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, e.g., Journal
IEEE Transactions on Signal Processing
Volume
59
Issue
8
Citation/Publisher Attribution
Ding, Quan, and Steven Kay. "Maximum likelihood estimator under a misspecified model with high signal-to-noise ratio." IEEE Transactions on Signal Processing 59, 8 (2011): 4012-4016. doi: 10.1109/TSP.2011.2150220.
