UNSPECIFIED. (1991) DYNAMIC LINEAR-MODEL DIAGNOSTICS. BIOMETRIKA, 78 (4). pp. 797-808. ISSN 0006-3444

Abstract

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.

Data sourced from Thomson Reuters' Web of Knowledge

Request changes to a record

Actions (login required)

View Item