Frequency Domain Maximum Likelihood Identification 1
نویسندگان
چکیده
منابع مشابه
Frequency Domain Maximum Likelihood Identification
The multivariable maximum-likelihood estimate is derived for the case of frequency domain data. The relation with the time domain estimate is commented upon. The algorithm is analyzed with respect to consistency and expressions of the asymptotic variance is presented.
متن کاملErrors-in-variables identification using maximum likelihood estimation in the frequency domain
This report deals with the identification of errors–in–variables (EIV) models corrupted by additive and uncorrelated white Gaussian noises when the noise–free input is an arbitrary signal, not required to be periodic. In particular, a frequency domain maximum likelihood (ML) estimator is proposed and analyzed in some detail. As some other EIV estimators, this method assumes that the ratio of th...
متن کاملIdentification and frequency domain quasi-maximum likelihood estimation of linearized dynamic stochastic general equilibrium models
This paper considers issues related to identification, inference, and computation in linearized dynamic stochastic general equilibrium (DSGE) models. We first provide a necessary and sufficient condition for the local identification of the structural parameters based on the (first and) second order properties of the process. The condition allows for arbitrary relations between the number of obs...
متن کاملAccuracy analysis of time domain maximum likelihood method and sample maximum likelihood method for errors-in-variables identification
The time domain maximum likelihood (TML) method and the sample maximum Likelihood (SML) method are two approaches for identifying errors-invariables models. Both methods may give the optimal estimation accuracy (achieve Cramér-Rao lower bound) but in different senses. In the TML method, an important assumption is that the noise-free input signal is modeled as a stationary process with rational ...
متن کاملMaximum likelihood slow frequency-selective fading channel estimation using the frequency domain approach
|This paper addresses the channel estimation problem for slow frequency-selective fading channels using training sequences and the maximum likelihood (ML) approach. In the literature people usually assume a symbol period spaced delay-tapped-line model and additive white Gaussian noise (AWGN). Due to the preltering in the receiver front end, if the sampling rate is larger than one sample per sym...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IFAC Proceedings Volumes
سال: 1997
ISSN: 1474-6670
DOI: 10.1016/s1474-6670(17)43077-1