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Graciela Muniz Terrera on Muck Rack

Graciela Muniz Terrera

As seen in: BMJ Open, Journal of Epidemiology and Community Health, Taylor & Francis Online, International Journal of Epidemiology, Dialnet - Universidad de La Rioja
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Articles

Statistical Quantile Learning for Large Additive Latent Variable Models

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By Julien Bodelet, Guillaume Blanc, Jiajun Shan, Graciela Muniz Terrera
| Taylor & Francis Online
?Mathematical formulae have been encoded as MathML and are displayed in this HTML version using MathJax in order to improve their display. Uncheck the box to turn MathJax off. This feature requires Javascript. Click on a formula to zoom. Abstract Large and complex datasets, emerging from technological advancements in fields such as genomics and brain imaging, hold ample promise for gaining new scientific insights.
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Modeling Risk Factors for Intraindividual Variability: A Mixed-Effects Beta-Binomial Model Applied to Cognitive Function in Older People in the English Longitudinal Study of Ageing

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By Kate Tilling, Graciela Muniz Terrera, Jessica Barrett, Richard M A
| Dialnet - Universidad de La Rioja
Modeling Risk Factors for Intraindividual Variability: A Mixed-Effects Beta-Binomial Model Applied to Cognitive Function in Older People in the English Longitudinal Study of Ageing Autores: Richard M A Parker, Kate Tilling, Graciela Muniz Terrera, Jessica Barrett Localización: American journal of epidemiology, ISSN-e 1476-6256, ISSN 0002-9262, Vol. 193, Nº. 1, 2024, págs.
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Longitudinal observational cohort study: Speech for Intelligent cognition change tracking and DEtection of Alzheimer’s Disease (SIDE-AD)

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By Stina Saunders, Fasih Ullah Haider, Graciela Muniz Terrera, Craig Ritchie
| BMJ Open Verified
Methods and analysis This is a remote observational longitudinal study developing speech biomarkers for use in neurodegenerative disease. The SIDE-AD study involves collecting voice data using an online platform and prompting individuals to talk about brain health (figure 1), using protocol V.4, dated 29 November 2023. The selection of the participant population aims to create a risk spectrum of individuals with a low to high risk of AD.
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