Study of spatial relationships between two sets of variables: a nonparametric approach
From MaRDI portal
Publication:2863044
DOI10.1080/10485252.2013.797091zbMath1416.62545OpenAlexW1965362088MaRDI QIDQ2863044
Emilio Porcu, Ronny Vallejos, Francisco J. Cuevas
Publication date: 21 November 2013
Published in: Journal of Nonparametric Statistics (Search for Journal in Brave)
Full work available at URL: http://hdl.handle.net/10533/133243
Inference from spatial processes (62M30) Nonparametric regression and quantile regression (62G08) Asymptotic properties of nonparametric inference (62G20) Applications of statistics to biology and medical sciences; meta analysis (62P10)
Related Items
Image similarity assessment based on coefficients of spatial association, Unnamed Item, The codispersion map: a graphical tool to visualize the association between two spatial variables, Assessing the significance of the correlation between the components of a bivariate Gaussian random field
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Codispersion coefficients for spatial and temporal series
- Quasi-arithmetic means of covariance functions with potential applications to space-time data
- Constructing and fitting models for cokriging and multivariable spatial prediction
- Kernel density estimation for spatial processes: The \(L_{1}\) theory
- Nonparametric kernel estimation of an isotropic variogram.
- Modified \(F\) tests for assessing the multiple correlation between one spatial process and several others
- Asymptotic normality of the Nadaraya-Watson semivariogram estimators
- Assessing the association between two spatial or temporal sequences
- Assessing the Significance of the Correlation between Two Spatial Processes
- A kernel‐based method for nonparametric estimation of variograms
- A measure of association for spatial variables
- Statistics for Spatial Data
- Matérn Cross-Covariance Functions for Multivariate Random Fields
- Space–Time Covariance Functions
- Highly robust variogram estimation
