Statistical analysis of irregularly spaced spatial data in frequency domain
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Publication:6604025
DOI10.1111/jtsa.12735MaRDI QIDQ6604025
Publication date: 12 September 2024
Published in: Journal of Time Series Analysis (Search for Journal in Brave)
discrete Fourier transformcentral limit theoremperiodogramspectral densityWhittle likelihoodgeneralized spectral mean
Inference from spatial processes (62M30) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Inference from stochastic processes and spectral analysis (62M15)
Cites Work
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- Quantile spectral processes: asymptotic analysis and inference
- Fourier analysis of stationary time series in function space
- Asymptotic properties of discrete Fourier transforms for spatial data
- Autoregressive-aided periodogram bootstrap for time series
- On asymptotic distribution and asymptotic efficiency of least squares estimators of spatial variogram parameters
- Statistical inference for spatial statistics defined in the Fourier domain
- On the nonparametric estimation of covariance functions
- Reconciling the Gaussian and Whittle likelihood with an application to estimation in the frequency domain
- Extending the validity of frequency domain bootstrap methods to general stationary processes
- Testing for stationarity of functional time series in the frequency domain
- Spectral analysis of high-dimensional time series
- A frequency domain empirical likelihood method for irregularly spaced spatial data
- Fourier Analysis of Irregularly Spaced Data onRd
- Inference for stationary random fields given Poisson samples
- Nonseparable, Stationary Covariance Functions for Space–Time Data
- Classes of Nonseparable, Spatio-Temporal Stationary Covariance Functions
- The Hybrid Wild Bootstrap for Time Series
- Estimating Space and Space-Time Covariance Functions for Large Data Sets: A Weighted Composite Likelihood Approach
- Approximate Likelihood for Large Irregularly Spaced Spatial Data
- A Test for Stationarity for Irregularly Spaced Spatial Data
- Statistical Methods for Regular Monitoring Data
- Continuous Auto-Regressive Moving Average Random Fields on Rn
- Time Series
- Linear-Cost Covariance Functions for Gaussian Random Fields
- A copula spectral test for pairwise time reversibility
- A subsampling perspective for extending the validity of state-of-the-art bootstraps in the frequency domain
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