Outlier detection and environmental association analysis are common methods to search for loci or genomic regions exhibiting signals of adaptation to environmental factors. However, a validation of outlier loci and corresponding allele distribution models through functional molecular biology or transplant/common garden experiments is rarely carried out. Here, we employ another method for valida...

This paper will examine the relationship between "Data Envelopment Analysis" and a statistical concept ``Outlier". Data envelopment analysis (DEA) is a method for estimating the relative efficiency of decision making units (DMUs) having similar tasks in a production system by multiple inputs to produce multiple ‎outputs.‎ An important issue in statistics is to identify the outliers. In this pap...

In this paper the use of outlier detection methods is discussed. This analysis is an introduction to the use of various methods of outlier detection in medical diagnoses (screening). The authors investigated the usefulness of selected outlier detection methods in the context of detection sensitivity, speed performance analysis and the difficulty of automating the performance analysis by using t...

This paper introduces a novel wrapper-based outlier detection framework (WrapperOD) and its instance (HOUR) for identifying outliers in noisy data (i.e., data with noisy features) with strong couplings between outlying behaviors. Existing subspace or feature selection-based methods are significantly challenged by such data, as their search of feature subset(s) is independent of outlier scoring ...

Outlier detection is an important process for text document collections, but as the collection grows, the detection process becomes a computationally expensive task. Random projection has shown to provide a good fast approximation of sparse data, such as document vectors, for outlier detection. The random samples of Fourier and cosine spectrum have shown to provide good approximations of sparse...

Outlier detection is a fundamental step in knowledge discovery in databases. With the increasing number of high-dimensional databases, existing outlier detection algorithms that work only in the context of full space are unable to effectively screen out informative outliers. This is because majority of these outliers exists only in subspaces. In this paper, we identify a new outlier detection t...

Outlier detection is a substantial research problem in the domain of data mining that aims to uncover objects which exhibit significantly different, exceptional and inconsistent from rest of the data. Outlier detection has been widely researched and finds use within various application domains including tax fraud detection, network robustness analysis, network intrusion and medical diagnosis. I...

We searched a gene expression dataset comprised of 634 schizophrenia (SZ) cases and 713 controls for expression outliers (i.e., extreme tails of the distribution of transcript expression values) with SZ cases overrepresented compared with controls. These outlier genes were enriched for brain expression and for genes known to be associated with neurodevelopmental disorders. SZ cases showed highe...