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The maximum likelihood estimation for multivariate EIV model
Abstract In this paper, a new method of parameter estimation for multivariate errors-in-variables (MEIV) model was proposed. The formulae of parameter solution for the MEIV model were deduced based on the principle of maximum likelihood estimation, and two iterative algorithms were presented. Since the iterative process is similar to the classical least square, both of the proposed algorithms are easy to program and understand. Finally, real and simulation datasets of affine coordinate transformation were employed to verify the applicability of the proposed algorithms. The results show that both of the proposed algorithms can achieve identical parameter estimators as those obtained by Lagrange algorithm and Newton algorithm. Additionally, the proposed Algorithm 2 can solve the MEIV model with higher convergence efficiency than Algorithm 1.
The maximum likelihood estimation for multivariate EIV model
Abstract In this paper, a new method of parameter estimation for multivariate errors-in-variables (MEIV) model was proposed. The formulae of parameter solution for the MEIV model were deduced based on the principle of maximum likelihood estimation, and two iterative algorithms were presented. Since the iterative process is similar to the classical least square, both of the proposed algorithms are easy to program and understand. Finally, real and simulation datasets of affine coordinate transformation were employed to verify the applicability of the proposed algorithms. The results show that both of the proposed algorithms can achieve identical parameter estimators as those obtained by Lagrange algorithm and Newton algorithm. Additionally, the proposed Algorithm 2 can solve the MEIV model with higher convergence efficiency than Algorithm 1.
The maximum likelihood estimation for multivariate EIV model
Wang, Qisheng (Autor:in) / Hu, Youjian (Autor:in) / Wang, Bin (Autor:in)
2019
Aufsatz (Zeitschrift)
Englisch
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