On the efficiency of misspecified Gaussian inference in nonlinear regression: application to time-delay and Doppler estimation
Résumé
Nonlinear regression play a crucial role in various engineering applications. For the sake of mathematical tractability and ease of implementation, most of the existing inference procedures are derived under the assumption of independent and identically distributed (i.i.d.) Gaussian-distributed data. However, real-world situations often deviate from this assumption, with the true data generating process being a correlated, heavytailed and non-Gaussian one. The paper aims at providing the Misspecified Cramér-Rao Bound (MCRB) on the Mean Squared Error (MSE) of any unbiased (or at least consistent) estimator of the parameters of a nonlinear regression model derived under the i.i.d. Gaussian assumption in the place of the actual correlated, non-Gaussian data generating process. As a special case, the MCRB for an uncorrelated, i.i.d. Complex Elliptically Symmetric (CES) data generating process under Gaussian assumption is also provided. Consistency and asymptotic normality of the related Mismatched Maximum Likelihood Estimator (MMLE) will be discussed along with its connection with the Nonlinear Least Square Estimator (NLLSE) inherent to the nonlinear regression model. Finally, the derived theoretical findings will be applied in the well-known problem of time-delay and Doppler estimation for GNSS.
Mots clés
Fichier principal
On_the_efficiency_of_misspecified_Gaussian_procedures_in_nonlinear_regression.pdf (516.96 Ko)
Télécharger le fichier
Supporting_material.pdf (259.05 Ko)
Télécharger le fichier
Origine | Fichiers produits par l'(les) auteur(s) |
---|
Origine | Fichiers produits par l'(les) auteur(s) |
---|