Title

Maximum likelihood estimator under a misspecified model with high signal-to-noise ratio

Document Type

Article

Date of Original Version

8-1-2011

Abstract

It is well known that the maximum-likelihood estimator (MLE) under a misspecified model converges to a well defined limit and it is asymptotically Gaussian as the sample size goes to infinity. In this correspondence, we consider a misspecified model with deterministic signal embedded in Gaussian noise and fully characterize the asymptotic performance of the MLE under this misspecified model with high signal-to-noise (SNR). We see that under some regularity conditions, it converges to a well defined limit and is asymptotically Gaussian with high SNR. © 2011 IEEE.

Publication Title

IEEE Transactions on Signal Processing

Volume

59

Issue

8

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