Nonstationary, nonparametric, nonseparable bayesian spatio-temporal modeling using kernel convolution of order based dependent dirichlet process

• Moumita Das ^[1] ; Sourabh Bhattacharya
  1. [1] Basque Center for Applied Mathematics

     Basque Center for Applied Mathematics

     Bilbao, España

     
• Localización: Proceedings of the 35th International Workshop on Statistical Modelling : July 20-24, 2020 Bilbao, Basque Country, Spain / Itziar Irigoien Garbizu (ed. lit.), Dae-Jin Lee (ed. lit.), Joaquín Martínez Minaya (ed. lit.), María Xosé Rodríguez Álvarez (ed. lit.), 2020, ISBN 978-84-1319-267-3, págs. 318-321
• Idioma: inglés
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• Resumen

  • In this work, using kernel convolution of order based dependent Dirichlet process (Grin and Steel (2006)) we construct a nonstationary, nonseparable, nonparametric space-time process, which, as we show, satis es desirable properties, and includes the stationary, separable, parametric processes as special cases.