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WIREs Comp Stat
Wiley Interdisciplinary Reviews:
WIREs Computational Statistics
Volume 13 Issue 1 (January 2021)
Page 0 - 0


Stationary count time series models
Published Online: Feb 13 2020
DOI: 10.1002/wics.1502
We review and compare popular models for stationary count time series, covering univariate and multivariate, as well as (un)bounded count data.
Abstract Full article on Wiley Online Library:   HTML | PDF
Model exploration using conditional visualization
Published Online: Feb 07 2020
DOI: 10.1002/wics.1503
Screenshot of condvis shiny app for interactive conditional model exploration.
Abstract Full article on Wiley Online Library:   HTML | PDF

Advanced Reviews

A review of flow field forecasting: A high‐dimensional forecasting procedure
Published Online: Feb 21 2020
DOI: 10.1002/wics.1505
Forecasting, especially high‐dimensional forecasting, is becoming more and more sought after, particularly as computing resources increase in both size and speed. Flow field forecasting is a general purpose regression‐based forecasting method that has recently been expanded to high‐dimensional settings. In this article, we provide an overview of the flow field forecasting methodology, with a particular emphasis on environments where the number of candidate predictor variables is large, potentially larger than the number of observations.
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Random projections: Data perturbation for classification problems
Published Online: Feb 05 2020
DOI: 10.1002/wics.1499
Projections determine distributions! Left: bivariate‐dimensional distributions, one uniform on the unit circle (black), the other uniform on the unit disk (blue). Right: the corresponding densities after the projecting into a one‐dimensional (1D) space. In fact, any p‐dimensional distribution is determined by its 1D projections (cf. Theorem 1).
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Tweedie regression models and its geometric sums for (semi‐)continuous data
Published Online: Jan 02 2020
DOI: 10.1002/wics.1496
Summary of Tweedie (Tw) and extended geometric Tweedie (eGTw) models including their power p, support Sp of distributions, mean domain M p, dispersion values ϕ of eGTw (ϕ > 0 for all Tw), and features: over‐(O), equi‐(E), under‐(U)varied, and zero‐mass (ZM).
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