… We monitor very robust regression by looking at the behaviour of residuals and test
statistics as we smoothly change the robustness of parameter estimation from a breakdown …

… Monitoring time series were simulated. Against these data four robust regression methods
… of squares (LMS), repeated median (RM), and deepest regression (DR). Moreover, online …

… monitoring the logistic regression profile in Phase I under the presence of outliers. Also,
three robust … estimation and monitoring the logistic regression profiles in both Phases I and II. …

… of robust regression to estimate slopes in CBM progress monitoring, we compared the
performance of several robust … typical CBM progress monitoring conditions. Three different …

… regression residual method and the robust regression … The robust regression method needs
iterative calculation, so … In terms of recognition effect, the robust regression residual method …

… In summary, the aim of monitoring systems regarding large static structures could be in
the short-term detection of fault or health novelty/prognosis or in the long-term to develop …

… This paper considers new robust regression Shewhart-type control charts based on the
exponential-type kernel and mean absolute deviation estimators. To evaluate the performance of …

… Because the outlier tests we are monitoring are divided by an estimate of σ 2 that is too
small, we need to scale up the values of the order statistics to obtain the envelopes …

… environmental monitoring … monitoring data collected in Boston Harbor by the Massachusetts
Water Resources Authority. We describe a simple robust approach for estimating regression …

… In this study, a probabilistic outlier detection method for robust regression analysis is
proposed with application to long-term structural monitoring data for high-speed railway systems. …