Empirical‐Process Limit Theory and Filter Approximation Bounds for Score‐Driven Time Series Models
Journal of Time Series Analysis, EarlyView.ABSTRACT This article examines the filtering and approximation‐theoretic properties of score‐driven time series models. Under specific Lipschitz‐type and tail conditions, new results are derived, leading to maximal and deviation inequalities for the filtering approximation error using empirical process theory.
Enzo D'Innocenzo
wiley +1 more source
Generalizing machine learning models from clinical free text
Scientific ReportsTo assess strategies for enhancing the generalizability of healthcare artificial intelligence models, we analyzed the impact of preprocessing approaches applied to medical free text, compared single- versus multiple-institution data models, and evaluated
Balaji Pandian +8 more
doaj +1 more source
A Generic Formula and Some Special Cases for the Kullback-Leibler Divergence between Central Multivariate Cauchy Distributions. [PDF]
Entropy (Basel), 2022 Bouhlel N, Rousseau D.
europepmc +1 more source
Characterizations and Kullback-Leibler Divergence of Gompertz Distributions
CoRR, 2014 In this note, we characterize the Gompertz distribution in terms of extreme value distributions and point out that it implicitly models the interplay of two antagonistic growth processes. In addition, we derive a closed form expressions for the Kullback-Leibler divergence between two Gompertz Distributions. Although the latter is rather easy to obtain,
openaire +2 more sources
Testing Distributional Granger Causality With Entropic Optimal Transport
Journal of Time Series Analysis, EarlyView.ABSTRACT We develop a novel nonparametric test for Granger causality in distribution based on entropic optimal transport. Unlike classical mean‐based approaches, the proposed method directly compares the full conditional distributions of a response variable with and without the history of a candidate predictor.
Tao Wang
wiley +1 more source
A Satellite Incipient Fault Detection Method Based on Decomposed Kullback-Leibler Divergence. [PDF]
Entropy (Basel), 2021 Zhang G, Yang Q, Li G, Leng J, Yan M.
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Is A Little Learning Dangerous?
Noûs, EarlyView.ABSTRACT I argue that a little learning is often dangerous even for ideal reasoners who are operating in extremely simple scenarios and know all the relevant facts about how the evidence is generated. More precisely, I show that, on many plausible ways of assigning value to a credence in a hypothesis H, ideal Bayesians should sometimes expect other ...
Bernhard Salow
wiley +1 more source
Measuring Synchronization between Spikes and Local Field Potential Based on the Kullback-Leibler Divergence. [PDF]
Comput Intell Neurosci, 2021 Yin L, Zhang G, Yin F.
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Topics in Cognitive Science, EarlyView.Abstract Recent studies suggest that learners who are asked to predict the outcome of an event learn more than learners who are asked to evaluate it retrospectively or not at all. One possible explanation for this “prediction boost” is that it helps learners engage metacognitive reasoning skills that may not be spontaneously leveraged, especially for ...
Joseph A. Colantonio +4 more
wiley +1 more source
Fault-Tolerant indoor localization based on speed conscious recurrent neural network using Kullback-Leibler divergence. [PDF]
Peer Peer Netw Appl, 2022 Varma PS, Anand V.
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