Spatio-Temporal Covariance Functions Generated by Mixtures

By Chunsheng Ma. 2002

112 citations recorded by the source corpus.

Subfields: Building and Construction, Economics and Econometrics, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/a6cb165dd19e8416d312af78b040cb54dd71b268

How researchers see this paper

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

In Building and Construction

1. Tilmann Gneiting
2. Marc G. Genton
3. Peter Guttorp
4. Simon Michael Papalexiou
5. F. Serinaldi
6. Jorge Mateu

In Economics and Econometrics

1. Marc G. Genton
2. Emilio Porcu
3. F. Serinaldi
4. Simon Michael Papalexiou
5. Claudia Klüppelberg
6. C. Steinkohl
7. Peter Guttorp
8. Richard A. Davis

In Environmental Engineering

1. Marc G. Genton
2. Emilio Porcu
3. Martin Schlather
4. Chunsheng Ma
5. Tilmann Gneiting
6. Peter Guttorp
7. Jorge Mateu
8. Richard A. Davis

Citations in the corpus

• On potentially negative space time covariances obtained as sum of products of marginal ones (2008, background): “Among other authors, it is worth citing Ma (2002) with mixtures of stationary covariance functions and Porcu, Saura and Mateu (2005) with the so called Mixed Forms.”
• Geostatistical Space-Time Models, Stationarity, Separability, and Full Symmetry (2007, methodology): “Ma (2003a) reported various interesting examples of parametric spacetime covariance functions that are also based on the mixture representation (13).”
• Random Fields Simplified: Preserving Marginal Distributions, Correlations, and Intermittency, With Applications From Rainfall to Humidity (2020, background): “This topic is still an active area of research in geostatistics with many interesting recent contributions (e.g., Alegría & Porcu, 2017; Bogaert & Christakos, 1997; Gneiting et al., 2006; Ma, 2002; Porcu et al., 2016; Schlather, 2012).”
• Max-stable processes for modelling extremes observed in space and time (2011, background): “Examples can be found in Cressie and Huang [10], Gneiting [24], Ma [32, 30], and Schlather [39].”
• Spatio-temporal stationary covariance models (2003, methodology): “One answer to this question is provided by the mixture method in this paper and Ma [11].”
• Methods for generating non-separable spatiotemporal covariance models with potential environmental applications (2004, background): “Several theoretical models of non-separable covariances can be found: (a) in the spatiotemporal stochastics literature [2,3,7–15,19,37]; as well as (b) in the statistics literature [16,18,20,27–30,33,36].”