Random Parameter Estimation Research Articles

A Barankin-type lower bound on the estimation error of a hybrid parameter vector

The Barankin (1949) bound is a realizable lower bound on the mean-square error (MSE) of any unbiased estimator of a (nonrandom) parameter vector. We present a Barankin-type bound which is useful in problems where there is a prior knowledge on some of the parameters to be estimated. That is, the parameter vector is a hybrid vector in the sense that some of its entries are deterministic while other are random variables. We present a simple expression for a positive-definite matrix which provides bounds on the covariance of any unbiased estimator of the nonrandom parameters and an estimator of the random parameters, simultaneously. We show that the Barankin bound for deterministic parameters estimation and the Bobrovsky-Zakai (1976) bound for random parameters estimation are special cases of our proposed bound.

Relative sufficiency

By the classical definition of a sufficient statistic, either a statistic is or is not sufficient. A measure called relative sufficiency, by which the degree of sufficiency can be defined for the problem of random parameter estimation is presented. It is shown that relative sufficiency is equivalent to the Kullback-Leibler information measure. Its calculation and interpretation are illustrated for the estimation of a scalar random variable. >

Lower bounds on the mean square estimation error

A general class of lower bounds on the mean square error (mse) in random parameter estimation is formulated. These bounds are generated using functions of the parameter and the data that are orthogonal to the data. A particular choice in the class yields a new lower bound which is superior to both the Cramer-Rao and Bobrovsky-Zakai lower bounds.

Partitioned adaptive estimation of time-varying random parameters with applications to economic forecasting

Partitioned adaptive estimation of time-varying random parameters with applications to economic forecasting