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Humans use pictures to model the world. The structure of a picture maps to mind space to form a concept. When an internal structure matches the corresponding external structure, an observation functions. Whether effective or not, the observation is self-consistent. In epistemology, people often differ from each other in terms of whether a concept is probabilistic or certain. Based on the effect of the presented IG and pull anti algorithm, we attempt to provide a comprehensive answer to this problem. Using the characters of hidden structures, we explain the difference between the macro and micro levels and the same difference between semantics and probability. In addition, the importance of attention is highlighted through the combination of symmetry and asymmetry included and the mechanism of chaos and collapse revealed in the presented model. Because the subject is involved in the expression of the object, representationalism is not complete. However, people undoubtedly reach a consensus based on the objectivity of the representation. Finally, we suggest that emotions could be used to regulate cognition. [ABSTRACT FROM AUTHOR]

Regression diagnostics help identify influential data points in a model. Detecting outliers in complex survey design data involving stratification, clustering, and unequal probability sampling is difficult due to the presence of masking, where one outlier makes it hard to detect others. The masking factor for survey–weighted linear regression is developed and applied to analyzing the Household Consumer Expenditure dataset of 68th round of the National Sample Survey Organization survey of India. Regression parameters are calculated before and after detection and removal of outliers. The standard error of regression parameters for survey-weighted least squares models is reduced by 2% for the intercept, 5% for variable “meat” (X5), 4% for “served processed food” (X9), and 4% for “packaged processed food” (X10). Inference alters the significance of regression coefficients of the variable “served processed food” (X9) leading to the emergence of significance. There is no change in inference for other variables. [ABSTRACT FROM AUTHOR]

Islands provide a great system to explore the processes that maintain genetic diversity and promote local adaptation. We explored the genomic diversity of the Balearic lizard Podarcis lilfordi, an endemic species characterized by numerous small insular populations with large phenotypic diversity. Using the newly available genome for this species, we characterized more than 300,000 SNPs, merging genotyping‐by‐sequencing (GBS) data with previously published restriction site‐associated DNA sequencing (RAD‐Seq) data, providing a dataset of 16 island populations (191 individuals) across the range of species distribution (Menorca, Mallorca, and Cabrera). Results indicate that each islet hosts a well‐differentiated population (FST = 0.247 ± 0.09), with no recent immigration/translocation events. Contrary to expectations, most populations harbor a considerable genetic diversity (mean nucleotide diversity, Pi = 0.144 ± 0.021), characterized by overall low inbreeding values (FIS < 0.1). While the genetic diversity significantly decreased with decreasing islet surface, maintenance of substantial genetic diversity even in tiny islets suggests variable selection or other mechanisms that buffer genetic drift. Maximum‐likelihood tree based on concatenated SNP data confirmed the existence of the two major independent lineages of Menorca and Mallorca/Cabrera. Multiple lines of evidence, including admixture and root testing, robustly placed the origin of the species in the Mallorca Island, rather than in Menorca. Outlier analysis mainly retrieved a strong signature of genome differentiation between the two major archipelagos, especially in the sexual chromosome Z. A set of proteins were target of multiple outliers and primarily associated with binding and catalytic activity, providing interesting candidates for future selection studies. This study provides the framework to explore crucial aspects of the genetic basis of phenotypic divergence and insular adaptation. [ABSTRACT FROM AUTHOR]

Meta‐analysis is a widely used tool for synthesizing results from multiple studies. The collected studies are deemed heterogeneous when they do not share a common underlying effect size; thus, the factors attributable to the heterogeneity need to be carefully considered. A critical problem in meta‐analyses and systematic reviews is that outlying studies are frequently included, which can lead to invalid conclusions and affect the robustness of decision‐making. Outliers may be caused by several factors such as study selection criteria, low study quality, small‐study effects, and so on. Although outlier detection is well‐studied in the statistical community, limited attention has been paid to meta‐analysis. The conventional outlier detection method in meta‐analysis is based on a leave‐one‐study‐out procedure. However, when calculating a potentially outlying study's deviation, other outliers could substantially impact its result. This article proposes an iterative method to detect potential outliers, which reduces such an impact that could confound the detection. Furthermore, we adopt bagging to provide valid inference for sensitivity analyses of excluding outliers. Based on simulation studies, the proposed iterative method yields smaller bias and heterogeneity after performing a sensitivity analysis to remove the identified outliers. It also provides higher accuracy on outlier detection. Two case studies are used to illustrate the proposed method's real‐world performance. [ABSTRACT FROM AUTHOR]

