Multivariate Outlier Research Articles

Development of a Comprehensive Multi-Factor Method for Comparing Batting Performances in One-Day International Cricket

In cricket, one-day-international (ODI) batsmen have traditionally been compared on the dimensions of batting average (BA) and strike rate (SR). The conventional method of computing BA assumes that runs scored by a batsman follow an exponential or geometric distribution. This results in unreasonably equating batting inconsistency with batting mean. Our study shows that a Weibull distribution model gives a very sensible assessment of a batsman’s inconsistency, independent of his BA. It also provides a superior fit to batting scores of ODI batsmen. We also introduce a measure for ‘quality-runs’ scored by a batsman which takes into account the difficulty level of opposition. Additionally, longevity index and opposition diversity index are defined to make comparisons more holistic. A substantial amount of data engineering effort is made in segregating available data into home, away and neutral matches. The measures proposed in this paper are more comprehensive and granular than those found in the literature. Various combinations of these six criteria are used to rank a select group of great ODI batsmen by assigning objective weights derived from principal component analysis. Finally, multivariate statistical outlier detection procedure produces different lists of outstanding players corresponding to different combinations of criteria. Our proposed methodology may be gainfully used by a team management to select best batsmen in a given situation.

A New Procedure of Clustering Based on Multivariate Outlier Detection

Clustering is an extremely important task in a wide variety of ap plication domains especially in management and social science research. In this paper, an iterative procedure of clustering method based on multivariate outlier detection was proposed by using the famous Mahalanobis distance. At first, Mahalanobis distance should be calculated for the entire sample, then using T 2 -statistic fix a UCL. Above the UCL are treated as outliers which are grouped as outlier cluster and repeat the same procedure for the remaining inliers, until the variance-covariance matrix for the variables in the last cluster achieved singularity. At each iteration, multivariate test of mean used to check the discrimination between the outlier clusters and the inliers. Moreover, multivariate control charts also used to graphically visual izes the iterations and outlier clustering process. Finally multivariate test of means helps to firmly establish the cluster discrimination and validity. This paper employed this procedure for clustering 275 customers of a famous two wheeler in India based on 19 different attributes of the two wheeler and its company. The result of the proposed technique confirms there exist 5 and 7 outlier clusters of customers in the entire sample at 5% and 1% significance level respectively.

P 34. High-order body representations affect human body sway

Motivation & Objectives The variance of human body sway can be explained by anthropometric parameters, such as body height and weight, to some extent. However, high-level factors, such as body representations, might additionally influence postural control, as people with distorted body representations, like anorexic and bulimic patients, have shown more body sway in previous literature. Moreover, anthropometry has been seen to have a different relationship with body sway dependent on sex, showing a higher influence on body sway in males than in females. Therefore, the aim of this study is to investigate the relationship between body representations and postural control, and the influence of a participant's gender. We expected body image and body schema accuracy to affect postural control. Furthermore, gender differences were expected to alter the influence of anthropometry and body image on body sway control. Methodology Anthropometric measurements were taken in 44 young adults (26.93 ± 3.63 years) with a Seca scale and a flexible measurement tape. As body image measures Body Image Distortion (BID) and Body Image Dissatisfaction (BIDS) were assessed. Body schema was assessed with both the Hand and Foot Laterality Task (limb-specific motor imagery task) and a whole-body taking-a-posture task. Body sway was assessed in two normal bipedal quiet standing conditions with 6 trials of 35s duration each: 1) Eyes closed and 2) Head in Neck, Eyes closed. To analyze the relationship between body representations and human body sway, a Hierarchical Multiple Linear Regression was calculated with the following levels: 1) stance condition, 2) anthropometry, 3) body image, 4) body schema. Results Due to missing data and multivariate outliers, four subjects were excluded from statistical analysis. Across 40 subjects (20 females, 20 males), adding body image measures increased the explained variance significantly. Furthermore, adding body schema measures also increased the explained variance significantly. Therefore, the final model containing body image and body schema, additionally to stance condition and body height, explained around 45% of body sway. Moreover, gender differences in the relationship between anthropometry, body image, and limb-specific body schema were observed. While in females, body height and foot-specific body schema did not increase explained variance, it did so in males. In contrast, adding body image measures explained variance in females, but not in males. Conclusion High-order body representations, such as body image dissatisfaction, body image distortion, and body schema accuracy, affect the control of human body sway. Furthermore, the influence of anthropometry, body image, and limb-specific body schema on body sway control seems to be altered by gender.

