Abstract
The rank-deficiency of a linear model indicates some information deficit that may be covered by “prior information” (p.i.) in spite of its uncertainty. There are several ways of introducing such p.i., which may be characterized as hierarchical or simultaneous. Here, three hierarchical methods will be compared with four simultaneous methods; in particular, the question of rescaling the p.i. itself or only its dispersion matrix will be investigated. A small (surveying) leveling network will serve as a numerical example for the comparison.
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Schaffrin, B., Snow, K., Fang, X. (2018). Alternative Approaches for the Use of Uncertain Prior Information to Overcome the Rank-Deficiency of a Linear Model. In: Tez, M., von Rosen, D. (eds) Trends and Perspectives in Linear Statistical Inference . Contributions to Statistics. Springer, Cham. https://doi.org/10.1007/978-3-319-73241-1_12
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DOI: https://doi.org/10.1007/978-3-319-73241-1_12
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