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A Parameter‐Elimination Method for Nonlinear Regression with Linear Parameters and Autocorrelated Errors
Author(s) -
Huang M.N. L.,
Huang M. K.
Publication year - 1991
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/bimj.4710330806
Subject(s) - autoregressive model , mathematics , monte carlo method , dimension (graph theory) , autocorrelation , nonlinear regression , statistics , regression , linear regression , nonlinear system , convergence (economics) , regression analysis , physics , quantum mechanics , pure mathematics , economics , economic growth
In this study, we are interested in the problem of estimating the parameters in a nonlinear regression model when the error terms are correlated. Throughout this work, we restrict ourselves to the special case when the error terms follow a pth order stationary autoregressive model ( AR(p )). Following the idea of LAWTON and SYLVESTRE (1971) and GALLANT and GOEBEL (1976), a parameter‐elimination method is proposed, which has the advantages that it is not sensitive to the initial values and convergence of the procedure may be more stable because of the reduced dimension of the problem. The parameter‐elimination method is compared with the methods by GALLANT and GOEBEL (1976) and GLASBEY (1980) by Monte Carlo Simulation, and the results of applying the first two methods to the real data obtained from the Environmental Protection Administration of the Executive Yuan of the Republic of China are presented.