Please use this identifier to cite or link to this item:
|Scopus||Web of Science®|
|Title:||Computing constrained Cramér-Rao bounds|
|Other Titles:||Computing constrained Cramer-Rao bounds|
|Citation:||IEEE Transactions on Signal Processing, 2012; 60(10):5543-5548|
|Publisher:||IEEE-Inst Electrical Electronics Engineers Inc|
|Abstract:||We revisit the problem of computing submatrices of the Cramér-Rao bound (CRB), which lower bounds the variance of any unbiased estimator of a vector parameter ɵ. We explore iterative methods that avoid direct inversion of the Fisher information matrix, which can be computationally expensive when the dimension of ɵ is large. The computation of the bound is related to the quadratic matrix program, where there are highly efficient methods for solving it. We present several methods, and show that algorithms in prior work are special instances of existing optimization algorithms. Some of these methods converge to the bound monotonically, but in particular, algorithms converging nonmonotonically are much faster. We then extend the work to encompass the computation of the CRB when the Fisher information matrix is singular and when the parameter ɵ is subject to constraints. As an application, we consider the design of a data streaming algorithm for network measurement.|
|Keywords:||Cramér-Rao bound; Fisher information; matrix functions; optimization; quadratic matrix program|
|Rights:||© 2012 IEEE|
|Appears in Collections:||Mathematical Sciences publications|
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.