On Bahadur asymptotic efficiency of the maximum likelihood and quasi-maximum likelihood estimators in Gaussian stationary processes
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Publication:1613579
DOI10.1016/S0304-4149(99)00063-0zbMath0991.62061MaRDI QIDQ1613579
Publication date: 29 August 2002
Published in: Stochastic Processes and their Applications (Search for Journal in Brave)
Toeplitz matrixspectral densityBahadur efficiencylarge deviation probabilitiesGaussian stationary processes
Asymptotic properties of parametric estimators (62F12) Non-Markovian processes: estimation (62M09) Inference from stochastic processes and spectral analysis (62M15) Large deviations (60F10)
Related Items (2)
Moderate deviation principle for maximum-likelihood estimator ⋮ On exponential rates of estimators of the parameter in the first-order autoregressive process
Cites Work
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- Efficient parameter estimation for self-similar processes
- Central limit theorems for quadratic forms in random variables having long-range dependence
- Large-sample properties of parameter estimates for strongly dependent stationary Gaussian time series
- Large sample point estimation: A large deviation theory approach
- Time series: theory and methods.
- Higher order asymptotic theory for time series analysis
- Classical asymptotic properties of a certain estimator related to the maximum likelihood estimator
- High-order efficiency in the estimation of linear processes
- On exponential rates of estimators of the parameter in the first-order autoregressive process
- Large deviations for quadratic forms of stationary Gaussian processes
- The second-order Fisher information
- Large Deviation Results for Statistics of Short‐ and Long‐memory Gaussian Processes
- The asymptotic theory of linear time-series models
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