Moments and Error Expressions in Polynomial Minimum Mean Square Estimation
estimator; polynomial; matrix expression
The mathematical complexity of the minimum mean square estimators made inevitable the consideration of suboptimal solutions, such as the linear minimum mean square estimators. The compromise between performance and complexity can be in general less serious if the estimator that will substitute the optimum one is polynomial. If the minimum mean square estimator happens to be equal to a polynomial one, the polynomial substitution does not involve any compromise with respect to performance. Balakrishnan  found a necessary and sufficient condition satisfied by the joint characteristic functions of observations and variable to be estimated, so that the m.m.s. estimiate is a polynomial. The equivalent relationships in this case were found in the present paper. A matrix expression of the error difference from two different m.m.s. polynomial estimators was also found. This form involves much fewer calculations than required for finding separately the two errors.
MetadataShow full item record
- ECE Publications 
Showing items related by title, author, creator and subject.
Papantoni-Kazakos, P.; Kazakos, D. (1974-11-20)The spectrum of a digital FM signal can be considered as an indicator of the resistance of the signal to distortions caused by band-limitation. The study in this paper is oriented toward the design of a signaling pulses ...
Papantoni-Kazakos, P.; Kazakos, D. (1974-10-20)The RC filter-hard limiter-RC filter nonlinear system shown in Fig. 1 is the subject of this paper. Because of computational difficulties implicated in the analysis of the above system, only its response to the zero mean ...
Papantoni-Kazakos, P. (1975-08-20)This paper is concerned with estimates of an unknown vector parameter S based on observations X. A generalized error autocorrelation matrix with components the error moments of order 2p, is defined. A lower bound for ...