In this work, the mixed multivariate T2 control chart's detailed performance evaluation based on PCA mix is explored. The control limit of the proposed control chart is calculated using the kernel density approach. Through simulation studies, the proposed chart's performance is assessed in terms of its capacity to identify outliers and process shifts. When 30% more outliers are included in the data, the proposed chart provides a consistent accuracy rate for identifying mixed outliers. For the balanced percentage of attribute qualities, misdetection happens because of the high false alarm rate. For unbalanced attribute qualities and excessive proportions, the masking effect is the key issue. The proposed chart shows the improved performance for the shift in identifying the shift in the process. [ABSTRACT FROM AUTHOR]

An outlier in the Internet of Things is an immediate change in data induced by a significant difference in the atmosphere (Event) or sensor malfunction (Error). Outliers in the data cause the decision-maker to make incorrect judgments about data analysis. Hence it is essential to detect outliers in any discipline. The detection of outliers becomes the most crucial task to improve sensor data quality and ensure accuracy, reliability and robustness. In this research, a novel outlier detection technique based on spatial, temporal correlations and attribute correlations is proposed to detect outliers (both Errors and Events). This research uses a correlation measure in the temporal correlation algorithm to determine outliers and the spatial correlation algorithm to classify the outliers, whether the outliers are events or errors. This research uses optimal clusters to improve network lifetime, and malicious nodes were also detected based on spatial–temporal correlations and attribute correlations in these clusters. The experimental results proved that the proposed method in this research outperforms some other models in terms of accuracy against the percentage of outliers infused and detection rate against the false alarm rate. [ABSTRACT FROM AUTHOR]

Normality, in the colloquial sense, has historically been considered an aspirational trait, synonymous with ideality. The arithmetic average and, by extension, statistics including linear regression coefficients, have often been used to characterize normality, and are often used as a way to summarize samples and identify outliers. We provide intuition behind the behavior of such statistics in high dimensions, and demonstrate that even for datasets with a relatively low number of dimensions, data start to exhibit a number of peculiarities which become severe as the number of dimensions increases. Whilst our main goal is to familiarize researchers with these peculiarities, we also show that normality can be better characterized with 'typicality', an information theoretic concept relating to entropy. An application of typicality to both synthetic and real-world data concerning political values reveals that in multi-dimensional space, to be 'normal' is actually to be atypical. We briefly explore the ramifications for outlier detection, demonstrating how typicality, in contrast with the popular Mahalanobis distance, represents a viable method for outlier detection. [ABSTRACT FROM AUTHOR]

Carbon fiber reinforced composites (CFRP) are susceptible to hidden damage from low velocity external impacts during their service life. To ensure the proper monitoring of the state of the composites, it is crucial to predict the location of an impact event. In this paper, fiber Bragg grating (FBG) sensors are affixed to the surface of a carbon fiber composite tube, and an optical sensing interrogator is used to capture the central wavelength shift of the FBG sensors due to low-velocity impacts. A discrete wavelet transform is used for noise reduction in the response signals. Then, the differences in the captured response signals of the FBG sensors at different locations of the impact were analyzed. Moreover, two methods were implemented to predict the location of low-velocity impacts, according to the differences in the captured response signals. The BP neural network-based method utilized three data sets to train the neural network, resulting in an average localization error of 20.68 mm. In contrast, the method based on error outliers selected a specific data set as the reference dataset, achieving an average localization error of 13.98 mm. The comparison of the predicted results shows that the latter approach has a higher predictive accuracy and does not require a significant amount of data. [ABSTRACT FROM AUTHOR]

Time is an extremely important element in the field of GNSS positioning. In precise positioning with a single-centimetre accuracy, satellite clock corrections are used. In this article, the longest available data set of satellite clock corrections of four GNSS systems from 2014 to 2021 was analysed. This study covers the determination of the quality (outliers number and magnitude), availability, stability, and determination of the specificity and nature of the clock correction for each satellite system. One problem with the two newest satellite systems (Galileo and BeiDou) is the lack of availability of satellite signals in the early years of the analysis. These data were available only in the later years of the period covered by the analysis, as most of the satellites have only been in orbit since 2018–2019. Interestingly, the percentage of outlying observations was highest in Galileo and lowest in BeiDou. Phase and frequency plots showed a significant number of outlying observations. On the other hand, after eliminating outlying observations, each system showed a characteristic graph waveform. The most consistent and stable satellite clock corrections are provided by the GPS and GLONASS systems. The main problems discussed in this paper are the determination of the number and magnitude of outliers in clock products of four GNSS systems (GPS, GLONASS, Galileo, Beidou) and the study on the long-term stability of GNSS clocks analysis, which covers the years 2014–2021. [ABSTRACT FROM AUTHOR]

