Parameter estimation and model order selection for linear regression models are two classical problems. In this article we derive the minimum mean-square error (MMSE) parameter estimate for a linear regression model with unknown order. We call the so-obtained estimator the Bayesian Parameter estimation Method (BPM). We also derive the model order selection rule which maximizes the probability of selecting the correct model. The rule is denoted BOSS—Bayesian Order Selection Strategy. The estimators have several advantages: They satisfy certain optimality criteria, they are non-asymptotic and they have low computational complexity. We also derive “empirical Bayesian” versions of BPM and BOSS, which do not require any prior knowledge nor do they need the choice of any “user parameters”. We show that our estimators outperform several classical methods, including the AIC and BIC for order selection.
['Yngve Selén', 'Erik G. Larsson']
Parameter estimation and order selection for linear regression problems