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On the Use of Growth Models to Study Normal Cognitive Aging.

Published on Jun 5, 2019in International Journal of Behavioral Development
· DOI :10.1177/0165025419851576
Paolo Ghisletta29
Estimated H-index: 29
,
Fabio Mason1
Estimated H-index: 1
+ 3 AuthorsUlman Lindenberger86
Estimated H-index: 86
(MPG: Max Planck Society)
Abstract
Growth models (GM) of the mixed-effects and latent curve varieties have become popular methodological tools in lifespan research. One of the major advantages of GM is their flexibility in studying ...
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  • Citations (2)
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#1Julian D. Karch (LEI: Leiden University)
#1Julian David Karch (LEI: Leiden University)H-Index: 4
Last. Manuel C. Voelkle (Humboldt University of Berlin)H-Index: 16
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In this article, we extend the Bayesian nonparametric regression method Gaussian Process Regression to the analysis of longitudinal panel data. We call this new approach Gaussian Process Panel Modeling (GPPM). GPPM provides great flexibility because of the large number of models it can represent. It allows classical statistical inference as well as machine learning inspired predictive modeling. GPPM offers frequentist and Bayesian inference without the need to resort to Markov chain Monte Carlo-...
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#1Andreas M. Brandmaier (MPG: Max Planck Society)H-Index: 15
#2Paolo Ghisletta (University of Geneva)H-Index: 29
Last. Timo von Oertzen (MPG: Max Planck Society)H-Index: 18
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Longitudinal data collection is a time-consuming and cost-intensive part of developmental research. Wu et al. (2016) discussed planned missing (PM) designs that are similar in efficiency to complete designs but require fewer observations per person. The authors reported optimal PM designs for linear latent growth curve models based on extensive Monte Carlo simulations. They called for further formal investigation of the question as to how much the proposed PM mechanisms influence study design ef...
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