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27 results for “Autoregressive models”
Figure 9 in Spatiotemporal patterns in marine fish and cephalopods communities across scales: using an autoregressive spatiotemporal clustering model. A study of fish and cephalopods of the Eastern English Channel
Figure 9. – Eastern English Channel absolute spatiotemporal community from low (blue) to high (red) median densities of numbers/ km2 in log scale for communities, AST522c1 (A), AST522c2 (B).
Figure 8 in Spatiotemporal patterns in marine fish and cephalopods communities across scales: using an autoregressive spatiotemporal clustering model. A study of fish and cephalopods of the Eastern English Channel
Figure 8. – Eastern English Channel spatiotemporal community from low (blue) to high (red) median densities of numbers/ km2 in log scale for communities ST522c1 (A), ST522c2 (B).
Figure S6 in Spatiotemporal patterns in marine fish and cephalopods communities across scales: using an autoregressive spatiotemporal clustering model. A study of fish and cephalopods of the Eastern English Channel
Figure S6. – Eastern English Channel spatiotemporal community from low (blue) to high (red) median densities of numbers/ km2 in log scale for communities ST782c1 (A), ST782c2 (B), ST1043c1 (C), ST1043c2 (D).
Figure S1 in Spatiotemporal patterns in marine fish and cephalopods communities across scales: using an autoregressive spatiotemporal clustering model. A study of fish and cephalopods of the Eastern English Channel
Figure S1. – Spatial correlation matrix at a 782 km2 (A) and 1043 km2 (B) scale displaying correlation from strongly negative (dark blue) to strongly positive (dark red).
Modeling of non-stationary autoregressive alpha-stable processe
In the literature, impulsive signals are mostly modeled by symmetric alpha-stable processes. To represent their temporal dependencies, usually autoregressive models with time-invariant coefficients are utilized. We propose a general sequential Bayesian modeling methodology where both unknown autoregressive coefficients and distribution parameters can be estimated successfully, even when they are time-varying. In contrast to most work in the literature on signal processing with alpha-stable distributions, our work is general and models also skewed alpha-stable processes. Successful performance of our method is demonstrated by computer simulations. We support our empirical results by providing posterior Cramer–Rao lower bounds. The proposed method is also tested on a practical application where seismic data events are modeled.
Visegrad FDI determinants in China and India: A panel autoregressive distributed lag model analysis
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Modeling non-Gaussian time-varying vector autoregressive process
We present a novel and general methodology for modeling time-varying vector autoregressive processes which are widely used in many areas such as modeling of chemical processes, mobile communication channels and biomedical signals. In the literature, most work utilize multivariate Gaussian models for the mentioned applications, mainly due to the lack of efficient analytical tools for modeling with non-Gaussian distributions. In this paper, we propose a particle filtering approach which can model non-Gaussian autoregressive processes having cross-correlations among them. Moreover, time-varying parameters of the process can be modeled as the most general case by using this sequential Bayesian estimation method. Simulation results justify the performance of the proposed technique, which potentially can model also Gaussian processes as a sub-case.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.