Assessing the statistical uniqueness of the Younger Dryas: a robust multivariate analysis

Abstract. During the last glacial period (ca. 120–11 kyr BP), dramatic temperature swings, known as Dansgaard–Oeschger (D–O) events, are clearly manifest in high-resolution oxygen isotope records from the Greenland Ice Sheet. Although variability in the Atlantic Meridional Overturning Circulation (AMOC) is often invoked, a unified explanation for what caused these “sawtooth-shaped” climate patterns has yet to be accepted. Of particular interest is the most recent D–O-shaped climate pattern that occurred from ∼ 14 600 to 11 500 years ago – the Bølling–Allerød (BA) warm interstadial and the subsequent Younger Dryas (YD) cold stadial. Unlike earlier D–O stadials, the YD is frequently considered a unique event, potentially resulting from a rerouting and/or flood of glacial meltwater into the North Atlantic or a meteorite impact. Yet, these mechanisms are less frequently considered as the cause of the earlier stadials. Using a robust multivariate outlier detection scheme – a novel approach for traditional paleoclimate research – we show that the pattern of climate change during the BA/YD is not statistically different from the other D–O events in the Greenland record and that it should not necessarily be considered unique when investigating the drivers of abrupt climate change. In so doing, our results present a novel statistical framework for paleoclimatic data analysis.

Infrequent Pattern Detection for Reliable Network Traffic Analysis Using Robust Evolutionary Computation.

While anomaly detection is very important in many domains, such as in cybersecurity, there are many rare anomalies or infrequent patterns in cybersecurity datasets. Detection of infrequent patterns is computationally expensive. Cybersecurity datasets consist of many features, mostly irrelevant, resulting in lower classification performance by machine learning algorithms. Hence, a feature selection (FS) approach, i.e., selecting relevant features only, is an essential preprocessing step in cybersecurity data analysis. Despite many FS approaches proposed in the literature, cooperative co-evolution (CC)-based FS approaches can be more suitable for cybersecurity data preprocessing considering the Big Data scenario. Accordingly, in this paper, we have applied our previously proposed CC-based FS with random feature grouping (CCFSRFG) to a benchmark cybersecurity dataset as the preprocessing step. The dataset with original features and the dataset with a reduced number of features were used for infrequent pattern detection. Experimental analysis was performed and evaluated using 10 unsupervised anomaly detection techniques. Therefore, the proposed infrequent pattern detection is termed Unsupervised Infrequent Pattern Detection (UIPD). Then, we compared the experimental results with and without FS in terms of true positive rate (TPR). Experimental analysis indicates that the highest rate of TPR improvement was by cluster-based local outlier factor (CBLOF) of the backdoor infrequent pattern detection, and it was 385.91% when using FS. Furthermore, the highest overall infrequent pattern detection TPR was improved by 61.47% for all infrequent patterns using clustering-based multivariate Gaussian outlier score (CMGOS) with FS.

A New Procedure of Clustering Based on Multivariate Outlier Detection

A New Procedure of Clustering Based on Multivariate Outlier Detection

Monitoring Robust Estimates for Compositional Data

In a number of recent articles Riani, Cerioli, Atkinson and others advocate the technique of monitoring robust estimates computed over a range of key parameter values. Through this approach the diagnostic tools of choice can be tuned in such a way that highly robust estimators which are as efficient as possible are obtained. This approach is applicable to various robust multivariate estimates like S- and MM-estimates, MVE and MCD as well as to the Forward Search in whichmonitoring is part of the robust method. Key tool for detection of multivariate outliers and for monitoring of robust estimates is the Mahalanobis distances and statistics related to these distances. However, the results obtained with thistool in case of compositional data might be unrealistic since compositional data contain relative rather than absolute information and need to be transformed to the usual Euclidean geometry before the standard statistical tools can be applied. Various data transformations of compositional data have been introduced in the literature and theoretical results on the equivalence of the additive, the centered, and the isometric logratio transformation in the context of outlier identification exist. To illustrate the problem of monitoring compositional data and to demonstrate the usefulness of monitoring in this case we start with a simple example and then analyze a real life data set presenting the technologicalstructure of manufactured exports. The analysis is conducted with the R package fsdaR, which makes the analytical and graphical tools provided in the MATLAB FSDA library available for R users.

