Results 121 to 130 of about 4,959 (254)

A partial envelope approach for modelling multivariate spatial‐temporal data

open access: yesCanadian Journal of Statistics, EarlyView.
Abstract In the new era of big data, modelling multivariate spatial‐temporal data is a challenging task due to both the high dimensionality of the features and complex associations among the responses across different locations and time points.
Reisa Widjaja   +3 more
wiley   +1 more source

Nonlinear permuted Granger causality

open access: yesCanadian Journal of Statistics, EarlyView.
Abstract Granger causality is an established, contentious method that seeks causal temporal connections via association and precedence. While not true causal inference, it assists in mapping networks of information flow that may warrant further study.
Noah D. Gade, Jordan Rodu
wiley   +1 more source

Copula‐based joint modelling of emergency department visits with time‐varying dependence

open access: yesCanadian Journal of Statistics, EarlyView.
Abstract Jointly modelling multiple correlated count time series is essential in health services research, where outcomes like emergency visits for mental health and substance use often evolve together. Ignoring these dependencies can obscure meaningful trends and limit the effectiveness of policy evaluation.
Guanjie Lyu, Cindy Feng, Lihui Liu
wiley   +1 more source

Power spectral density and the brain

open access: yesCanadian Journal of Statistics, EarlyView.
Abstract Time series from M/EEG (magneto/electroencephalography) and ECoG (electrocorticography) recordings are common sources of information about brain function. The power spectral density (PSD) preserves much of this information, up to second order. In the current decade, a burst of brain diagnostics using the slope of log(PSD) has appeared.
Priscilla E. Greenwood   +2 more
wiley   +1 more source

Fully Characterizing Lossy Catalytic Computation. [PDF]

open access: yesAlgorithmica
Folkertsma M   +3 more
europepmc   +1 more source

Optimal subsampling for regression with mixed‐type predictors

open access: yesCanadian Journal of Statistics, EarlyView.
Abstract Subsampling has emerged as an appealing strategy to mitigate the computational and storage challenges imposed by large datasets. Recent subsampling techniques have shown notable computational gains for data dominated by numerical predictors. However, real‐world datasets frequently contain both numerical and categorical predictors.
Jiaqing Zhu, Lin Wang, Fasheng Sun
wiley   +1 more source

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