CopFITi is the first marginalization-consistent copula for irregular multivariate time series, using normalizing flows for marginals and a Gaussian mixture copula for dependencies to reach new state-of-the-art joint density modeling.
year = 2006, series =
5 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 5verdicts
UNVERDICTED 5representative citing papers
Introduces multi-time renewal chains via multi-index convolution and power series, with FFT-based computation, asymptotic theorems under proportional growth, and a nonparametric MLE for fixed-horizon censored data.
Proposes a declining CVaR glidepath framework for target-date funds that links portfolio risk constraints to exogenous return targets derived from pension parameters and applies it to the Chilean system.
Framework transforms complex chance-constrained problems into convex SOCPs for individual constraints and uses copulas for joint constraints under moment, support, and data-driven ambiguity sets, demonstrated on beamforming.
Optimal basis risk weighting in expectile-based parametric insurance exists and is unique under boundary conditions in a utility-maximization framework, with a link to separability in location-scale distributions.
citing papers explorer
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Valid and Expressive Copulas for Irregular Multivariate Time Series
CopFITi is the first marginalization-consistent copula for irregular multivariate time series, using normalizing flows for marginals and a Gaussian mixture copula for dependencies to reach new state-of-the-art joint density modeling.
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Discrete time-multidimensional renewal theory and applications
Introduces multi-time renewal chains via multi-index convolution and power series, with FFT-based computation, asymptotic theorems under proportional growth, and a nonparametric MLE for fixed-horizon censored data.
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A Declining CVaR Glidepath Framework for Target-Date Fund Design with an Application to the Chilean Pension System
Proposes a declining CVaR glidepath framework for target-date funds that links portfolio risk constraints to exogenous return targets derived from pension parameters and applies it to the Chilean system.
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Distributionally Robust Complex Chance-Constrained Optimization
Framework transforms complex chance-constrained problems into convex SOCPs for individual constraints and uses copulas for joint constraints under moment, support, and data-driven ambiguity sets, demonstrated on beamforming.
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Optimal basis risk weighting in expectile-based parametric insurance
Optimal basis risk weighting in expectile-based parametric insurance exists and is unique under boundary conditions in a utility-maximization framework, with a link to separability in location-scale distributions.