Journal article
Modeling spatial correlation that grows on trees, with a stream network application
Spatial statistics, Vol.45, p.100536
10/01/2021
DOI: 10.1016/j.spasta.2021.100536
Abstract
Spatial data on a network, like spatial data on a Euclidean domain, may exhibit nonstationarity. This article develops two classes of nonstationary models for continuously indexed data on directed tree networks, such as stream networks, that are adaptations of models used previously for nonstationary temporal or spatial data on Euclidean domains. These classes, called elastic models and spatially varying moving average models, allow the spatial dependence between observations at sites any fixed distance apart to grow monotonically as one moves either up or down the network. The process variance, or components thereof, may also be allowed to grow monotonically. An example of trout density data from a stream network in Wyoming, USA indicates that the proposed nonstationary models fit those data much better than their existing stationary or quasi-stationary counterparts. (C) 2021 Elsevier B.V. All rights reserved.
Details
- Title: Subtitle
- Modeling spatial correlation that grows on trees, with a stream network application
- Creators
- Ruida Song - University of IowaDale L Zimmerman - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Spatial statistics, Vol.45, p.100536
- Publisher
- Elsevier
- DOI
- 10.1016/j.spasta.2021.100536
- ISSN
- 2211-6753
- eISSN
- 2211-6753
- Number of pages
- 16
- Language
- English
- Date published
- 10/01/2021
- Academic Unit
- Statistics and Actuarial Science; Biostatistics
- Record Identifier
- 9984257732502771
Metrics
9 Record Views