Random central limit theorems for linear processes with weakly dependent innovations
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Publication:457302
DOI10.1016/J.JKSS.2011.10.004zbMath1296.60057OpenAlexW2018547585MaRDI QIDQ457302
Publication date: 26 September 2014
Published in: Journal of the Korean Statistical Society (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.jkss.2011.10.004
Related Items (3)
The stationary bootstrap for the joint distribution of sum and maximum of stationary sequences ⋮ Asymptotic behavior of LSE estimator of an AR(1) coefficient with associated innovations ⋮ Weak convergence for stationary bootstrap empirical processes of associated sequences
Cites Work
- An exponential inequality under weak dependence
- Central limit theorem for stationary linear processes
- Probability and moment inequalities for sums of weakly dependent random variables, with applications
- The notion of \(\psi \)-weak dependence and its applications to bootstrapping time series
- Sample autocovariances of long-memory time series
- A random CLT for dependent random variables
- Asymptotics for linear processes
- A new mixing notion and functional central limit theorems for a sieve bootstrap in time series
- Parameter estimation for infinite variance fractional ARIMA
- Central limit theorem for linear processes
- Random central limit theorem for the linear process generated by a strong mixing process
- A new weak dependence condition and applications to moment inequalities
- Coupling for \(\tau\)-dependent sequences and applications
- A central limit theorem with random indices for stationary linear processes
- A note on the asymptotic normality of sample autocorrelations for a linear stationary sequence
- Weak dependence beyond mixing and asymptotics for nonparametric regression
- Martingale approximations for sums of stationary processes.
- On the central limit theorem and law of the iterated logarithm for stationary processes with applications to linear processes
- Asymptotic distributions of the sample mean, autocovariances, and autocorrelations of long-memory time series
- A triangular central limit theorem under a new weak dependence condition
- A central limit theorem for self-normalized sums of a linear process
- On linear processes with dependent innovations
- A study on moment inequalities under a weak dependence
- Weak dependence. With examples and applications.
- Maximal moment inequality for partial sums of strong mixing sequences and application
- Functional Estimation of a Density Under a New Weak Dependence Condition
- Inference For Autocorrelations Under Weak Assumptions
- CENTRAL LIMIT THEOREMS FOR WEIGHTED SUMS OF LINEAR PROCESSES: LP -APPROXIMABILITY VERSUS BROWNIAN MOTION
- Moment inequalities for mixing sequences of random variables
- sequential estimation of the mean of a linear process
- SAMPLE MEANS, SAMPLE AUTOCOVARIANCES, AND LINEAR REGRESSION OF STATIONARY MULTIVARIATE LONG MEMORY PROCESSES
- Random central limit theorems for martingales
- 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
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