Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
1148828 | Journal of Statistical Planning and Inference | 2012 | 8 Pages |
Abstract
We study nonlinear least-squares problem that can be transformed to linear problem by change of variables. We derive a general formula for the statistically optimal weights and prove that the resulting linear regression gives an optimal estimate (which satisfies an analogue of the Rao-Cramer lower bound) in the limit of small noise.
Related Topics
Physical Sciences and Engineering
Mathematics
Applied Mathematics
Authors
Shimin Zheng, A.K. Gupta,