Ordinary Least Squares Estimator (OLSE) is widely used to estimate parameters in regression analysis. In practice, the assumptions of regression analysis are often not met. The most common problems that break these assumptions are outliers and multicollinearity problems. As a result of these problems, OLSE loses efficiency. Therefore, alternative estimators to OLSE have been proposed to solve these problems. Robust estimators are often used to solve the outlier problem, and biased estimators are often used to solve the multicollinearity problem. These problems do not always occur individually in the real‐world dataset. Therefore, robust biased estimators are proposed for simultaneous solutions to these problems. The aim of this study is to propose Liu‐type Generalized M Estimator as an alternative to the robust biased estimators available in the literature to obtain more efficient results. This estimator gives effective results in the case of outlier and multicollinearity in both dependent and independent variables. The proposed estimator is theoretically compared with other estimators available in the literature. In addition, Monte Carlo simulation and real dataset example are performed to compare the performance of the estimator with existing estimators. [ABSTRACT FROM AUTHOR]

Determining the predictor variables that have a non-linear effect as well as those that have a linear effect on the response variable is crucial in additive semi-parametric models. This issue has been extensively investigated by many researchers in the area of semi-parametric linear additive models, and various separation methods are proposed by the authors. A popular issue that might affect both estimation and separation results is the existence of outliers among the observations. In order to address this lack of sensitivity towards extreme observations, robust estimating approaches are frequently applied. We propose a robust method for simultaneously identifying the linear and nonlinear components of a semi-parametric linear additive model, even in the presence of outliers in the observations. Additionally, this model is sparse in that it may be used to determine which explanatory variables are ineffective by giving accurate zero estimates for their coefficients. To assess the effectiveness of the proposed method, a comprehensive Monte Carlo simulation study is conducted along with an application to investigate the dataset, which includes Boston property prices dataset. [ABSTRACT FROM AUTHOR]

An anonymous survey was given to n = 73 students in an asynchronous online statistical literacy course at a mid-sized Hispanic Serving Institution. Informed by teaching experience, literature on lexical ambiguity, and everyday usage of statistics words and phrases, the first author designed the survey to yield insight into how students view phrases such as "average person," "random person," and "outlier person," and to explore possible connections or patterns with such phrases. Findings suggest that students view phrases such as "random person" in a way much further from standard usage than they do phrases such as "anonymous person." Considerations such as diversity, agency, and variable identification are identified as possibly affecting usage and meriting further investigation. Recommendations for teaching and future research are provided. Supplementary materials for this article are available online. [ABSTRACT FROM AUTHOR]

Ensuring a secure network environment is crucial, especially with the increasing number of threats and attacks on digital systems. Implementing effective security measures, such as anomaly detection can help detect any abnormal traffic patterns. Several statistical and machine learning approaches are used to detect network anomalies including robust statistical methods. Robust methods can help identify abnormal traffic patterns and distinguish them from normal traffic accurately. In this study, a robust Principal Component Analysis (PCA) method called ROBPCA which is known for its extensive use in the literature of chemometrics and genetics is utilized for detecting network anomalies and compared with another robust PCA method called PCAGRID. The anomaly detection performances of these methods are evaluated by injecting synthetic traffic volume into a well-known traffic matrix. According to the application results, when the normal subspace is contaminated with large anomalies the ROBPCA method provides much better performance in detecting anomalies. [ABSTRACT FROM AUTHOR]

With an increased volume of spatial data, conventional spatial prediction methods have encountered significant challenges in handling complex data structures while maintaining statistical and computational efficiency. Recently, a spatial neural network method called DeepKriging has been proposed, which utilizes a set of basis functions as an embedded input to capture spatial information. In this study, we enhance this approach by using an ordered set of multi‐resolution thin plate spline basis functions, which offers ease of implementation and alleviates the challenges associated with basis function allocation, particularly when the data locations are highly irregular. The proposed method requires only the selection of the number of basis functions based on a validation dataset. In addition, we propose a robust version of DeepKriging, which is resistant to outliers. Several simulation experiments are conducted to show the advantages of our method over conventional statistical methods and the original DeepKriging approach. Finally, we apply the proposed method to a PM2.5 dataset in Taiwan. [ABSTRACT FROM AUTHOR]

