Some covariance models based on normal scale mixtures

By Martin Schlather. 2011

74 citations recorded by the source corpus.

Modelling spatio-temporal processes has become an important issue in current research. Since Gaussian processes are essentially determined by their second order structure, broad classes of covariance functions are of interest. Here, a new class is described that merges and generalizes various models presented in the literature, in particular models in Gneiting (J. Amer. Statist. Assoc. 97 (2002) 590--600) and Stein (Nonstationary spatial covariance functions (2005) Univ. Chicago). Furthermore, new models and a multivariate extension are introduced.

Subfields: Artificial Intelligence, Economics and Econometrics, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/e11261027b80ac935228979e7cc21e2ab8c4a81a

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In Artificial Intelligence

1. Claudia Klüppelberg
2. Richard A. Davis
3. C. Steinkohl
4. Dimitris Nicolas Politis
5. Konstantinos Fokianos
6. Dominik Janzing
7. Efstathios Paparoditis
8. Jean-Michel Zakoian

In Economics and Econometrics

1. Claudia Klüppelberg
2. C. Steinkohl
3. Richard A. Davis
4. A. C. Davison
5. Peter J. Brockwell
6. Raphaël Huser
7. Qihe Tang
8. Thomas Mikosch

In Environmental Engineering

1. Richard A. Davis
2. Claudia Klüppelberg
3. C. Steinkohl
4. Raphaël Huser

Citations in the corpus

• Max-stable processes for modelling extremes observed in space and time (2011, background): “Before presenting the construction of a max-stable space-time process, we begin with the definition of the Brown-Resnick space-time process with Fréchet marginals (see Brown and Resnick [5] or Schlather [38]).”