Dynamic linear model diagnostics

Published

Journal Article

SUMMARY: In time series analysis using dynamic linear models, retrospective analysis involves the calculation of filtered, or smoothed, distributions for state parameters in the past. We develop and illustrate novel results that are useful in retrospective assessment of the influence of individual observations on such distributions. In particular, new and computationally simple filtering equations are derived for past state parameters based on leaving out one observation at a time, providing dynamic model based versions of methods currently used in standard, static regression diagnostics. © 1991 Biometrika Trust.

Full Text

Duke Authors

Cited Authors

  • Harrison, J; West, M

Published Date

  • December 1, 1991

Published In

Volume / Issue

  • 78 / 4

Start / End Page

  • 797 - 808

International Standard Serial Number (ISSN)

  • 0006-3444

Digital Object Identifier (DOI)

  • 10.1093/biomet/78.4.797

Citation Source

  • Scopus