Combining non-parametric models with logistic regression: an application to motor vehicle injury data

Kuhnert, PM, Do, KA and McClure, R (2000) Combining non-parametric models with logistic regression: an application to motor vehicle injury data. Computational statistics & Data Analysis, 34 3: 371-386. doi:10.1016/S0167-9473(99)00099-7


Author Kuhnert, PM
Do, KA
McClure, R
Title Combining non-parametric models with logistic regression: an application to motor vehicle injury data
Journal name Computational statistics & Data Analysis   Check publisher's open access policy
ISSN 0167-9473
Publication date 2000
Sub-type Article (original research)
DOI 10.1016/S0167-9473(99)00099-7
Volume 34
Issue 3
Start page 371
End page 386
Total pages 16
Place of publication Amsterdam
Publisher Elsevier Science
Collection year 2000
Language eng
Subject C1
321201 Environmental and Occupational Health and Safety
730220 Injury control
Abstract To date, computer-intensive non-parametric modelling procedures such as classification and regression trees (CART) and multivariate adaptive regression splines (MARS) have rarely been used in the analysis of epidemiological studies. Most published studies focus on techniques such as logistic regression to summarise their results simply in the form of odds ratios. However flexible, non-parametric techniques such as CART and MARS can provide more informative and attractive models whose individual components can be displayed graphically. An application of these sophisticated techniques in the analysis of an epidemiological case-control study of injuries resulting from motor vehicle accidents has been encouraging. They have not only identified potential areas of risk largely governed by age and number of years driving experience but can also identify outlier groups and can be used as a precursor to a more detailed logistic regression analysis. (C) 2000 Elsevier Science B.V. All rights reserved.
Keyword Computer Science, Interdisciplinary Applications
Mathematics, Applied
Statistics & Probability
Classification And Regression Trees
Injury
Logistic Regression
Multivariate Adaptive Regression Splines
Recursive Partitioning
Classification Trees
Neural Networks
Ovarian-cancer
Risk-factors
Splines
Mars
Q-Index Code C1

Document type: Journal Article
Sub-type: Article (original research)
Collection: School of Mathematics and Physics
 
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Created: Tue, 10 Jun 2008, 11:31:50 EST