Twenty-one ML estimators for model selection
1995 (English)In: Automatica, ISSN 00051098 (ISSN), Vol. 31, no 10, 1377-1392 p.Article in journal (Refereed) Published
Classical approaches to determine a suitable model structure from observed input-output data are based on hypothesis tests and information-based criteria. Recently, the model structure has been considered as a stochastic variable, and standard estimation techniques have been proposed. The resulting estimators are closely related to the aforementioned methods. However, it turns out that there are a number of prior choices in the problem formulation, which are crucial for the estimators' behavior. The contribution of this paper is to clarify the role of the prior choices, to examine a number of possibilities and to show which estimators are consistent. This is done in a linear regression framework. For autoregressive models, we also investigate a novel prior assumption on stability, and give the estimator for the model order and the parameters themselves. Copyright Â© 1995 Elsevier Science Ltd All rights reserved.
Place, publisher, year, edition, pages
1995. Vol. 31, no 10, 1377-1392 p.
Estimation, Maximum likelihood, Model structure selection, Modeling, Mathematical models, Random processes, Regression analysis, Spurious signal noise, System stability, Linear regression, Parameter estimation
Research subject SRA - ICT
IdentifiersURN: urn:nbn:se:kth:diva-60587DOI: 10.1016/0005-1098(95)00058-5OAI: oai:DiVA.org:kth-60587DiVA: diva2:477549
NR 201408052012-01-132012-01-132012-01-13Bibliographically approved