2005Unpublished venueRequires access

On the computation of the Cramer Rao bound for ARMA parameter estimation

B. Friedlander

Open publisher page 2 citations

Abstract

The Cramer-Rao lower bound (CRLB) provides a useful tool for evaluating the performance of parameter estimation techniques. Several techniques for the computation of the CRLB for ARMA and AR-plus-noise models are presented. It is shown that the CRLB can be expressed as an explicit function of the model parameters.

About this research paper

What this paper is about

The Cramer-Rao lower bound (CRLB) provides a useful tool for evaluating the performance of parameter estimation techniques. Several techniques for the computation of the CRLB for ARMA and AR-plus-noise models are presented. It is shown that the CRLB can be expressed as an explicit function of the model parameters.

Why it matters

OpenAlex reports 2 citations for this work. Citation counts describe recorded attention and do not establish research quality.

Key contribution

A contribution statement is not available in the OpenAlex record.

Method / approach

Method details are not available in the OpenAlex metadata.

Main findings

Findings are not separately available in the OpenAlex metadata.

Limitations

Limitations are not available in the OpenAlex metadata.

Applications

Application details are not available in the OpenAlex metadata.

Available abstract

The Cramer-Rao lower bound (CRLB) provides a useful tool for evaluating the performance of parameter estimation techniques. Several techniques for the computation of the CRLB for ARMA and AR-plus-noise models are presented. It is shown that the CRLB can be expressed as an explicit function of the model parameters.

Key concepts: Cramér–Rao bound, Estimation theory, Upper and lower bounds, Computation, Algorithm, Mathematics, Function (biology), Computer science

Related papers

Back to paper searchBrowse research topicsOriginal source
On the computation of the Cramer Rao bound for ARMA parameter estimation — Research Paper | ScholarLens