Resilient Consensus for Robust Multiplex Networks with Asymmetric Confidence Intervals

Robust misinterpretation of confidence intervals.

Null hypothesis significance testing (NHST) is undoubtedly the most common inferential technique used to justify claims in the social sciences. However, even staunch defenders of NHST agree that its outcomes are often misinterpreted. Confidence intervals (CIs) have frequently been proposed as a more useful alternative to NHST, and their use is strongly encouraged in the APA Manual. Nevertheless...

Globally Robust Confidence Intervals for Location

Classical inference considers sampling variability to be the only source of uncertainty, and does not address the issue of bias caused by contamination. Naive robust intervals replace the classical estimates by their robust counterparts without considering the possible bias of the robust point estimates. Consequently, the asymptotic coverage proportion of these intervals of any nominal level wi...

Confidence Intervals and Prediction Intervals for Feed-Forward Neural Networks

Artificial neural networks have been used as predictive systems for variety of medical domains, but none of the systems encountered by Baxt (1995) and Dybowski & Gant (1995) in their review of the literature provided any measure of confidence in the predictions made by those systems. In a medical setting, measures of confidence are of paramount importance (Holst, Ohlsson, Peterson & Edenbrandt ...

Confidence Intervals Based on Robust Estimators

Robust Confidence Intervals for Average Treatment Effects under Limited Overlap

Robust Confidence Intervals for Average Treatment Effects under Limited Overlap Estimators of average treatment effects under unconfounded treatment assignment are known to become rather imprecise if there is limited overlap in the covariate distributions between the treatment groups. But such limited overlap can also have a detrimental effect on inference, and lead for example to highly distor...