In this paper, we address the problem of detecting outlier samples with highly different expression patterns in microarray data. Although outliers are not common, they appear even in widely used benchmark data sets and can negatively affect microarray data analysis. It is important to identify outliers in order to explore underlying experimental or biological problems and remove erroneous data....

The aim of this paper is to propose some diagnostic methods in linear ridge measurement error models with stochastic linear restrictions using the corrected likelihood. Based on the bias-corrected estimation of model parameters, diagnostic measures are developed to identify outlying and influential observations. In addition, we derive the corrected score test statistic for outliers detection ba...

In this paper we give a motivation for the shrinking rate 1/ √ n: Let p0 and qn be the outlier probability under the ideal model, and some member of a neighborhood about this ideal model of radius rn, respectively. Assuming n i.i.d. observations, the critical rate of rn may be de ned such that the minimax test for outlier probability qn = p0 vs. qn > p0 has asymptotic error probabilities bounde...

Outlier detection is an important problem that has applications in many fields. High dimensional datasets are common in such applications. Among the existing outlier detection methods, Distance-Based outlier (DB-Outlier) detection is one of the most generalizable and simplest approaches. It finds outliers by calculating distances between data points. However, in high dimensional space, data dis...

Recent research in motion detection has shown that various outlier detection methods could be used for efficient detection of small moving targets. These algorithms detect moving objects as outliers in a properly defined attribute space, where outlier is defined as an object distinct from the objects in its neighborhood. In this paper, we compare the performance of two incremental outlier detec...

We developed a quality assurance (QA) tool, namely microarray outlier filter (MOF), and have applied it to our microarray datasets for the identification of problematic arrays. Our approach is based on the comparison of the arrays using the correlation coefficient and the number of outlier spots generated on each array to reveal outlier arrays. For a human universal reference (HUR) dataset, whi...

Outlier detection is one of the obstacles of big dataset analysis because of its time consumption issues. This paper proposes a fast outlier detection method for big datasets, which is a combination of cell-based algorithms and a ranking-based algorithm with various depths. A cell-based algorithm is proposed to transform a very large dataset to a fairly small set of weighted cells based on pred...

his paper tests for evidence in support of the purchasing power parity (PPP) in the bilateral real exchange rate series of the South African rand against the US dollar. The importance of considering structural breaks in the PPP test is illustrated. Using standard unit root tests without considering structural breaks, the study is unable to reject the null hypothesis of a unit root in the exchan...