MP estimation is a method which concerns estimating of the location parameters when the probabilistic models of observations differ from the normal distributions in the kurtosis or asymmetry. The system of Pearson’s distributions is the probabilistic basis for the method. So far, such a method was applied and analyzed mostly for leptokurtic or mesokurtic distributions (Pearson’s distributions of types IV or VII), which predominate practical cases. The analyses of geodetic or astronomical observations show that we may also deal with sets which have moderate asymmetry or small negative excess kurtosis. Asymmetry might result from the influence of many small systematic errors, which were not eliminated during preprocessing of data. The excess kurtosis can be related with bigger or smaller (in relations to the Hagen hypothesis) frequency of occurrence of the elementary errors which are close to zero. Considering that fact, this paper focuses on the estimation with application of the Pearson platykurtic distributions of types I or II. The paper presents the solution of the corresponding optimization problem and its basic properties. Although platykurtic distributions are rare in practice, it was an interesting issue to find out what results can be provided by MP estimation in the case of such observation distributions. The numerical tests which are presented in the paper are rather limited; however, they allow us to draw some general conclusions.
The method that is proposed in the present paper is a special case of squared M split estimation. It concerns a direct estimation of the shift between the parameters of the functional models of geodetic observations. The shift in question may result from, for example, deformation of a geodetic network or other non-random disturbances that may influence coordinates of the network points. The paper also presents the example where such shift is identified with a phase displacement of a wave. The shift is estimated on the basis of wave observations and without any knowledge where such displacement took place. The estimates of the shift that are proposed in the paper are named Shift- M split estimators.