Magnesite is an ore used in the production of a wide variety of industrial minerals and compounds and magnesium metal, as well as its alloys. The main components of magnesite are MgO and CO2. However, magnesite, which is not generally observed in nature as pure, contains certain amounts of SiO2, CaO, and Fe2O3. The loss on ignition (LOI) value in magnesite minerals largely depends on the CO2 content. This study aims to develop a model that estimates the LOI values of magnesite minerals by using SiO2, MgO, CaO, and Fe2O3tot data obtained from the geochemical analysis. Measurement of the LOI can provide important information not only for calculating the amount of volatiles in magnesite, but also about the acquisition of the element magnesium. With the estimation of LOI values, samples will not need to be subjected to LOI analysis. The data used in this study were compiled from the literature and information about the study areas, deposit types, analysis methods and the laboratory/device names was presented as supplementary material. The multivariate linear regression model was applied to represent the relationship between the LOI and these major oxides. A global independent data set comprising 170 observations was used to validate the proposed model. The preliminary analysis of the data is discussed in detail to improve the quality of the analysis. In the first step, the OLS estimation method is implemented to estimate the unknown parameters. Then, model assumptions are tested. Due to the existence of outliers, violation of the assumptions can lead to the misuse of the OLS method. In such cases, obtaining reliable estimates depends on strong estimators such as robust estimators, which are resistant to the outliers. Robust M-estimation methods can be used as effective tools for this purpose. For this reason, in the final step, we consider a robust M-estimation method based on Huber, Tukey, and Hampel objective functions. The results of OLS and robust regression methods, including parameter estimates, standard errors, residual standard error, and weighted R2 are presented as a comparison. The validity of the models and the importance of each explanatory variable to the relationship are also investigated. According to the residual standard error (RSE) which is a way to measure the standard deviation of the residuals in a regression model, and R W 2 values, a robust M- estimation method based on all three considered objective functions produces more accurate results in comparison with the outcomes of the conventional OLS method. In particular, the robust estimation method based on the Tukey objective function has the smallest RSE and largest R W 2 among the others. It is observed that deleting a few outlying points has a big impact on the regression results. In this study, a multi-linear relationship between the main oxide values and LOI was determined. As a result, an estimation model with 91% accuracy and an RSE value of 0.283 was proposed. In addition, some relationships were established between the outliers and the determination of the ranges of major oxide values in the magnesite mineral. Our results suggest that robust M-estimation can provide efficient and stable estimates when analyzing geochemical data that may contain outliers. The application of the multivariate regression analysis has been confirmed to estimate LOI by using SiO2, MgO, CaO, and Fe2O3tot as a new approach. This additional field investigation has shown promising results. Also, we recommend that this study can be improved by using more data and considering different magnesite deposit types. [ABSTRACT FROM AUTHOR]

Nonlinear models are commonly used for analyzing real-life data such as in medicine, engineering, and economics. To make efficient inferences about model parameter estimations and statistical results in nonlinear regression, assumptions related to error term are needed to be satisfied. Ordinary least squares and some modified least squares methods fail to give efficient parameter estimates when there are the problems of autocorrelation and outlier together in nonlinear regression. In this study, a novel hybrid robust tapering approach called as robust modified two-stage least squares is proposed to overcome the problems for obtaining more efficient parameter estimates in nonlinear regression. Two numerical examples and a comprehensive Monte–Carlo simulation study are given in order to examine the performance of robust modified two-stage least squares. [ABSTRACT FROM AUTHOR]

With the advent of the Global Navigation Satellite System (GNSS), the need for precise and highly accurate orbit and clock products becomes crucial in processing GNSS data. Clocks in GNSS observations form the basis of positioning. Their high quality and stability enable high accuracy and the reliability of the obtained results. The clock modelling algorithms are continuously improved; thus, the accuracy of the clock products is evolving. At present, 8 Analysis Centers (ACs) contribute to the International GNSS Service final clock products. These products are based on GNSS observations on a network of reference stations, where for a given day one of the reference station clocks is the reference clock. In this paper, the authors determined the impact of the reference clock on the quality of clock product, especially outliers, for the first time. For this purpose, the multi-GNSS final clock products provided by the Center for Orbit Determination in Europe (CODE) for the period 2014–2021 (1773–2190 GPS week, 2921 days) were analysed. Analysis shows that by applying the Median Absolute Deviation (MAD) algorithm for outlier detection, the Passive Hydrogen Maser (PHM) clock installed on board the GALILEO satellites have the lowest level of noise, whereas the Block IIR GPS satellite launched in 1999 appears to have the highest levels of noise. Furthermore, the GNSS station OHIE3, when used as a reference clock, generates an increase in the level of noise, especially noticeable on the G09 and E03 satellites. [ABSTRACT FROM AUTHOR]

The ridge regression estimator (RRE) is a widely used estimation method for the multiple linear regression model when the explanatory variables are correlated. The situation becomes problematic for the RRE when the data set contains outliers in the y-direction. The use of the RRE in the presence of outliers may have some adverse effects on parameter estimates. To address this issue, the robust ridge estimators based on M-estimator are available in the literature which are less sensitive to the presence of outliers. It is a well-known fact that the selection of ridge parameter k is very crucial while using the RRE and the same phenomenon may happen in the case of robust ridge estimators. This study proposes some robust ridge estimators for the ridge parameter k. The performance of proposed estimators is evaluated with the help of the Monte Carlo simulations and a real application where the mean squared error (MSE) is considered as a performance evaluation criterion. Results show a better performance of the proposed robust ridge estimators as compared to the RRE, least square and M-estimation methods. While for modified ridge M-estimator, different ridge parameters found to be better for different conditions. [ABSTRACT FROM AUTHOR]

