A New Approach to Statistical Efficiency of Weighted Least Squares Fitting Algorithms for Reparameterization of Nonlinear Regression Models
Document Type
Article
Publication Date
4-1-2012
Description
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.
Citation Information
Zheng, Shimin; and Gupta, A. K.. 2012. A New Approach to Statistical Efficiency of Weighted Least Squares Fitting Algorithms for Reparameterization of Nonlinear Regression Models. Journal of Statistical Planning and Inference. Vol.142(4). 1001-1008. https://doi.org/10.1016/j.jspi.2011.11.011 ISSN: 0378-3758