Results 131 to 140 of about 11,706 (202)

Detecting Critical Change in Dynamics Through Outlier Detection with Time‐Varying Parameters

open access: yesBritish Journal of Mathematical and Statistical Psychology, Volume 79, Issue 2, Page 237-262, May 2026.
Abstract Intensive longitudinal data are often found to be non‐stationary, namely, showing changes in statistical properties, such as means and variance‐covariance structures, over time. One way to accommodate non‐stationarity is to specify key parameters that show over‐time changes as time‐varying parameters (TVPs). However, the nature and dynamics of
Meng Chen   +2 more
wiley   +1 more source

Multipliers on bi-parameter Haar system Hardy spaces. [PDF]

open access: yesMath Ann
Lechner R   +3 more
europepmc   +1 more source

Multiple Changepoint Detection for Non‐Gaussian Time Series

open access: yesJournal of Time Series Analysis, Volume 47, Issue 3, Page 465-484, May 2026.
ABSTRACT This article combines methods from existing techniques to identify multiple changepoints in non‐Gaussian autocorrelated time series. A transformation is used to convert a Gaussian series into a non‐Gaussian series, enabling penalized likelihood methods to handle non‐Gaussian scenarios.
Robert Lund   +3 more
wiley   +1 more source

Pointwise Multipliers on the Lorentz Spaces

open access: yesPointwise Multipliers on the Lorentz Spaces
L^p 〓spaces ...
openaire  

Turbulent Mixing and Dissipation Around Rough Seamounts

open access: yesGeophysical Research Letters, Volume 53, Issue 8, 28 April 2026.
Abstract Seamounts are critical components of the global ocean energy budget, contributing significantly to turbulent dissipation through their interaction with large‐scale flows. However, most previous numerical investigations used smoothed bathymetry that omits small‐scale topographic variability. We use turbulence‐resolving large‐eddy simulations to
Tomas Chor   +2 more
wiley   +1 more source

Explainable AI Insights Into EEG Classification and Its Alignment to Neural Correlates

open access: yesHuman Brain Mapping, Volume 47, Issue 6, April 15, 2026.
We systematically generate, filter, and cluster explanations of deep learning models of EEG data to identify universal patterns of relevance, which we validate via connections to observations from neuroscience. ABSTRACT While deep learning has drastically improved the performance of electroencephalography (EEG) analysis, it remains unclear what these ...
Hendrik Eilts   +5 more
wiley   +1 more source

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