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Iterative Least Squares Estimation and Identification of the Transfer Function Model
Author(s) -
Muller Daniel,
Wei William W. S.
Publication year - 1997
Publication title -
journal of time series analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.576
H-Index - 54
eISSN - 1467-9892
pISSN - 0143-9782
DOI - 10.1111/1467-9892.00069
Subject(s) - mathematics , least squares function approximation , generalized least squares , non linear least squares , moment (physics) , transfer function , ordinary least squares , identification (biology) , residual sum of squares , statistics , estimation , function (biology) , iteratively reweighted least squares , estimation theory , basis (linear algebra) , system identification , computer science , data modeling , physics , botany , management , geometry , classical mechanics , estimator , evolutionary biology , electrical engineering , economics , biology , engineering , database
The ordinary least squares method is the most commonly used estimation procedure in statistics but estimates of the input and output parameters through this method for transfer function models are not necessarily consistent. An iterative regression procedure is proposed to produce consistent estimates. Consistent moment estimates are also given. On the basis of these consistent estimates a method of model specification is proposed. An example is given to illustrate the procedure