Robust estimation and hypothesis testing under short-tailedness and inliers

Estimation and hypothesis testing based on normal samples censored in the middle are developed and shown to be remarkably efficient and robust to symmetric shorttailed distributions and to inliers in a sample. This negates the perception that sample mean and variance are the best robust estimators in such situations (Tiku, 1980; Dunnett, 1982). Professor Emeritus, Department of Mathematics and Statistics, McMaster University, Professor Emeritus, Department of Mathematics and Statistics, McMaster University,