Inliers Detection Using Schwartz Information Criterion

In failure time distributions, inliers in a data set are subset of observations sufficiently small relative to the rest of the observations, which appears to be inconsistent with the remaining data set. They are either the resultant of instantaneous failures or early failures, experienced in many life-testing experiments. The model used in outliers, where r observations are outliers is modified by ) (E Mr F G ∂ ∂ / strictly decreasing instead of increasing function of X to represent this situation, where F is the target distribution and G generates inliers. Usually number of inliers is not known and is to be determined. We use the information criterion given by Schwartz (1978) to detect the number of inliers in the model. The method is illustrated with a simulated experiment and a real life data.