Precise Asymptotics in Complete Moment Convergence of Parameter Estimator in the Gaussian Autoregressive Process
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Publication:5265844
DOI10.1080/03610926.2012.763098zbMath1320.60084OpenAlexW2001052890MaRDI QIDQ5265844
Publication date: 29 July 2015
Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/03610926.2012.763098
convergence ratelaw of iterated logarithmcomplete moment convergenceparameter estimatorGaussian autoregressive process
Gaussian processes (60G15) Linear regression; mixed models (62J05) Martingales with discrete parameter (60G42) Strong limit theorems (60F15) Large deviations (60F10)
Cites Work
- Unnamed Item
- Moderate deviation principle for autoregressive processes
- A nonuniform bound on the rate of convergence in the martingale central limit theorem
- Moderate deviations for stable Markov chains and regression models
- Deviation inequalities and moderate deviations for estimators of parameters in TAR models
- Large deviations for quadratic forms of stationary Gaussian processes
- Precise asymptotics for a new kind of complete moment convergence
- Precise asymptotics in the self-normalized law of the iterated logarithm
- Precise rates in the law of iterated logarithm for the moment of i.i.d. random variables
- Precise asymptotics in complete moment convergence for self-normalized sums
- Exact Moment Convergence Rates ofU-Statistics
- Asymptotic Properties of theR/SStatistics for Linear Processes
- On Asymptotic Distributions of Estimates of Parameters of Stochastic Difference Equations
- The Limiting Distribution of the Serial Correlation Coefficient in the Explosive Case
- EDGEWORTH APPROXIMATION IN THE AR(1) PROCESS WITH SOME POSSIBLY NONZERO INITIAL VALUE
- Convergence Rates for Probabilities of Moderate Deviations
- On the Statistical Treatment of Linear Stochastic Difference Equations
- On large deviations in the Gaussian autoregressive process: Stable, unstable and explosive cases