Multivariate Outlier Removing for the Risk Prediction of Gas Leakage based Methane Gas

In this study, the relationship between natural gas (NG) data and gas-related environmental elements was performed using machine learning algorithms to predict the level of gas leakage risk without directly measuring gas leakage data. The study was based on open data provided by the server using the IoT-based remote control Picarro gas sensor specification. The naturel gas leaks into the air, it is a big problem for air pollution, environment and the health. The proposed method is multivariate outlier removing method based Random Forest (RF) classification for predicting risk of NG leak. After, unsupervised k-means clustering, the experimental dataset has done imbalanced data. Therefore, we focusing our proposed models can predict medium and high risk so best. In this case, we compared the receiver operating characteristic (ROC) curve, accuracy, area under the ROC curve (AUC), and mean standard error (MSE) for each classification model. As a result of our experiments, the evaluation measurements include accuracy, area under the ROC curve (AUC), and MSE; 99.71%, 99.57%, and 0.0016 for MOL_RF respectively.

Subtle mistakes in self-report surveys predict future transition to dementia.

IntroductionWe investigate whether indices of subtle reporting mistakes derived from responses in self‐report surveys are associated with dementia risk.MethodsWe examined 13,831 participants without dementia from the prospective, population‐based Health and Retirement Study (mean age 69 ± 10 years, 59% women). Participants’ response patterns in 21 questionnaires were analyzed to identify implausible responses (multivariate outliers), incompatible responses (Guttman errors), acquiescent responses, random errors, and the proportion of skipped questions. Subsequent incident dementia was determined over up to 10 years of follow‐up.ResultsDuring follow‐up, 2074 participants developed dementia and 3717 died. Each of the survey response indices was associated with future dementia risk controlling for confounders and accounting for death as a competing risk. Stronger associations were evident for participants who were younger and cognitively normal at baseline.DiscussionMistakes in the completion of self‐report surveys in longitudinal studies may be early indicators of dementia among middle‐aged and older adults.

Distinguishing type II focal cortical dysplasias from normal cortex: A novel normative modeling approach

ObjectiveFocal cortical dysplasias (FCDs) are a common cause of apparently non-lesional drug-resistant focal epilepsy. Visual detection of subtle FCDs on MRI is clinically important and often challenging. In this study, we implement a set of 3D local image filters adapted from computer vision applications to characterize the appearance of normal cortex surrounding the gray-white junction. We create a normative model to serve as the basis for a novel multivariate constrained outlier approach to automated FCD detection. MethodsStandardized MPRAGE, T2 and FLAIR MR images were obtained in 15 patients with radiologically or histologically diagnosed FCDs and 30 healthy volunteers. Multiscale 3D local image filters were computed for each MR contrast then sampled onto the gray-white junction surface. Using an iterative Gaussianization procedure, we created a normative model of cortical variability in healthy volunteers, allowing for identification of outlier regions and estimates of similarity in normal cortex and FCD lesions. We used a constrained outlier approach following local normalization to automatically detect FCD lesions based on projection onto the mean FCD feature vector. ResultsFCDs as well as some normal cortical regions such as primary sensorimotor and paralimbic regions appear as outliers. Regions such as the paralimbic regions and the anterior insula have similar features to FCDs. Our constrained outlier approach allows for automated FCD detection with 80% sensitivity and 70% specificity. SignificanceA normative model using multiscale local image filters can be used to describe the normal cortical variability. Although FCDs appear similar to some cortical regions such as the anterior insula and paralimbic cortices, they can be identified using a constrained outlier detection approach. Our method for detecting outliers and estimating similarity is generic and could be extended to identification of other types of lesions or atypical cortical areas.

Estimating the Efforts of Mobile Application Development in the Planning Phase Using Nonlinear Regression Analysis

Abstract The authors consider the construction of a nonlinear multiple regression model, its confidence and prediction intervals to evaluate the efforts of mobile application development in the planning phase based on the multivariate normalizing transformation and outlier detection. The constructed model is compared to the linear regression model and nonlinear regression models based on the univariate transformations, such as the decimal logarithm, Box–Cox, and Johnson transformation. This model, in comparison with other regression models, has better prediction accuracy.

Too bored for sports? Adaptive and less-adaptive latent personality profiles for exercise behavior

Physical exercise is an effective tool for improving public health, but the general population exercises too little. Drawing on recent theorizing on the combined role of boredom and self-control in guiding goal-directed behavior, we test the hypothesis that individual differences in boredom and self-control differentiate high from low exercisers. The role of boredom as a non-adaptive disposition is of particular interest, because research on boredom in sports is scarce. Here, we investigate the role of such individual differences in self-reported weekly exercise behavior (in minutes) in a sample of N = 507 participants (n = 200 female, Mage = 36.43 (±9.54)). We used the robust variant of Mahalanobis distance to detect and remove n = 51 multivariate outliers and then performed latent profile analysis to assess if boredom (boredom proneness; exercise-related boredom) and self-control (trait self-control; if-then planning) combine into identifiable latent profiles. In line with theoretical considerations, the Bayesian Information Criterion favored a solution with two latent profiles. One profile was characterized by higher-than-average exercise-related boredom and boredom proneness and lower-than-average self-control and if-then planning values. This pattern was reversed for the second profile. A one-sided Bayesian two-sample t-test supported the hypothesis that the first profile is associated with less exercise behavior than the second profile, BF = 16.93. Our results foster the notion of self-control and if-then planning as adaptive dispositions. More importantly, they point to an important role of boredom in the exercise setting: exercise-related boredom and getting easily bored in general are associated with less exercise activity. This is in line with recent theorizing on boredoms' and self-controls’ function in guiding goal-directed behavior.

