. Secondly, we derive a new method DOUW (detecting outliers using weights) which may be used to fit logistic regression models … robustly and for the detection of outliers.In the first problem, the focus is on a measure which may be used to study the … problem, the focus is on providing a robust logistic regression fit andthe identification of outliers. It is well-known that …

, overall caseload acuity profiles, case length, and acuity differentials. Normal distributions and outliers were analyzed and …

the psychological literature to the effect that non-normality and outliers have on the power of a given statistical test …

outliers and influential observations. A variety of diagnostic methods are employed to identify these influential observations … methods utilised proved incapable of identifying or accommodating the gross outlier(s) in the data, the more successful …

access to European markets, and the presence of many outliers due to different reasons. These factors must be considered in …

. However, these sample statistics are notoriously susceptible to contamination by outliers, a problem compounded by the fact … that the outliers may be invisible to conventional diagnostics. High-breakdown estimation is a procedure designed to remove … this cause for concern by producing estimates that are immune to serious distortion by a minority of outliers, regardless …