Twenty-one ML estimators for model selection

• 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 © 1995 Elsevier Science Ltd All rights reserved.

Ämnesord

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Reglerteknik (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Control Engineering (hsv//eng)

Nyckelord

Estimation

Maximum likelihood

Model structure selection

Modeling

Mathematical models

Random processes

Regression analysis

Spurious signal noise

System stability

Linear regression

Parameter estimation

SRA - ICT

SRA - Informations- och kommunikationsteknik

Automatic control

Publikations- och innehållstyp

ref (ämneskategori)

art (ämneskategori)