Penalized Whittle likelihood for spatial data
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
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Article number | 105156 |
Journal / Publication | Journal of Multivariate Analysis |
Volume | 195 |
Online published | 14 Jan 2023 |
Publication status | Published - May 2023 |
Link(s)
Abstract
Inference for spatial data is challenging because fitting an appropriate parametric model is often difficult. The penalized likelihood-type approach has been successfully developed for various nonparametric function estimation problems in time series analysis. However, it has not been well developed in spatial analysis. In this paper, a penalized Whittle likelihood approach is developed for nonparametric estimation of spectral density functions for regularly spaced spatial data. In particular, the estimated spectral density is the minimizer of a criterion which is developed based on the Whittle likelihood and a penalty for roughness. This approach aggregates several popular nonparametric density estimation methods into a coherent framework. Asymptotic properties of the proposed estimator are derived under mild assumptions without assuming Gaussianity. In addition, a computationally efficient method is developed to optimize the penalized likelihood function. Simulation results and real data examples are also provided to illustrate the finite sample performances of the methodology.
Research Area(s)
- Adaptive smoothing, Frequency domain, Regularization, Spatial lattice data, Spatial periodogram
Bibliographic Note
Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).
Citation Format(s)
Penalized Whittle likelihood for spatial data. / Chen, Kun; Chan, Ngai Hang; Yau, Chun Yip et al.
In: Journal of Multivariate Analysis, Vol. 195, 105156, 05.2023.
In: Journal of Multivariate Analysis, Vol. 195, 105156, 05.2023.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review