Parameter Estimation for p-Order Random Coefficient Autoregressive (RCA) Models Based on Kalman Filter
Copyright policy )Parameter Estimation for p-Order Random Coefficient Autoregressive (RCA) Models Based on Kalman Filter
In this paper we elaborate an algorithm to estimate p-order Random Coefficient Autoregressive Model (RCA(p)) parameters. This algorithm combines quasi-maximum likelihood method, the Kalman filter, and the simulated annealing method. In the aim to generalize the results found for RCA(1), we have integrated a subalgorithm which calculate the theoretical autocorrelation. Simulation results demonstrate that the algorithm is viable and promising.
- Premier University, Chittagong Bangladesh
- Mohammed V University Morocco
- Mohamed I University Morocco
Function Approximation, Backpropagation Learning, Particle Filtering and Nonlinear Estimation Methods, Kalman Filters, Autoregressive model, Simulated annealing, Inference from stochastic processes and prediction, Control Systems and Network Applications, Engineering, Artificial Intelligence, QA1-939, FOS: Mathematics, Self-Organizing Maps, Statistics, Nonlinear Estimation, Neural Network Fundamentals and Applications, Applied mathematics, Computer science, Extended Kalman filter, Algorithm, Time series, auto-correlation, regression, etc. in statistics (GARCH), Control and Systems Engineering, Autocorrelation, Computer Science, Physical Sciences, Kalman filter, Mathematics
Function Approximation, Backpropagation Learning, Particle Filtering and Nonlinear Estimation Methods, Kalman Filters, Autoregressive model, Simulated annealing, Inference from stochastic processes and prediction, Control Systems and Network Applications, Engineering, Artificial Intelligence, QA1-939, FOS: Mathematics, Self-Organizing Maps, Statistics, Nonlinear Estimation, Neural Network Fundamentals and Applications, Applied mathematics, Computer science, Extended Kalman filter, Algorithm, Time series, auto-correlation, regression, etc. in statistics (GARCH), Control and Systems Engineering, Autocorrelation, Computer Science, Physical Sciences, Kalman filter, Mathematics
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