Journal article
A BOOTSTRAP VARIANT OF AIC FOR STATE-SPACE MODEL SELECTION
Statistica Sinica, Vol.7(2), pp.473-496
04/01/1997
Abstract
Following in the recent work of Hurvich and Tsai (1989, 1991, 1993) and Hurvich, Shumway, and Tsai (1990), we propose a corrected variant of AIC developed for the purpose of small-sample state-space model selection. Our variant of AIC utilizes bootstrapping in the state-space framework (Stoffer and Wall (1991)) to provide an estimate of the expected Kullback-Leibler discrepancy between the model generating the data and a fitted approximating model. We present simulation results which demonstrate that in small-sample settings, our criterion estimates the expected discrepancy with less bias than traditional AIC and certain other competitors. As a result, our AIC variant serves as an effective tool for selecting a model of appropriate dimension. We present an asymptotic justification for our criterion in the Appendix.
Details
- Title: Subtitle
- A BOOTSTRAP VARIANT OF AIC FOR STATE-SPACE MODEL SELECTION
- Creators
- Joseph E. CavanaughRobert H. Shumway
- Resource Type
- Journal article
- Publication Details
- Statistica Sinica, Vol.7(2), pp.473-496
- ISSN
- 1017-0405
- eISSN
- 1996-8507
- Publisher
- Institute of Statistical Science, Academia Sinica and International Chinese Statistical Association
- Language
- English
- Date published
- 04/01/1997
- Academic Unit
- Statistics and Actuarial Science; Biostatistics; Injury Prevention Research Center
- Record Identifier
- 9984213373902771
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