We investigate a shift parameter approach to logistic regression for robust classification. Shift parameter moves margin to the minimum of loss function. For robust estimation, margin-based logistic regression requires its own version of thresholding-type estimate which is different from residual-based regression. We discuss shift parameter estimation desirable to robust classification and propose some penalty functions producing such shift parameter estimates. Comparing to existing robust logistic regression methods requiring non-convex optimization or label transition modelling, our proposal is implemented in a simple alternating optimization: the classifier is obtained as a solution of conventional logistic regression with an offset and shift parameter is individually estimated in a closed form. We discuss some robust properties of the method and demonstrate its performance in linear and nonlinear classification with synthetic and real-world examples. [ABSTRACT FROM AUTHOR]

This paper considered the problem of outlier detection in a repeated measure design (RMD) using the Estimate distance and the Liu and Weng's residual methods. The comparative performance of the two methods in identifying outlier in a RMD was evaluated through empirical and simulation studies. For the empirical study, two types of outliers (outliers I and II) were randomly introduced into the real‐life dataset from physiological research at a time. The results obtained revealed that the Liu and Weng's Residual and Estimate Distance tests correctly detected the outlier I randomly introduced into all the subjects at a time. However, the Liu and Weng's Residual test was only able to correctly detect the outlier II in subjects 1, 4 and 6 while the Estimate distance test could not detect the outlier II in any of the subject. In the simulation study, random samples of size 105, 120, 135, and 150 with a corresponding number of subjects (k = 7, 8, 9, 10), respectively, were generated from a multivariate normal distribution. Two types of outliers (outliers I and II) were randomly introduced into the simulated datasets. The results of the simulation study indicated that the Liu and Weng's residual and Estimate distance methods correctly detected the outlier I randomly introduced into the simulated data for all the sample sizes considered. However, the Liu and Weng's Residual test outperformed the Estimate distance test in detecting the outlier II. Thus, the Liu and Weng's Residual proved to be more powerful than the Estimate Distance test in identifying outlier in a RMD. [ABSTRACT FROM AUTHOR]

There is a wide range of outliers in spatial data, and these potential outliers will have a great impact on parameter estimation and corresponding statistical inference. Relying on the framework of maximum likelihood estimation (MLE), we investigate the asymptotic distribution of robust ML estimator under the mixed spatial autoregressive models with outliers and compare it with that of the ML estimator. Furthermore, based on the asymptotic theoretical result, we conduct the confidence interval of robust MLE and MLE. Similar to the results of MLE, we construct the second-order-corrected robust confidence interval using the parametric and semi-parametric bootstrap method. Simulation studies using Monte Carlo show that the robust estimator with the Huber loss function is more accurate and outperforms the MLE in most sample settings when data is contaminated by outliers. Then the use of the method is demonstrated in the analysis of the Neighborhood Crimes Data and the Boston Housing Price Data. The results further support the eligibility of the robust method in practical situations. [ABSTRACT FROM AUTHOR]

In this study, it is predicted that there may be a certain order in the consumption of an important need such as drinking water by the household, as well as irregular consumption depending on different factors. Increasing population, limited drinking water resources, developing infrastructure and technology have increased the demand for drinking and utility water. There is a search for alternative water sources to meet this demand, but it is foreseen that these demands can be met by not wasting existing water and using it more efficiently. By using machine learning (ML) methods, which is a sub-branch of artificial intelligence (AI), drinking water consumption data in the past periods were analyzed, and ordinary and unusual consumption behavior models were extracted. It is envisaged that by detecting abnormal consumptions that may occur in drinking water consumption and informing the subscribers about this issue, it will be ensured that the consumption in the household remains within the normal consumption range. Although the amount of data collected, recorded and processed in today's IT world has increased significantly, it is known that the exact analysis is difficult in terms of time and cost. In this study, subscriber, meter, consumption, bill and payment data of 8,224 residential subscribers, whose water meter index reading is more than 160 periods throughout the province of Kayseri, between 2006 and 2022 (first 6 months) were taken into account. The data are combined on a spatial subscriber basis and a 41-features dataset is obtained. The dataset was transformed into a dataset with 24 features as a result of data preprocessing. In the study, 6 sub-datasets were obtained by using information gain (IG), gain ratio (GR), symmetric uncertainty coefficient (SU), pearson correlation coefficient (r), f-score and random forest (RF) feature selection methods. The 7th sub-dataset was obtained from the intersections of the selected features in the sub-datasets. In all datasets, abnormal and normal drinking water consumptions were determined by using 7 different ML anomaly analysis methods: tukey outlier labeling (TOL), forest of isolation (IF), z-score, copula-based outlier detection (COPOD), median absolute deviation (MAD), local outlier factor (LOF), and elliptical envelope (EE). At the beginning of the study were unsupervised drinking water consumption data at the end of the study, labeled as 4 different classes and the dataset was made supervised. Using the finally obtained supervised dataset, decision trees (DT), gaussian naive bayes (NB), k-nearest neighbors (KNN), logistic regression (LJR), multilayer perceptron neural network (MLP-NN), RF and gradient boosting (GB) have been developed consumption class estimation models with 7 different ML methods. As a result of the study, it has been proven that abnormal drinking water consumption can be detected by ML methods, and it has been revealed that necessary policies can be created for more efficient use of water without wasting water and measures can be taken for this. [ABSTRACT FROM AUTHOR]

