MINIMUM BIAS PRIORS FOR ESTIMATING PARAMETERS OF ADDITIVE TERMS IN STATE-SPACE MODELS

We treat the problem of estimating parameters of additive terms, somet imes called bias terms, in state-space models. We consider models that depend linearly on the state but possibly nonlinearly on the paramete rs, where both the state and observation are corrupted by additive noi se. A prior density for the parameters is introduced that, when combin ed with the likelihood function to form a posterior density, minimizes the bias of the posterior mean. The result is a useful prior based on ignorance. Two examples and simulations illustrate the use of the pri or.