Barankin Bound for Multiple Change-Point Estimation

Author

La Rosa, Patricio S. ; Renaux, Alexandre ; Nehorai, Arye

Author_Institution

Dept. of Electr. & Syst. Eng., Washington Univ. in St. Louis, St. Louis, MO

fYear

2007

fDate

12-14 Dec. 2007

Firstpage

37

Lastpage

40

Abstract

We derive the Barankin bound on the mean-squared error for multiple change-point estimation of an independent measurement sequence. We first derive a general form of this bound and give the structure of the so-called Barankin information matrix (BIM). We show that the BIM for the change-point parameters has a tri-diagonal structure which means that one change-point estimation depends on its neighboring change points. Using this result, we propose a computationally efficient inversion algorithm of the BIM. As an illustration, we analyze the case of changes in the mean vector of a Gaussian distribution.

Keywords

estimation theory; matrix algebra; mean square error methods; Barankin bound; Barankin information matrix; Gaussian distribution; independent measurement sequence; inversion algorithm; mean-squared error; multiple change-point estimation; tri-diagonal structure; Biomedical imaging; Econometrics; Force measurement; Performance analysis; Power capacitors; Probability density function; Random variables; Speech processing; Systems engineering and theory; Testing; Barankin lower bounds on the mean-squared error; Multiple change-point estimation; performance analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Advances in Multi-Sensor Adaptive Processing, 2007. CAMPSAP 2007. 2nd IEEE International Workshop on

Conference_Location

St. Thomas, VI

Print_ISBN

978-1-4244-1713-1

Electronic_ISBN

978-1-4244-1714-8

Type

conf

DOI

10.1109/CAMSAP.2007.4497959

Filename

4497959