This paper addresses the issue of the prediction of slope stability with machine learning (ML) applications. Five well-known and popular ML algorithms, namely neural network (NNet), decision tree (DT), support vector machine (SVM), k-nearest neighbor (kNN), and random forest (RF), are used to demonstrate the effectiveness of the ML algorithms for predicting binary classification of slope stability based on a case history dataset containing outliers. This study also evaluates the winsorization method used to treat outliers in the dataset by outlining the effect of outliers on the prediction performances of models. To this end, the performance of all the generated ML models is assessed and compared both for unwinsorized (e.g., raw) and winsorized datasets based on performance metrics (i.e., Recall, Precision, Accuracy, and F1-Score) obtained from the confusion matrix. The experimental outputs showed that the application of winsorization enhanced the prediction performance of the models, and thus, all ML models built with winsorized datasets outperformed the unwinsorized ones. In this paper, the RF model achieves the best prediction performance, especially in the case of the winsorized dataset used. Moreover, it is found that SVM is the most sensitive algorithm to outliers as against the other ML algorithms, while the kNN algorithm is the least among the applied algorithms. Results showed that the increment percentage of accuracy nearly reaches 20% for the SVM model and the following 18% for DT, 11% for NNet, 10% for RF, and 4% for kNN, respectively. Furthermore, the results of the study reveal not only the performance of ML algorithms for the slope stability problem but also show how the handling of outliers of a dataset affects the models' prediction performance. [ABSTRACT FROM AUTHOR]

In mixed methods approaches, statistical models are used to identify "nested" cases for intensive, small- n investigation for a range of purposes, including notably the examination of causal mechanisms. This article shows that under a commonsense interpretation of causal effects, large- n models allow no reliable conclusions about effect sizes in individual cases—even if we choose "onlier" cases as is usually suggested. Contrary to established practice, we show that choosing "reinforcing" outlier cases—where outcomes are stronger than predicted in the statistical model—is appropriate for testing preexisting hypotheses on causal mechanisms, as this reduces the risk of false negatives. When investigating mechanisms inductively, researchers face a choice between "onlier" and reinforcing outlier cases that represents a trade-off between false negatives and false positives. We demonstrate that the inferential power of nested research designs can be much increased through paired comparisons of cases. More generally, this article provides a new conceptual framework for understanding the limits to and conditions for causal generalization from case studies. [ABSTRACT FROM AUTHOR]

The assumption of normality has a crucial importance in many statistical procedures. Therefore, a number of normality tests are proposed. Also, many investigations are conducted on the performance of these normality tests under a set of alternative distribution. However, there are few studies to compare the performance of the commonly used normality tests in the presence of outliers, but they are not comprehensive. It is important, since the outliers may increase the variability in the data set, they cause the decrease in the statistical power. In this study we show the performance of the commonly used normality tests in the presence of outlier in the data set. An outlier generation method is implied to generate uniform tails and the effects of various magnitude of outliers on the normality tests are obtained in terms of penalized power and Type I error probability. As a result, the most powerful tests are suggested to the researchers for different magnitude of outliers, sample sizes and the contamination ratios. Two real data applications are given to illustrate the importance of choosing the appropriate test. [ABSTRACT FROM AUTHOR]

We present our automated real-time socket inspection system capable of detecting an assortment of defects including metallic and liquid staining, loose capacitors and pins, and other debris and foreign material (FM). Our test tools pick and place manufactured units into sockets for electrical testing. Any debris accumulated inside the test sockets will likely damage subsequent units until the defective socket is replaced. To quickly capture in-situ defects and mitigate further damage, we equipped each pick-and-place arm with a new vision system designed to fit within the existing tool. The tight footprint constraints required a highly compact imaging system which resulted in a variety of image artifacts, creating several unique challenges. Our inspection algorithm utilizes a variety of advanced computer vision and machine learning techniques to normalize images, extract and match features, register the images, suppress unwanted artifacts, and detect defects. The detected changes are then sent to a deep learning classifier to further filter between true defects and natural socket deterioration. The flagged socket images can be manually dispositioned by the user and the socket can be sent for repair or cleaning as needed. [ABSTRACT FROM AUTHOR]

