Correlation theory of almost periodically correlated processes
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Publication:2640994
DOI10.1016/0047-259X(91)90109-FzbMath0721.60045OpenAlexW1985086994MaRDI QIDQ2640994
Publication date: 1991
Published in: Journal of Multivariate Analysis (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/0047-259x(91)90109-f
spectral measurestrongly harmonizablealmost periodically correlatedFourier series and transformsrandom time shiftsuniformly almost periodic processes
Related Items (33)
ASYMPTOTIC ANALYSIS ABOUT THE PERIODOGRAM OF A GENERAL CLASS OF TIME SERIES MODELS WITH SPECTRAL SUPPORTSON LINES NOT PARALLEL TO THE MAIN DIAGONAL ⋮ Spectral analysis of the covariance of the almost periodically correlated processes ⋮ Prediction for the processes with almost cyclostationary structure ⋮ Asymptotically Stationary Processes on Amenable Groups ⋮ Generalized seasonal tapered block bootstrap ⋮ Asymptotic normality of the spectral density estimators for almost periodically correlated stochastic processes ⋮ Laws of large numbers for periodically and almost periodically correlated processes ⋮ First and second order analysis for periodic random arrays using block bootstrap methods ⋮ ASYMPTOTIC PROPERTY OF SPECTRAL DENSITY ESTIMATORS OF A CONTINUOUS TIME PROCESS ALMOST PERIODICALLY CORRELATED LOW DEPENDENT BY POISSON ⋮ Spectral Density Estimation for Nonstationary Data With Nonzero Mean Function ⋮ On the support of the spectral measure of a harmonizable sequence ⋮ WAVELET ESTIMATION OF THE COVARIANCE OF ALMOST PERIODICALLY CORRELATED PROCESSES AND STUDY OF ASYMPTOTIC PROPERTIES IN A CONTEXT OF WEAK DEPENDENCE ⋮ Testing for seasonal means in time series data ⋮ Asymptotic distributions and subsampling in spectral analysis for almost periodically correlated time series ⋮ Generalized subsampling procedure for non-stationary time series ⋮ Conditions for the completeness of the spectral domain of a harmonizable process ⋮ Least squares method for statistical analysis of polyrhythmics ⋮ Weak law of large numbers for almost periodically correlated processes ⋮ Subsampling for continuous-time almost periodically correlated processes ⋮ Convolved subsampling estimation with applications to block bootstrap ⋮ Subsampling in testing autocovariance for periodically correlated time series ⋮ Trace measures of a positive definite bimeasure ⋮ Consistency and application of moving block bootstrap for non-stationary time series with periodic and almost periodic structure ⋮ Estimation of the Fourier coefficient functions and their spectral densities for \(\phi\)-mixing almost periodically correlated processes ⋮ Discrete periodic sampling with jitter and almost periodically correlated processes ⋮ Almost periodically unitary stochastic processes ⋮ Block Bootstrap for the Autocovariance Coefficients of Periodically Correlated Time Series ⋮ Generalized Resampling Scheme With Application to Spectral Density Matrix in Almost Periodically Correlated Class of Time Series ⋮ RANDOM SAMPLING ESTIMATION FOR ALMOST PERIODICALLY CORRELATED PROCESSES ⋮ On the spectrum of correlation autoregressive sequences ⋮ Unnamed Item ⋮ Block Bootstrap for Poisson‐Sampled Almost Periodic Processes ⋮ Circular block bootstrap for coefficients of autocovariance function of almost periodically correlated time series
Cites Work
- Representation of strongly harmonizable periodically correlated processes and their covariances
- Correlation theory of stationary and related random functions. Volume II: Supplementary notes and references
- On stationarity conditions for a certain periodic random process
- An identification problem in almost and asymptotically almost periodically correlated processes
- Characterization of cyclostationary random signal processes
- Periodically Correlated Processes with Discontinuous Correlation Functions
- Stationarizable random processes
- Stationarizing Properties of Random Shifts
- RANDOM SAMPLING ESTIMATION FOR ALMOST PERIODICALLY CORRELATED PROCESSES
- Periodically and Almost-Periodically Correlated Random Processes with a Continuous Time Parameter
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