The Inverse Problem: Autoregressive Estimators

Leonid I. Piterbarg and Alexander G. Ostrovskii
Additional contact information
Leonid I. Piterbarg: University of Southern California, Center for Applied Mathematical Sciences
Alexander G. Ostrovskii: Kyushu University, Research Institute for Applied Mechanics

Chapter Chapter 8 in Advection and Diffusion in Random Media, 1997, pp 179-190 from Springer

Abstract: Abstract Roughly speaking, all numerical solutions of linear partial differential equations can be broken down into two groups: (1) the expansion of the solution in terms of some basis (Galerkin method), and (2) the approximation of derivatives by finite differences. The same is relevant to the inverse problem.

Keywords: Gaussian Kernel; Autoregressive Model; Stochastic Partial Differential Equation; Linear Partial Differential Equation; Stochastic Partial Differential Equa (search for similar items in EconPapers)
Date: 1997
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-1-4757-4458-3_8

Ordering information: This item can be ordered from
http://www.springer.com/9781475744583

DOI: 10.1007/978-1-4757-4458-3_8

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().