The Almon technique is a widely used estimation procedure for the distributed lag model (DLM) to encounter the problems associated with direct application of the ordinary least squares method to this model. The Almon estimator may be sensitive to outliers in the y-direction. This study is aimed to propose a robust estimator for parameter vector of the DLM when data set is contaminated with outliers. Moreover, this study proposes the robust t-tests and confidence intervals for the lag coefficients. Performance of our proposed estimator is evaluated through the Monte Carlo simulations by comparing the estimated mean squared error while the performance of t-tests and confidence intervals is evaluated using the null rejection rates and coverage, respectively. The simulation results reveal an attractive performance of the proposed methods in the presence of outliers in the y-direction. [ABSTRACT FROM AUTHOR]

Day-boundary discontinuity (DBD) is an effect present in precise GNSS satellite orbit and clock products originating from the method used for orbit and clock determination. The non-Gaussian measurement noise and data processing in 24 h batches are responsible for DBDs. In the case of the clock product, DBD is a time jump in the boundary epochs of two adjacent batches of processed data and its magnitude might reach a couple of ns. This article presents the four GNSS (Global Navigation Satellite System) systems DBD analysis in terms of change over an 8 year period. For each of 118 satellites available in this period, the yearly value of DBD was subject to analysis including standard deviation and frequency of outliers. Results show that the smallest DBDs appear in the GPS system, the biggest - for the BeiDou space segment. Moreover, the phenomenon of changes in DBDs over time is clearly seen at the beginning of the analysed period when the magnitude and number of the DBDs were larger than for current, newest clock products. [ABSTRACT FROM AUTHOR]

Online monitoring of laser welding depth is increasingly important, with the growing demand for the precise welding depth in the field of power battery manufacturing for new energy vehicles. The indirect methods of welding depth measurement based on optical radiation, visual image and acoustic signals in the process zone have low accuracy in the continuous monitoring. Optical coherence tomography (OCT) provides a direct welding depth measurement during laser welding and shows high achievable accuracy in continuous monitoring. Statistical evaluation approach accurately extracts the welding depth from OCT data but suffers from complexity in noise removal. In this paper, an efficient method coupled DBSCAN (Density-Based Spatial Clustering of Application with Noise) and percentile filter for laser welding depth determination was proposed. The noise of the OCT data were viewed as outliers and detected by DBSCAN. After eliminating the noise, the percentile filter was used to extract the welding depth. By comparing the welding depth determined by this approach and the actual weld depth of longitudinal cross section, an average error of less than 5% was obtained. The precise laser welding depth can be efficiently achieved by the method. [ABSTRACT FROM AUTHOR]

In response time (RT) research, RTs which largely deviate from the RT distribution are considered "outliers". Outliers are typically excluded from RT analysis building upon the implicit assumption that cognitive processing is distorted in outlier trials. The present study aims to test this assumption by comparing cognitive processing indexed by event‐related potentials (ERP) of trials with outliers and valid trials in two different tasks. To this end, we compared stimulus‐ and response‐locked ERPs for outliers identified by nine different methods with valid trials, using cluster‐based permutation tests. Consistently across outlier exclusion methods and tasks, the late positive complex (P3) associated with response‐related processes was reduced in outliers. Analyses of response‐locked ERPs related this P3 attenuation to a slower and temporally more extended increase of the P3, possibly indexing reduced evidence accumulation speed in outliers. P3 peak amplitude in response‐locked ERPs was similar between outliers and valid trials, suggesting that the absolute amount of evidence required for a response remained comparable. Furthermore, in addition to these more general ERP correlates of outliers, the contingent negative variation (CNV) ERP component was reduced in outliers as a function of preparatory demands of the task. Hence, electrophysiological correlates, and thus cognitive processing, are altered in outliers compared to valid trials. In order to avoid distortion of observed ERP differences between conditions, the RT outlier distribution should be considered for the analysis of ERPs in combined ERP and RT studies. Many studies in experimental psychology incorporate the assessment of response times (RT). For statistical analysis, RT outliers are typically excluded, as they are implicitly assumed to not represent the cognitive processes intended to be studied. The present study aims to test this assumption by comparing event‐related potentials (ERP) between outliers and valid trials. The results showed that ERPs differ between outlier and valid trials, supporting accounts assuming a distorted processing in outliers. [ABSTRACT FROM AUTHOR]

