ignoring two main characteristics of the concentration-response data, correlation between observations and presence of outliers, may lead to misleading results. therefore the special method should be considered. in this paper in to examine the effect of phenylephrine in rat corpus cavernosum, outlier robust nonlinear mixed estimation is used. in this study, eight different doses of phenylephrin...

In this paper we will propose a new probabilistic topic model to score the expertise of participants on the projects that they contribute to based on their previous experience. Based on each participant’s score, we rank participants and define those who have the lowest scores as outlier participants. Since the focus of our study is on outliers, we name the model as Mining Outlier Participants f...

Plain vanilla K-means clustering is prone to produce unbalanced clusters and suffers from outlier sensitivity. To mitigate both shortcomings, we formulate a joint outlier-detection and clustering problem, which assigns a prescribed number of datapoints to an auxiliary outlier cluster and performs cardinality-constrained K-means clustering on the residual dataset. We cast this problem as a mixed...

This paper suggests an outlier detection procedure which applies a nonparametric model accounting for undesired outputs and exogenous influences in the sample. Although efficiency is estimated in a deterministic frontier approach, each potential outlier initially benefits of the doubt of not being an outlier. We survey several outlier detection procedures and select five complementary methodolo...

Continuous outlier detection in data streams has important applications in fraud detection, network security, and public health. The arrival and departure of data objects in a streaming manner impose new challenges for outlier detection algorithms, especially in time and space efficiency. In the past decade, several studies have been performed to address the problem of distance-based outlier de...

Outlier detection, a data mining technique to detect rare events, deviant objects, and exceptions from data, has been drawing increasing attention in recent years. Most existing outlier detection algorithms focus on numerical data sets. We target categorical record databases and detect records in which many attribute values are not observed even though they should occur in association with othe...

Of all the data mining techniques, outlier detection seems closest to the definition of “discovering nuggets of information” in large databases. When an outlier is detected, and determined to be genuine, it can provide insights, which can radically change our understanding of the underlying process. The purpose of the research underlying this thesis was to investigate and devise methods to mine...

In many application areas, it is important to detect outliers. Traditional engineering approach to outlier detection is that we start with some “normal” values , compute the sample average , the sample standard variation , and then mark a value as an outlier if is outside the -sigma interval (for some pre-selected parameter ). In real life, we often have only interval ranges for the normal valu...

Multi-task feature selection methods often make the hypothesis that learning tasks share relevant and irrelevant features. However, this hypothesis may be too restrictive in practice. For example, there may be a few tasks with specific relevant and irrelevant features (outlier tasks). Similarly, a few of the features may be relevant for only some of the tasks (outlier features). To account for ...

Outlier detection statistics based on two models, the case-deletion model and the mean-shift model, are developed in the context of a multivariate linear regression model. These are generalizations of the univariate Cook’s distance and other diagnostic statistics. Approximate distributions of the proposed statistics are also obtained to get suitable cutoff points for significance tests. In addi...