Grubbs test (extreme studentized deviate test, maximum normed residual test) is used in various fields to identify outliers in a data set, which are ranked in the order of x 1 ≤ x 2 ≤ x 3 ≤ ⋅ ⋅ ⋅ ≤ x n (i = 1, 2, 3, . . . , n). However, ranking of data eliminates the actual sequence of a data series, which is an important factor for determining outliers in some cases (e.g., time series). Thus i...

A theory of exceptional extreme events, characterized by their abnormal sizes compared with the rest of the distribution, is presented. Such outliers, called “dragonkings”, have been reported in the distribution of financial drawdowns, city-size distributions (e.g., Paris in France and London in the UK), in material failure, epileptic seizure intensities, and other systems. Within our theory, t...

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...

BACKGROUND The study of length of stay (LOS) outliers is important for the management and financing of hospitals. Our aim was to study variables associated with high LOS outliers and their evolution over time. METHODS We used hospital administrative data from inpatient episodes in public acute care hospitals in the Portuguese National Health Service (NHS), with discharges between years 2000 a...

The word robustness is frequently used in the literature, and is often stated with completely di®erent meaning. In this contribution robustness means to reduce the in°uence of \unusual" observations on statistical estimates. Such observations are frequently denoted as outliers, and are often thought to be extreme values caused by measurement or transcription errors. However, the notion of outli...

Abstract. Using methods of statistical mechanics, we analyse the effect of outliers on the supervised learning of a classification problem. The learning strategy aims at selecting informative examples and discarding outliers. We compare two algorithms which perform the selection either in a soft or a hard way. When the fraction of outliers grows large, the estimation errors undergo a first-orde...

Multiple outliers are frequently encountered in applied studies in business and economics. Most of the practitioners depend on ordinary least squares (OLS) method for parameter estimation in regression analysis without identifying outliers properly. It is evident that OLS totally fails even in presence of single outlying observation. Single observation outlier detection methods are failed to id...

Identiication of outliers can lead to the discovery of unexpected, and interesting knowledge. Existing methods are designed for detecting spatial outliers in multidimensional geometric data sets, where a distance metric is available. In this paper, we focus on detecting spatial outliers in graph structured data sets. We deene statistical tests, analyze the statistical foundation underlying our ...

This paper proposes a systematic method to classify data with outliers. The essential techniques consist of the outlier detection and the fuzzy support vector machine (FSVM). In this approach, the main body set for each class is first determined by the outlier detection algorithm (ODA) that estimates the outliers based on the total similarity objective function. Then, incorporated with the tota...

Identiication of outliers can lead to the discovery of unexpected and interesting knowledge. Existing methods are designed for detecting spatial outliers in multidimensional geometric data sets, where a distance metric is available. In this paper, we focus on detecting spatial outliers in graph structured data sets. We deene statistical tests, analyze the statistical foundation underlying our a...