A Large Deviation Result for Parameter Estimators and its Application to Nonlinear Regression Analysis

Elaborating on the work of Ibragimov and Has'minskii (1981) we prove a law of large deviations (LLD) for M-estimators, i.e., those estimators which maximize a functional, continuous in the parameter, of the observations. This LLD is applied, using the results of Petrov (1975), to the problem of...

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Veröffentlicht in:	The Annals of statistics 1987-09, Vol.15 (3), p.1031-1049
Hauptverfasser:	Sieders, Arthur, Dzhaparidze, Kacha
Format:	Artikel
Sprache:	eng
Schlagworte:	60F10 62F12 62J02 Continuous functions Estimators Exact sciences and technology large deviations least-squares M-estimation Mathematical functions Mathematical independent variables Mathematical theorems Mathematics Michaelis-Menten model nonlinear regression Parametric inference Polynomials Probability and statistics Random variables rate of convergence Regression analysis Sciences and techniques of general use Statistical theories Statistics
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