Print Email Facebook Twitter Distributional theory for the DIA method Title Distributional theory for the DIA method Author Teunissen, P.J.G. (TU Delft Mathematical Geodesy and Positioning; Curtin University) Date 2017-07-06 Abstract The DIA method for the detection, identification and adaptation of model misspecifications combines estimation with testing. The aim of the present contribution is to introduce a unifying framework for the rigorous capture of this combination. By using a canonical model formulation and a partitioning of misclosure space, we show that the whole estimation–testing scheme can be captured in one single DIA estimator. We study the characteristics of this estimator and discuss some of its distributional properties. With the distribution of the DIA estimator provided, one can then study all the characteristics of the combined estimation and testing scheme, as well as analyse how they propagate into final outcomes. Examples are given, as well as a discussion on how the distributional properties compare with their usage in practice. Subject Baarda test statisticBest linear unbiased estimation (BLUE)Best linear unbiased prediction (BLUP)BiasCorrect detection (CD)Correct identification (CI)Detection, Identification and Adaptation (DIA)DIA estimatorHazardous probabilityMisclosure partitioningMissed detection (MD)Tienstra transformationVoronoi-partitioning unit sphere To reference this document use: http://resolver.tudelft.nl/uuid:ff36e75d-966c-446f-b86d-3e8726ce84f1 DOI https://doi.org/10.1007/s00190-017-1045-7 ISSN 0949-7714 Source Journal of Geodesy, 92 (2018), 59-80 Part of collection Institutional Repository Document type journal article Rights © 2017 P.J.G. Teunissen Files PDF 10.1007_s00190_017_1045_7.pdf 1.32 MB Close viewer /islandora/object/uuid:ff36e75d-966c-446f-b86d-3e8726ce84f1/datastream/OBJ/view