There are two main reasons that motivate people to detect outliers; the first is the researchers' intention; see the example of Mr Haldum's cases in Barnett and Lewis. The second is the effect of outliers on analyses. This article does not differentiate between the various justifications for outlier detection. The aim was to advise the analyst about observations that are isolated from the other observations in the data set. In this article, we introduce the eigenstructure based angle for outlier detection. This method is simple and effective in dealing with masking and swamping problems. The method proposed is illustrated and compared with Mahalanobis distance by using several data sets.
In this study, group acceptance sampling plan (GASP) proposed by Aslam et al. (2011) is redesigned where the lifetime of
test items are following Pareto distribution of 2nd kind. The optimal plan parameters are found by considering various
pre-determined designed parameters. The plan parameters were obtained using the optimization solution and it also
concludes that the proposed plan is more efficient than the existing plan as it requires minimum sample size.