Results 81 to 90 of about 24,138 (305)
Data on copula modeling of mixed discrete and continuous neural time series
Data in Brief, 2016 Copula is an important tool for modeling neural dependence. Recent work on copula has been expanded to jointly model mixed time series in neuroscience (“Hu et al., 2016, Joint Analysis of Spikes and Local Field Potentials using Copula” [1]).
Meng Hu, Mingyao Li, Wu Li, Hualou Liang
doaj +1 more source
Copula-based testing for dependence structures.. [PDF]
This thesis describes tests for specific dependence structures between two random variables, in particular: quadrant dependence, tail monotonicity and stochastic monotonicity.
Sznajder, Dominik
core
Copula-based Kernel Dependency Measures
, 2018 The paper presents a new copula based method for measuring dependence between random variables. Our approach extends the Maximum Mean Discrepancy to the copula of the joint distribution.
Zoubin Ghahramani (5363936) +2 more
core +1 more source
International Journal of Eating Disorders, EarlyView.ABSTRACT Objective Self‐reported frequency measures of social media use (e.g., “How often do you use social media?”) are convenient, yet their criterion validity against objective behavioral data remains largely untested in eating disorder research. We compared self‐reports of TikTok use with objective data extracted from TikTok datafiles.
Scott Griffiths +7 more
wiley +1 more source
Estimation of Copula-Based Semiparametric Time Series Models [PDF]
This paper studies the estimation of a class of copula-based semiparametric stationary Markov models. These models are characterized by nonparametric invariant (or marginal) distributions and parametric copula functions that capture the temporal ...
Yanqin Fan, Xiaohong Chen
core
Point and Risk estImation Using an enSemble of Models for Nowcasting: PRISM‐Now
Journal of Forecasting, EarlyView.ABSTRACT We propose PRISM‐Now, a novel ensemble forecasting system for near‐term GDP projection. Recognizing that relevant economic information evolves over time, we treat forecasts from multiple base models as draws from a mixture distribution of “good” and “bad” estimates, whose composition changes continuously and cannot be identified ex ante.
Beomseok Seo, Hyungbae Cho, Dongjae Lee
wiley +1 more source
Synchronization frequency analysis and stochastic simulation of multi-site flood flows based on the complicated vine copula structure [PDF]
Hydrology and Earth System SciencesAccurately modeling and predicting flood flows across multiple sites within a watershed presents significant challenges due to potential issues of insufficient accuracy and excessive computational demands in existing methodologies.
X. Yu, Y.-P. Xu, Y. Guo, S. Chen, H. Gu
doaj +1 more source
Asymptotic properties of the Bernstein density copula for dependent data [PDF]
Copulas are extensively used for dependence modeling. In many cases the data does not reveal how the dependence can be modeled using a particular parametric copula. Nonparametric copulas do not share this problem since they are entirely data based.
ROMBOUTS, Jeroen V.K. +2 more
core
, 2018 <p>Bayesian copula selection algorithms.</p> <p>Library of MatLab functions to estimate the probability of different copula models given a joint distribution of fractiles.</p ...
David Huard, Évin, Guillaume
core +1 more source
Enjoy the Joy of Copulas: With a Packagecopula [PDF]
Journal of Statistical Software, 2007 Copulas have become a popular tool in multivariate modeling successfully applied in many fields. A good open-source implementation of copulas is much needed for more practitioners to enjoy the joy of copulas. This article presents the design, features, and some implementation details of the R package copula.
openaire +3 more sources