On applying the extended Kalman filter to nonlinear regression models

In using an extended Kalman filter to estimate the parameters of a nonlinear regression model, the order in which the measurements are processed can be important, as the filter cannot always be expected to produce a satisfactory global fit when processing the measurements in the causal order in which they occur. To obtain a better fit, the possibility is explored of using a sequential state estimator in an offline mode to process the measurements in a random order rather than in the causal order in which they occur