Journal article
Reml and best linear unbiased prediction in state space models
Communications in statistics. Theory and methods, Vol.23(8), pp.2253-2268
01/01/1994
DOI: 10.1080/03610929408831384
Abstract
This article takes a hierarchical model approach to the estimation of state space models with diffuse initial conditions. An initial state is said to be diffuse when it cannot be assigned a proper prior distribution. In state space models this occurs either when fixed effects are present or when modelling nonstationarity in the state transition equation. Whereas much of the literature views diffuse states as an initialization problem, we follow the approach of Sallas and Harville (1981,1988) and incorporate diffuse initial conditions via noninformative prior distributions into hierarchical linear models. We apply existing results to derive the restricted loglike-lihood and appropriate modifications to the standard Kalman filter and smoother. Our approach results in a better understanding of De Jong's (1991) contributions. This article also shows how to adjust the standard Kalman filter, the fixed inter- val smoother and the state space model forecasting recursions, together with their mean square errors, for he presence of diffuse components. Using a hierarchical model approach it is shown that the estimates obtained are Best Linear Unbiased Predictors (BLUP).
Details
- Title: Subtitle
- Reml and best linear unbiased prediction in state space models
- Creators
- John Tsimikas - Harvard Medical SchoolJohnnes Ledolter - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Communications in statistics. Theory and methods, Vol.23(8), pp.2253-2268
- Publisher
- Marcel Dekker, Inc
- DOI
- 10.1080/03610929408831384
- ISSN
- 0361-0926
- eISSN
- 1532-415X
- Language
- English
- Date published
- 01/01/1994
- Academic Unit
- Statistics and Actuarial Science; Business Analytics
- Record Identifier
- 9984380478002771
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