The purpose of this paper is to construct a new non-parametric detector of univariate outliers and to study its asymptotic properties. This detector is based on a Hill’s type statistic. It satisfies a unique asymptotic behavior for a large set of probability distributions with positive unbounded support (for instance: for the absolute value of Gaussian, Gamma, Weibull, Student or regular variations distributions). We have illustrated our results by numerical simulations which show the accuracy of this detector with respect to other usual univariate outlier detectors (Tukey, MAD or Local Outlier Factor detectors). The detection of outliers in a database providing the prices of used cars is also proposed as an application to real-life database.
SUMMARY The paper gives an overview of concepts and techniques pertaining to (i) the robust estimation of multivariate location and dispersion; (ii) the analysis of two types of multidimensional residuals-namely those that occur in the context of principal components analysis as well as the more familiar residuals associated with least squares fitting; and (iii) the detection of multiresponse outliers. The emphasis is on methods for informal exploratory analysis and the coverage is both a survey of existing techniques and an attempt to propose, tentatively, some new methodology which needs further investigation and development. Some examples of use of the methods are included.
MLL1 regulates circadian promoters by depositing H3K4 trimethyl marks, whose levels are also modulated by the NAD+-dependent deacetylase SIRT1. SIRT1 is now shown to promote circadian deacetylation of MLL1, thus affecting MLL1's methyltransferase activity.