Using Multivariate Outliers from Smartphone Sensor Data to Detect Physical Barriers While Walking in Urban Areas

Nowadays, our mobile devices have become smart computing platforms, incorporating a wide number of embedded sensors such as accelerometers, gyroscopes, barometers, GPS receivers, and magnetometers. Smartphones are valuable devices for gathering user-related data and transforming it into value-added information for the user. In this study, a novel mechanism to process sensor data from mobile devices in order to detect the type of area the user is crossing while walking in an urban setting is presented. The method is based on combining outlier data analysis and classification techniques from data collected by several pedestrians while traversing an urban environment. A theoretical framework, composed of methods for detecting multivariate outliers combined with supervised classification techniques, has been proposed in order to identify different situations and physical barriers while walking. Each type of element to be detected is characterized by using a feature vector computed based on the outliers detected. Finally, a radial SVM is used for the classification task. The classifier is trained in a supervised way with data from 20 different segments containing several physical barriers and used later to assign a class to new un-labelled data. The results obtained with this approach are very promising with an average accuracy around 95% when detecting different types of physical barriers.

Outliers in official statistics

The purpose of this manuscript is to provide a survey on the important methods addressing outliers while producing official statistics. Outliers are often unavoidable in survey statistics. They may reduce the information of survey datasets and distort estimation on each step of the survey statistics production process. This paper defines outliers to be focused on each production step and introduces practical methods to cope with them. The statistical production process is roughly divided into the following three steps. The first step is data cleaning, and outliers to be focused are that may contain mistakes to be corrected. Robust estimators of a mean vector and covariance matrix are introduced for the purpose. The next step is imputation. Among a variety of imputation methods, regression and ratio imputation are the subjects in this paper. Outliers to be focused on in this step are not erroneous but have extreme values that may distort parameter estimation. Robust estimators that are not affected by remaining outliers are introduced. The final step is estimation and formatting. We have to be careful about outliers that have extreme values with large design weights since they have a considerable influence on the final statistics products. Weight calibration methods controlling the influence are discussed based on the robust weights obtained in the previous imputation step. A few examples of practical application are also provided briefly, although multivariate outlier detection methods introduced in this paper are mostly in the research stage in the field of official statistics.

Machine learning-guided discovery and design of non-hemolytic peptides

Reducing hurdles to clinical trials without compromising the therapeutic promises of peptide candidates becomes an essential step in peptide-based drug design. Machine-learning models are cost-effective and time-saving strategies used to predict biological activities from primary sequences. Their limitations lie in the diversity of peptide sequences and biological information within these models. Additional outlier detection methods are needed to set the boundaries for reliable predictions; the applicability domain. Antimicrobial peptides (AMPs) constitute an extensive library of peptides offering promising avenues against antibiotic-resistant infections. Most AMPs present in clinical trials are administrated topically due to their hemolytic toxicity. Here we developed machine learning models and outlier detection methods that ensure robust predictions for the discovery of AMPs and the design of novel peptides with reduced hemolytic activity. Our best models, gradient boosting classifiers, predicted the hemolytic nature from any peptide sequence with 95–97% accuracy. Nearly 70% of AMPs were predicted as hemolytic peptides. Applying multivariate outlier detection models, we found that 273 AMPs (~ 9%) could not be predicted reliably. Our combined approach led to the discovery of 34 high-confidence non-hemolytic natural AMPs, the de novo design of 507 non-hemolytic peptides, and the guidelines for non-hemolytic peptide design.

