Bayesian hierarchical modeling and analysis for actigraph data from wearable devices-Reference-Cited by-同舟云学术

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Bayesian hierarchical modeling and analysis for actigraph data from wearable devices

Published:2023-12-01 Issue:4 Volume:17 Page:
ISSN:1932-6157
Container-title:The Annals of Applied Statistics
language:
Short-container-title:Ann. Appl. Stat.

Author:

Alaimo Di Loro Pierfrancesco¹,Mingione Marco²,Lipsitt Jonah³,Batteate Christina M.⁴,Jerrett Michael³,Banerjee Sudipto⁵

Affiliation:

1. Department GEPLI, LUMSA

2. Department of Political Sciences, Roma Tre University

3. Department of Environmental Health Sciences, University of California, Los Angeles

4. Center of Occupational and Environmental Health, University of California, Los Angeles

5. Department of Biostatistics, University of California, Los Angeles

Publisher

Institute of Mathematical Statistics

Subject

Statistics, Probability and Uncertainty,Modeling and Simulation,Statistics and Probability

Reference53 articles.

1. Eilers, P. H. C. and Marx, B. D. (1996). Flexible smoothing with B-splines and penalties. Statist. Sci. 11 89–121.

2. PERUZZI, M., BANERJEE, S. and FINLEY, A. O. (2022). Highly scalable Bayesian geostatistical modeling via meshed Gaussian processes on partitioned domains. J. Amer. Statist. Assoc. 117 969–982.

3. KATZFUSS, M., GUINNESS, J., GONG, W. and ZILBER, D. (2020). Vecchia approximations of Gaussian-process predictions. J. Agric. Biol. Environ. Stat. 25 383–414.

4. Liu, J. S., Wong, W. H. and Kong, A. (1994). Covariance structure of the Gibbs sampler with applications to the comparisons of estimators and augmentation schemes. Biometrika 81 27–40.

5. CRESSIE, N. and WIKLE, C. K. (2011). Statistics for Spatio-Temporal Data. Wiley Series in Probability and Statistics. Wiley, Hoboken, NJ.

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