Effects of Common Point Distribution and Adjustment Models on Detecting and Distinguishing Gross Error

The classical method for detecting and distinguishing gross errors considers redundant observation components, and inner and external reliabilities are the important robustness indicators of an adjustment. It employs data snooping successively to detect gross errors and find dubitable observations, and correlation coefficients to distinguish two gross errors. Based on the method, we systematically and completely investigate the effects of common point distributions and adjustment models on the redundant observation component, interior and exterior reliabilities, and gross error distinguish ability, and find some reliable and directive results.