An empirical central limit theorem with applications to copulas under weak dependence
From MaRDI portal
Publication:625311
DOI10.1007/s11203-008-9026-3zbMath1333.62207OpenAlexW2022541646MaRDI QIDQ625311
Gabriel Lang, Jean-David Fermanian, Paul Doukhan
Publication date: 15 February 2011
Published in: Statistical Inference for Stochastic Processes (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s11203-008-9026-3
Density estimation (62G07) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Functional limit theorems; invariance principles (60F17)
Related Items
On the diversity score: a copula approach, A multivariate version of Hoeffding's phi-square, Consistent testing for a constant copula under strong mixing based on the tapered block multiplier technique, Empirical and sequential empirical copula processes under serial dependence, A note on weak convergence of the sequential multivariate empirical process under strong mixing, Time-dependent copulas, Copula-based semiparametric models for multivariate time series, When uniform weak convergence fails: empirical processes for dependence functions and residuals via epi- and hypographs, Nonparametric tests for tail monotonicity, Dependence of Stock Returns in Bull and Bear Markets, Tests of stochastic monotonicity with improved power, Gaussian Limits for a Fork-Join Network with Nonexchangeable Synchronization in Heavy Traffic, Hybrid copula estimators
Cites Work
- Estimation of copula-based semiparametric time series models
- Goodness-of-fit tests for copulas
- An introduction to copulas. Properties and applications
- Mixing: Properties and examples
- Invariance principles for absolutely regular empirical processes
- A new weak dependence condition and applications to moment inequalities
- ARCH-type bilinear models with double long memory.
- Weak convergence of empirical copula processes
- Weak dependence beyond mixing and asymptotics for nonparametric regression
- Rates in the empirical central limit theorem for stationary weakly dependent random fields.
- Stochastic algorithms
- Weak convergence and empirical processes. With applications to statistics
- A triangular central limit theorem under a new weak dependence condition
- A new covariance inequality and applications.
- Functional Estimation of a Density Under a New Weak Dependence Condition
- A LARCH(∞) Vector Valued Process
- Central Limit Theorems for dependent variables. I
- Nonparametric Estimation and Sensitivity Analysis of Expected Shortfall
- Convergence Criteria for Multiparameter Stochastic Processes and Some Applications
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item