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image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao zbMATH Openarrow_drop_down
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Article
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Biometrika
Article . 1975 . Peer-reviewed
Data sources: Crossref
Biometrika
Article . 1975 . Peer-reviewed
Data sources: Crossref
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A Bayesian Approach to Growth Curves

A Bayesian approach to growth curves
Authors: Fearn, T.;

A Bayesian Approach to Growth Curves

Abstract

SUMMARY Recent work on the analysis of growth curves has concentrated on the generalized growth model put forward by Potthoff & Roy (1964), the model being studied from a Bayesian viewpoint by Geisser (1970). The analysis of such data however falls naturally within the scope of the general Bayesian linear model proposed and analyzed by Lindley & Smith (1972). Here we apply the theory developed by Lindley & Smith to the estimation problem for growth curves, and also consider the problem of prediction, given a sample from this model.

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Keywords

Linear regression; mixed models, Bayesian inference, Applications of statistics to biology and medical sciences; meta analysis

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
68
Average
Top 1%
Average
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