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In principle, the parameters of the LSTAR can be estimated by applying nonlinear least squares, which is equivalent to the maximum likelihood estimation when the errors are normal and independent. a nonlinear least squares fitting algorithm that adjusts model parameters to determine the best match, as judged by minimizing the sum of squares of residuals. 8) Because of overidentification and nonlinear restrictions present in equation (2) we adopt nonlinear least squares (Gauss-Newton) estimation. |
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