Weak convergence of marked empirical processes for focused inference on \(\mathrm{AR}(p)\) vs \(\mathrm{AR}(p+1)\) stationary time series
DOI10.1007/s11009-011-9270-7OpenAlexW2168388077MaRDI QIDQ1930624
Alejandra Cabaña, Marco Scavino, Enrique M. Cabaña
Publication date: 11 January 2013
Published in: Methodology and Computing in Applied Probability (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s11009-011-9270-7
Nonparametric hypothesis testing (62G10) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Asymptotic properties of nonparametric inference (62G20) Central limit and other weak theorems (60F05)
Related Items (2)
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Weak convergence of non-stationary multivariate marked processes with applications to martingale testing
- Local power of a Cramér-von Mises type test for parametric autoregressive models of order one
- Goodness-of-fit tests for continuous regression
- On the residuals of autoregressive processes and polynomial regression
- Tests of normality based on transformed empirical processes
- Weighted empirical processes in dynamic nonlinear models.
- On residual empirical processes of stochastic regression models with applications to time series
- Nonparametric model checks for time series
- Weak convergence of the sequential empirical processes of residuals in ARMA models
- Goodness-of-fit tests based on quadratic functionals of transformed empirical processes
- The empirical process of autoregressive residuals
- ASYMPTOTIC DISTRIBUTION-FREE DIAGNOSTIC TESTS FOR HETEROSKEDASTIC TIME SERIES MODELS
- Testing Statistical Hypotheses
- Contributions to Central Limit Theory for Dependent Variables
- Goodness-of-Fit to the Exponential Distribution, Focused on Weibull Alternatives
This page was built for publication: Weak convergence of marked empirical processes for focused inference on \(\mathrm{AR}(p)\) vs \(\mathrm{AR}(p+1)\) stationary time series