Globally Robust Confidence Intervals for Location

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...

Constructing confidence intervals for QTL location.

We describe a method for constructing the confidence interval of the QTL location parameter. This method is developed in the local asymptotic framework, leading to a linear model at each position of the putative QTL. The idea is to construct a likelihood ratio test, using statistics whose asymptotic distribution does not depend on the nuisance parameters and in particular on the effect of the Q...

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...

Confidence Intervals Based on Robust Estimators

On Globally Robust Conndence Intervals for Regression Coeecients

1 Abstract We will develop conndence intervals for linear regression coeecients when the para-metric model is violated by the presence of a fraction of outliers and high leverage data points. Our method will be based on a robustiied bootstrap technique. Unlike the classical bootstrap, our robust bootstrap does not produce unduly heavy tails or extreme re-sampled statistics when the original sam...