Asymptotic distribution with random indices for linear processes
From MaRDI portal
Publication:5864498
DOI10.2298/FIL1912925MzbMath1499.60062OpenAlexW3007596694MaRDI QIDQ5864498
Yu Miao, Qinghui Gao, Shui-Li Zhang
Publication date: 7 June 2022
Published in: Filomat (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.2298/fil1912925m
Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Central limit and other weak theorems (60F05)
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Moderate deviation principle for autoregressive processes
- Central limit theorem for stationary linear processes
- Asymptotic normality of autoregressive processes
- Functional limit theorems for linear processes in the domain of attraction of stable laws
- A moderate deviation principle for \(m\)-dependent random variables with unbounded \(m\)
- Large deviation principles for moving average processes of real stationary sequences
- Asymptotic normality for random sums of linear processes
- Sequential estimation for the autocorrelations of linear processes
- Central limit theorem for linear processes
- Large and moderate deviations for moving average processes
- A central limit theorem with random indices for stationary linear processes
- Large deviations for moving average processes
- A note on the central limit theorems for dependent random variables
- Functional limit theorem for moving average processes generated by dependent random vari\-ables
- Central limit theorems for moving average processes
- Precise asymptotics in the law of the iterated logarithm of moving-average processes
- Moderate deviations of empirical periodogram and non-linear functionals of moving average processes
- Moderate deviation principles for moving average processes of real stationary sequences
- The Discounted Berry-Esséen Analogue for Autoregressive Processes
- The Discounted Large Deviation Principle for Autoregressive Processes
- On mixing sequences of sets
- An extension of central limit theorem for randomly indexed m-dependent random variables
- Moderate deviation principle for the error variance estimator in linear models
- Moderate Deviation Principles for Empirical Covariance in the Neighbourhood of the Unit Root
- A central limit theorem for randomly indexed m-dependent random variables
- On the central limit theorem for the sum of a random number of independent random variables
- On the central limit theorem for the sum of a random number of independent random variables
This page was built for publication: Asymptotic distribution with random indices for linear processes