NDLI: The Kriged Kalman filter

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Test : Volume 26

Test : Volume 25

Test : Volume 7, Issue 2, December 1998

The Kriged Kalman filter

Discussion ( Test , Volume 7 , Issue 2 )

A note on the robust interpretation of regression coefficients

On the subsample bootstrap variance estimation

Characterizations of multivariate spherical distributions inl $_{∞}$-norm

A simulation-intensive approach for checking hierarchical models

Optimal designs with respect to Elfving's partial minimax criterion for heteroscedastic polynomial regression

An MSE comparison of the restricted Stein-rule and minimum mean squared error estimators in regression

Distributions most nearly compatible with given families of conditional distributions

Natural exponential families associated to Pick functions

Choosing the smoothing parameter for unordered multinomial data

A note on an assertion by E. Gutiérrez-Peña and A.F.M. Smith (Test, 1997, p.87)

Test : Volume 7, Issue 1, June 1998

Test : Volume 6

The Kriged Kalman filter

Content Provider	Springer Nature Link
Author	Mardia, Kanti V. Goodall, Colin Redfern, Edwin J. Alonso, Francisco J.
Copyright Year	1998
Abstract	In recent years there has been growing interest in spatial-temporal modelling, partly due to the potential of large scale data in pollution and global climate monitoring to answer important environmental questions. We consider the Kriged Kalman filter (KKF), a powerful modelling strategy which combines the two wellestablished approaches of (a) Kriging, in the field of spatial statistics, and (b) the Kalman filter, in general state space formulations of multivariate time series analysis. We give a brief introduction to the model and describe its various properties, and highlight that the model allows prediction in time as well as in space, simultaneously. Some special cases of the time series model are considered. We give some procedures to implement the model, also illustrated through a practical example. The paper concludes with a discussion.
Starting Page	217
Ending Page	282
Page Count	66
File Format	PDF
ISSN	11330686
Journal	Test
Volume Number	7
Issue Number	2
e-ISSN	18638260
Language	English
Publisher	Springer-Verlag
Publisher Date	1998-01-01
Publisher Institution	Spanish Society of Statistics and Operations Research
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Bending energy EM algorithm Kalman filter Karahunen-Loeve expansions Kriging pollution spatial temporal modelling state-space model Time series, auto-correlation, regression, etc. Spatial processes Statistics Statistical Theory and Methods Statistics for Business/Economics/Mathematical Finance/Insurance
Content Type	Text
Resource Type	Article
Subject	Statistics and Probability Statistics, Probability and Uncertainty

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