Multiresolution models for nonstationary spatial covariance functions

By D. Nychka, Christopher K. Wikle, J. Andrew Royle. 2002

244 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Economics and Econometrics, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/2b5d313e2406103ca4d91975ecc53e097f43e4f3

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Economics and Econometrics

In Environmental Engineering

Citations in the corpus

Some topics in convolution-based spatial modeling (2007, unknown): “Related approaches include Nychka et al. (2002) ’s model for nonstationary spatial data based on linear combinations of wavelet basis functions and Banerjee et al. (2004)’s spatial model for house prices based on normalized distance-weighted sums of stationary processes.”
Local Gaussian Process Approximation for Large Computer Experiments (2013, methodology): “ices and fast sparse linear algebra packages (Kaufman et al., 2012; Sang and Huang, 2012). Our contribution has aspects in common with all of these approaches and several others by association (e.g., Nychka et al., 2002; Quinonero{Candela~ and Rasmussen, 2005; Furrer et al., 2006). It is reminiscent”
Modeling Nonstationary Processes Through Dimension Expansion (2010, methodology): “…of atmospheric science, empirical orthogonal functions have been used to model a nonstationary process as the sum of a stationary isotropic process and a set of basis functions with random coeﬃcients representing departures from nonstationarity (Nychka and Saltzman (1998), Nychka et al. (2002)).”
A kernel-based spectral model for non-Gaussian spatio-temporal processes (2002, result): “This is consistent with other recent work demonstrating the flexibility of spectral approaches in nonstationary spatial modeling (e.g., Nychka et al. 1999; Nychka et al. 2002; Fuentes 2002).”
Second-order non-stationary modeling approaches for univariate geostatistical data (2017, background): “Nychka et al. (2002) propose a modeling approach where orthogonal basis functions are replaced by wavelets.”
Bayesian Inference for the Spatial Random Effects Model (2011, background): “Then in (6), where the spatial basis functions are made up of r = 30 W-wavelets (e.g., Kwong and Tang 1994; Nychka, Wikle, and Royle 2002).”
Parameter space exploration with Gaussian process trees (2004, background): “Also in the recent literature (Johns et al., 2003; Nychka et al., 2002; Schmidt & O’Hagan, 2003) there has been success in designing non-stationarity models by parameterizing the covariance (or correlation) matrix hierarchically.”
Basis-Function Models in Spatial Statistics (2021, background): “One popular approach that does not require its knowledge is MCMC; see, for example, Nychka et al. (2002), Paciorek (2007), Wikle (2010), Kang & Cressie (2011), Eidsvik et al. (2012), and Zammit-Mangion & Rougier (2020) for the use of MCMC in a spatial-basis-function-model setting.”
Non-Stationary Dependence Structures for Spatial Extremes (2014, methodology): “Nychka et al. (2002) built flexible non-stationary covariance functions using multi-resolution wavelets.”
Spatial–temporal mesoscale modeling of rainfall intensity using gage and radar data (2008, background): “don et al., 1999, Nychka et al., 2002, and Fuentes 2002, among others.”
A Spatio-Temporal Model for Mean, Anomaly, and Trend Fields of North Atlantic Sea Surface Temperature (2009, background): “Recent examples are Nychka, Wikle, and Royle (2002), Tzeng, Huang, and Cressie (2005), Fuentes (2007), Johannesson, Cressie, and Huang (2007), and Paciorek (2007).”