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.
| Identifer | oai:union.ndltd.org:ETSU/oai:dc.etsu.edu:etsu-works-1035 |
| Date | 01 April 2012 |
| Creators | Zheng, Shimin, Gupta, A. K. |
| Publisher | Digital Commons @ East Tennessee State University |
| Source Sets | East Tennessee State University |
| Detected Language | English |
| Type | text |
| Source | ETSU Faculty Works |
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