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Wiley Interdisciplinary Reviews:
WIREs Computational Statistics
Volume 13 Issue 2 (March 2021)
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Cover Image, Volume 13, Issue 2
Published Online: Feb 05 2021
DOI: 10.1002/wics.1552
The cover image is based on the Advance Review Differential network analysis: A statistical perspective by Ali Shojaie., https://doi.org/10.1002/wics.1508.
Abstract Full article on Wiley Online Library:   HTML | PDF

Advanced Reviews

Aggregating predictions from experts: A review of statistical methods, experiments, and applications
Published Online: Jun 16 2020
DOI: 10.1002/wics.1514
Expert judgmental forecasts—models that combine expert‐generated predictions into a single forecast—can make predictions when training data islimited and directly involve decision makers in the prediction process.We give an updated review of aggregating expert predictions in the digital age and recommendations for how to improve future work in this field.
Abstract Full article on Wiley Online Library:   HTML | PDF
Parallel computing with R: A brief review
Published Online: Jun 15 2020
DOI: 10.1002/wics.1515
Abstract Full article on Wiley Online Library:   HTML | PDF
30 Years of space–time covariance functions
Published Online: May 20 2020
DOI: 10.1002/wics.1512
A separable covariance function (left) and a space‐time covariance function with dynamical compact support.
Abstract Full article on Wiley Online Library:   HTML | PDF
Differential network analysis: A statistical perspective
Published Online: Apr 06 2020
DOI: 10.1002/wics.1508
Abstract Full article on Wiley Online Library:   HTML | PDF
Bayesian spatial and spatiotemporal models based on multiscale factorizations
Published Online: Mar 17 2020
DOI: 10.1002/wics.1509
We review the literature on spatial and spatiotemporal models based on multiscale factorizations. These multiscale models decompose spatial and spatiotemporal datasets into many small components, called multiscale coefficients, at multiple levels of resolution. Then analysis proceeds independently for each multiscale coefficient. After that, aggregation equations are used to coherently combine the analyses from the multiple multiscale coefficients to obtain a statistical analysis at the original resolution level. For example, here we present a graphical representation of dynamic multiscale spatiotemporal models. At each time t, t = 1, …, T, the spatiotemporal data ytL are decomposed into a set θte of empirical multiscale coefficients. These empirical multiscale coefficients are assumed to be noisy measurements of latent multiscale coefficients. Finally, the latent multiscale coefficients θt evolve through time according to a dynamic process indexed by a vector of hyperpameters ψ. Computations for these models are scalable, parallelizable, and fast. Therefore, these multiscale models are tremendously useful for the analysis of massive spatial and spatiotemporal datasets.
Abstract Full article on Wiley Online Library:   HTML | PDF

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