Blunder Detection and Data Snooping in LS and Robust Adjustments

Abstract

There are many procedures for adjusting data and detecting the presence of blunders in a set of observations. Most such procedures involve examining the adjustment results for residuals whose magnitude is in some sense “large.” In data snooping, each residual is divided by its own standard deviation, resulting in a statistic whose distribution is known. Thus blunder detection becomes a statistical hypothesis testing problem. In iterated data snooping, only the observation with the largest normalized residual is deleted at each iteration. The residuals may be either from a conventional least‐squares LS adjustment or from an “