Mixing and moments properties of a non-stationary copula-based Markov process

DOI10.1080/03610926.2019.1602653OpenAlexW2964008701WikidataQ128029334 ScholiaQ128029334MaRDI QIDQ5077525

Sabrina Mulinacci, Fabio Gobbi

Publication date: 18 May 2022

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610926.2019.1602653

zbMATH Keywords

copula\(\beta\)-mixingGaussian processMarkov process\(\rho\)-mixing

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Measures of association (correlation, canonical correlation, etc.) (62H20) Statistics (62-XX)

Related Items (1)

A copula-based approximation to Markov chains

Cites Work

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• Estimation of copula-based semiparametric time series models
• Some aspects of modeling dependence in copula-based Markov chains
• Convolution copula econometrics
• A copula-based model of speculative price dynamics in discrete time
• An introduction to copulas.
• Basic properties of strong mixing conditions. A survey and some open questions
• Efficient estimation of copula-based semiparametric Markov models
• Copulas and Markov processes
• Some properties of the bivariate normal distribution considered in the form of a contingency table
• The Structure of Bivariate Distributions
• On measures of dependence
• Some Limit Theorems for Random Functions. I
• On Strong Mixing Conditions for Stationary Gaussian Processes
• Some Limit Theorems for Random Functions. II
• Copulas and Temporal Dependence

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