An algorithm for outlier detection in a time series model using backpropagation neural network

Outliers are commonplace in many real-life experiments. The presence of even a few anomalous data can lead to model misspecification, biased parameter estimation, and poor forecasts. Outliers in a time series are usually generated by dynamic intervention models at unknown points of time. Therefore, detecting outliers is the cornerstone before implementing any statistical analysis. In this paper, a multivariate outlier detection algorithm is given to detect outliers in time series models. A univariate time series is transformed to bivariate data based on the estimate of robust lag. The proposed algorithm is designed by using robust measures of location and dispersion matrix. Feed forward neural network is used for designing time series models. Number of hidden units in the network is determined based on the standard error of the forecasting error. A comparison study between the proposed algorithm and the widely used algorithms is given based on three real-data sets. The results demonstrated that the proposed algorithm outperformed the existing algorithms due to its non-requirement of a priori knowledge of the time series and its control of both masking and swamping effects. We also discussed an efficient method to deal with unexpected jumps or drops on share prices due to stock split and commodity prices near contract expiry dates.

Multivariate Outliers: A Conceptual and Practical Overview for the Nurse and Health Researcher.

The presence of statistical outliers is a shared concern in research. If ignored or improperly handled, outliers have the potential to distort parameter estimates and possibly compromise the validity of research findings. The purpose of this paper is to provide a conceptual and practical overview of multivariate outliers with a focus on common techniques used to identify and manage multivariate outliers. Specifically, this paper discusses the use of Mahalanobis distance and residual statistics as common multivariate outlier identification techniques. It also discusses the use of leverage and Cook's distance as two common techniques to determine the influence that multivariate outliers may have on statistical models. Finally, this paper discusses techniques that are commonly used to handle influential multivariate outlier cases.

Multivariate Monitoring Workflow for Formulation, Fill and Finish Processes.

Process monitoring is a critical task in ensuring the consistent quality of the final drug product in biopharmaceutical formulation, fill, and finish (FFF) processes. Data generated during FFF monitoring includes multiple time series and high-dimensional data, which is typically investigated in a limited way and rarely examined with multivariate data analysis (MVDA) tools to optimally distinguish between normal and abnormal observations. Data alignment, data cleaning and correct feature extraction of time series of various FFF sources are resource-intensive tasks, but nonetheless they are crucial for further data analysis. Furthermore, most commercial statistical software programs offer only nonrobust MVDA, rendering the identification of multivariate outliers error-prone. To solve this issue, we aimed to develop a novel, automated, multivariate process monitoring workflow for FFF processes, which is able to robustly identify root causes in process-relevant FFF features. We demonstrate the successful implementation of algorithms capable of data alignment and cleaning of time-series data from various FFF data sources, followed by the interconnection of the time-series data with process-relevant phase settings, thus enabling the seamless extraction of process-relevant features. This workflow allows the introduction of efficient, high-dimensional monitoring in FFF for a daily work-routine as well as for continued process verification (CPV).

A classification technique for local multivariate clusters and outliers of spatial association

AbstractThe detection of spatial clusters and outliers is critical to a number of spatial data analysis techniques. Many techniques embed spatial clustering components with the aim of exploring spatial variability and patterns in a data set, caused by the spatial association that generally affects most spatial data. A frontier challenge in spatial data analysis is to extend techniques—originally designed for univariate analysis—to a multivariate context, in order to be able to cope with the increasing complexity and variety of modern spatial data. This article proposes an exploratory procedure to detect and classify clusters and outliers in a multivariate spatial data set. Cluster and outlier detection relies on recently introduced multivariate extensions of the well‐established local indicators of spatial association statistics. Two new indicators are proposed enabling the classification of multivariate clusters and outliers, not directly achievable with any already established technique. The procedure is fully implemented using free and open source geospatial software and libraries. The raw source code is made available for future reviews and replications. Empirical results from early applications on both synthetic and real spatial data are discussed. Advantages and limitations of the introduced procedure are outlined according to the empirical results.

Complete multivariate flood frequency analysis, applied to northern Algeria

AbstractExtreme hydrologic events are commonly described by several dependent characteristics, such as duration, volume and peak flow for floods. Traditionally in Algeria and North Africa, flood frequency analysis (FFA) is conducted as a univariate approach focusing separately on each single of flood characteristics. On the other hand, elsewhere, multivariate FFA studies have been conducted focusing on some FFA steps (especially modelling). The current study aims to consider complete multivariate FFA at‐site case studies in northern Algeria using 11 hydrometric stations. It is also among the first studies dealing with multivariate FFA in a complete way by considering all the required steps of the analysis (multivariate outliers detection, multivariate assumptions testing and copula fitting) and on datasets from Algeria. Multivariate stationarity, homogeneity and independence assumptions have been well verified before modelling. The Weibull distribution is mostly selected as margin distribution for the duration, volume and peak flow series. Frank, Clayton and Gumbel copulas are commonly selected to describe the dependence structure on the three flood pairs of variables. These findings should be interesting in water management and flood risk assessment in these regions. Combining these flood characteristics enables the design of more efficient hydraulic structures.