Sensitivity analysis in Gauss-Markov models

The estimated parameters from a Gauss-Markov model have varying sensitivity to the individual observations included in the model. Similarly, the redundancy contribution (number) of any observation is associated differently to all the other observations in the model. Evaluation of the sensitivity of parameters to observations, and the sensitivity of the redundancy contribution of one observation to the others are useful to gain more insight into Gauss-Markov models. Such analysis has found practical applications in survey network design and in multiple outlier detections. This paper presents some quantitative sensitivity measures for general Gauss-Markov models. The application of the concept in surveying network design is also discussed.