Journal article
Randomized quantile residuals
Journal of Computational and Graphical Statistics, Vol.5(3), pp.236-244
1996
Abstract
In this paper we give a general definition of residuals for regression models with independent responses. Our definition produces residuals which are exactly normal, apart from sampling variability in the estimated parameters, by inverting the fitted distribution function for each response value and finding the equivalent standard normal quantile. Our definition includes some randomization to achieve continuous residuals when the response variable is discrete. Quantile residuals are easily computed in computer packages such as SAS, S-Plus, GLIM or LispStat, and allow residual analyses to be carried out in many commonly occurring situations in which the customary definitions of residuals fail. Quantile residuals are applied in this paper to three example data sets.
Details
- Title
- Randomized quantile residuals
- Authors
- Peter K Dunn (Author) - University of QueenslandG K Smyth (Author) - University of Queensland
- Publication details
- Journal of Computational and Graphical Statistics, Vol.5(3), pp.236-244
- Publisher
- American Statistical Association
- Date published
- 1996
- DOI
- 10.1080/10618600.1996.10474708
- ISSN
- 1061-8600
- Copyright note
- Copyright © 1996 The Author. This is an Accepted Manuscript of an article published in the Journal of Computational and Graphical Statistics online [February 21, 2012], available online: http://www.tandfonline.com/doi/abs/10.1080/10618600.1996.10474708
- Organisation Unit
- University of the Sunshine Coast, Queensland; School of Health and Sport Sciences - Legacy; School of Science, Technology and Engineering
- Language
- English
- Record Identifier
- 99449465702621
- Output Type
- Journal article
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