For deriving the robust estimation by the EM (expectation maximization) algorithm for a model, which is more general than the linear model, the nonlinear Gauss Helmert (GH) model is chosen. It contains the errors-in-variables model as a special case. The nonlinear GH model is difficult to handle because of the linearization and the Gauss Newton iterations. Approximate values for the observations have to be introduced for the linearization.
Koch, K. Robust estimations for the nonlinear Gauss Helmert model by the expectation maximization algorithm,
Springer Berlin Heidelberg, 2014, с. 263-271.
Koch, K. .
Robust estimations for the nonlinear Gauss Helmert model by the expectation maximization algorithm.
: Springer Berlin Heidelberg, 2014, с. 263-271.
Koch, K. (2014)
Robust estimations for the nonlinear Gauss Helmert model by the expectation maximization algorithm,
: Springer Berlin Heidelberg, с. 263-271
Koch, K.
(2014).
Robust estimations for the nonlinear Gauss Helmert model by the expectation maximization algorithm. Journal of Geodesy. Springer Berlin Heidelberg 88 (3), с. 263-271.
