Autocorrelation Models
Autocorrelation Models is a statistical framework within longitudinal data analysis that characterizes the temporal dependency structure among repeated measurements by modeling the correlation of a variable with itself across different time points, distinguishing it from other covariance structures through its explicit specification of lag-based dependencies.
673
Publications
43.7K
Citations
1.7K
Authors
778
Institutions
Publications per year
2017–2026
109
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Temporal Autocorrelation in Univariate Linear Modeling of FMRI Data
Mark W. Woolrich, B. D. Ripley, Michael Brady et al. · NeuroImage · 2001 · 3K citations
M. Golay · IEEE Transactions on Information Theory · 1961 · 1.5K citations
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Centre National de la Recherche Scientifique
Paris, France
10 h-index
17 publications
490 citations

Monash University
Melbourne, Australia
9 h-index
17 publications
971 citations
University of California, Santa Barbara
Santa Barbara, United States
8 h-index
16 publications
737 citations
Massachusetts Institute of Technology
Cambridge, United States
7 h-index
11 publications
1K citations
North Carolina State University
Raleigh, United States
7 h-index
11 publications
343 citations
University of Washington
Seattle, United States
6 h-index
10 publications
588 citations
Western Michigan University
Kalamazoo, United States
6 h-index
8 publications
625 citations

University of Alberta
Edmonton, Canada
5 h-index
9 publications
330 citations
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JASA · 15 publications · 558 citations
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10 publications · 464 citations
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10 publications · 1.1K citations
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Repository · 9 publications · 341 citations
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8 publications · 508 citations
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