Accession Number:
ADA037678
Title:
Levinson- and Chandrasekhar-Type Equations for a General, Discrete-Time Linear Estimation Problem,
Descriptive Note:
Corporate Author:
STANFORD UNIV CALIF DEPT OF ELECTRICAL ENGINEERING
Personal Author(s):
Report Date:
1976-12-01
Pagination or Media Count:
13.0
Abstract:
Recursive algorithms for the solution of linear least-squares estimation problems have been based mainly on state-space models. It has been known, however, that such algorithms exist for stationary time-series, using input-output descriptions e.g., covariance matrices. We introduce a way of classifying stochastic processes in terms of their distance from stationarity that leads to a derivation of an efficient Levinson-type algorithm for arbitrary nonstationary processes. By adding structure to the covariance matrix, these general results specialize to state-space type estimation algorithms. In particular, the Chandrasekhar equations are shown to be the natural descondants of the Levinson algorithm. Author
Descriptors:
Subject Categories:
- Statistics and Probability