range of data, although with non-Gaussian response variables the likelihood cannot be obtained in clo...
Why Propensity Scores Should Not Be Used for Matching
We show that propensity score matching (PSM), an enormously popular method of preprocessing data for causal inference, often accomplishes the opposite of its intended goal—thus ...
Singular Integrals and Differentiability Properties of Functions.
Singular integrals are among the most interesting and important objects of study in analysis, one of the three main branches of mathematics. They deal with real and complex numb...
A Modified Principal Component Technique Based on the LASSO
In many multivariate statistical techniques, a set of linear functions of the original p variables is produced. One of the more difficult aspects of these techniques is the inte...
Variable selection – A review and recommendations for the practicing statistician
Abstract Statistical models support medical research by facilitating individualized outcome prognostication conditional on independent variables or by estimating effects of risk...
Publication Info
- Year
- 1972
- Type
- book-chapter
- Pages
- 697-716
- Citations
- 454
- Access
- Closed
External Links
Social Impact
Social media, news, blog, policy document mentions
Citation Metrics
Cite This
Identifiers
- DOI
- 10.1525/9780520325883-036