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Traditional outlier detection methods such as the minimum volume ellipsoid method and MCD method are all based on normal distance. As is well known, the norm-based distance is only effective in detecting difference with dense signals. However, these existing approaches will encounter detection power loss under the sparse signals settings. In this paper, we try to solve the problem of detecting outliers under the sparsity assumption by adapting the thresholding method. The proposed outlier detection procedure finds the clean set by the minimum diagonal product algorithm, then the maximum thresholding statistic is employed to identify the outlier. The finite sample performance of our method is evaluated through simulations. Compared with the existing outlier detection methods, simulation results show that our proposed outlier detection procedure is very efficient under sparse settings. [ABSTRACT FROM AUTHOR]

For testing k upper outliers in a sample from a gamma distribution, the sampling distribution of the likelihood-ratio statistic has not been obtained so far unless k = I or in the case of an exponential distribution. The exact null distribution for k = 2 is found in this paper, and extensive critical values are tabulated. [ABSTRACT FROM AUTHOR]

We study alternative models for capturing abrupt structural changes (level shifts) in a times series. The problem is confounded by the presence of transient outliers. We compare the performance of non-Gaussian time-varying parameter models and multiprocess mixture models within a Monte Carlo experimental setup. Our findings suggest that once we incorporate shocks with thick-tailed probability distributions, the superiority of the multiprocess mixture models over the time-varying parameter models, reported in an earlier study, disappears. The behavior of the two models, both in adapting to level shifts and in reacting to transient outliers, is very similar. [ABSTRACT FROM AUTHOR]

The problem considered is the estimation of the population total of some characteristic from a simple random sample containing a few large or extreme observations. The effect of these large units in the sample is to distort the estimate of the population total. It is therefore important to correct the weights for such units or deflate their values at the estimation stage once they have been sampled and identified as unusually large units. In this paper, three estimators that alter the usual sampling weights have been considered. The efficiencies of these estimators have been worked out in terms of the ratio of the mean squared error of the usual estimator of the population total to the mean squared error of these estimators. A numerical study of these estimators is also discussed. [ABSTRACT FROM AUTHOR]

A few repeats of a simple forward search from a random starting point are shown to provide sufficiently robust parameter estimates to reveal masked multiple outliers the stability of the patterns obtained is exhibited by the stalactite plot The robust estimators used are least median of squares for regression and the minimum volume ellipsoid for multivariate outliers The forward search also has potential as an algorithm for calculation of these parameter estimates For large problems, parallel computing provides appreciable reduction in computational time. [ABSTRACT FROM AUTHOR]