<p><p><p>This paper considers parametric estimation problems with independent, identically,non-regularly distributed data. It focuses on rate-effciency, in the sense of maximal possible convergence rates of stochastically bounded estimators, as an optimality criterion,largely unexplored in parametric estimation.Under mild conditions, the Hellinger metric,defined on the space of parametric probability measures, is shown to be an essentially universally applicable tool to determine maximal possible convergence rates. These rates are shown to be attainable in general classes of parametric estimation problems.</p></p></p>
Authors
![Walter Beckert](/sites/default/files/styles/square_desktop/public/2022-05/Walter_Beckert.jpg?itok=_Sjg-kgz)
Research Associate Birkbeck, University of London
Walter is a Research Associate at the IFS, a Reader in Economics at Birkbeck, University of London and holds a position in the Academic Panel at CMA.
![Person graphic](/sites/default/files/styles/square_desktop/public/2022-06/IFS-person-graphic.png?itok=hWCtTSrz)
Daniel McFadden
Working Paper details
- DOI
- 10.1920/wp.cem.2007.2807
- Publisher
- IFS
Suggested citation
Beckert, W and McFadden, D. (2007). Maximal uniform convergence rates in parametric estimation problems. London: IFS. Available at: https://ifs.org.uk/publications/maximal-uniform-convergence-rates-parametric-estimation-problems (accessed: 30 June 2024).
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