Purpose: Conventional instruments for total knee arthroplasty (TKA) have limited accuracy. The occurrence of outliers can negatively influence the clinical outcome and long-term survival of the implant. Orthopaedic robotic systems were developed to increase the accuracy of implant positioning and bone resections. Several systems requiring preoperative imaging have shown a higher degree of precision compared to conventional instrumentation. An imageless system needs less preoperative time and preparation and is more cost effective. Aim of this study was to find out whether this system is as precise, reproduces accurately the surgeon's planning and reduces the occurrence of outliers. Methods: This retrospective study included the first 71 robotic-assisted TKA and 308 conventional TKA in 374 patients. Intraoperatively planned and actual bone resections were compared. Postoperative alignment, measured on full leg weight bearing radiographs, was related to the respective planning and statistically compared between the groups. Results: Baseline characteristics (age, BMI, ASA, preoperative Knee Society Score and deformity) between both groups were comparable. According to the planned alignment, the postoperative mean difference was – 1.01° in the robotic versus 2.05° in the conventional group. The maximum deviation was − 2/+ 2.5° in the robotic and – 6.6/ + 6.8° in the conventional group. According to the plan, there were no outliers above ± 3° in the robotic versus 24% in the conventional group. The mean difference between planned and measured bone resection was 0.21 mm with a maximum of 2 mm. The 95% confidence interval was at each position 1 mm or below. Conclusions: The described imageless robotic system is accurate in terms of coronal alignment and bone resections. In precision, it is superior to conventional instrumentation and could therefore be used to evaluate new alignment concepts. [ABSTRACT FROM AUTHOR]

The hierarchical dimensionless method can effectively remove the effect of different index dimensions, and solve imbalanced data distribution and low discrimination caused by anomalous index values. However, when using this method, it is necessary to artificially specify the number of partition intervals so that the dimensionless results are interfered by human factors and lose objectivity. To solve this problem, a dimensionless method of density hierarchy is proposed considering the distribution characteristics of raw data. This method divides the interval according to the density of data distribution, objectively determines the hierarchical series, and takes into account the advantages of the hierarchical dimensionless method. The calculation is comparatively simple and reduces human factors. In addition, through the stochastic simulation method, it is found that the method has good anti-interference to outliers, and the balance of dimensionless results is affected by the scale of raw data. [ABSTRACT FROM AUTHOR]

In multiple linear regression analysis, the variance inflation factor is a well-known collinearity measure. It is defined as the function of the coefficient of determination between the explanatory variables, and it is based on the maximum likelihood estimator of the regression coefficients. Nevertheless, in addition to outliers, leverage observations can have significant impact on the coefficient of determination, and thereby the variance inflation factor. This study presents an improved robust variance inflation factor estimator that is not affected by these observations. Simulation studies and a real data analysis indicate that the modified robust variance inflation factor estimator performs better than the traditional one. [ABSTRACT FROM AUTHOR]

Presence of one or more outliers in the observations affect inference procedure in statistical analysis. Designs robust against presence of a single outlier can be found in the literature for both regression and block design set-ups. In this paper, an attempt has been made to find a robust design in a block design set-up for the estimation of a full set of orthonormal treatment contrasts when there are more than one outlier among the observations. It appears that symmetry and balance play an important role in this study; it is seen that as we deviate from them, designs deviate from robustness. [ABSTRACT FROM AUTHOR]

Forest trees growing in high altitude conditions offer a convenient model for studying adaptation processes. They are subject to a whole range of adverse factors that are likely to cause local adaptation and related genetic changes. Siberian larch (Larix sibirica Ledeb.), whose distribution covers different altitudes, makes it possible to directly compare lowland with highland populations. This paper presents for the first time the results of studying the genetic differentiation of Siberian larch populations, presumably associated with adaptation to the altitudinal gradient of climatic conditions, based on a joint analysis of altitude and six other bioclimatic variables, together with a large number of genetic markers, single nucleotide polymorphisms (SNPs), obtained from double digest restriction-site-associated DNA sequencing (ddRADseq). In total, 25,143 SNPs were genotyped in 231 trees. In addition, a dataset of 761 supposedly selectively neutral SNPs was assembled by selecting SNPs located outside coding regions in the Siberian larch genome and mapped to different contigs. The analysis using four different methods (PCAdapt, LFMM, BayeScEnv and RDA) revealed 550 outlier SNPs, including 207 SNPs whose variation was significantly correlated with the variation of some of environmental factors and presumably associated with local adaptation, including 67 SNPs that correlated with altitude based on either LFMM or BayeScEnv and 23 SNPs based on both of them. Twenty SNPs were found in the coding regions of genes, and 16 of them represented non-synonymous nucleotide substitutions. They are located in genes involved in the processes of macromolecular cell metabolism and organic biosynthesis associated with reproduction and development, as well as organismal response to stress. Among these 20 SNPs, nine were possibly associated with altitude, but only one of them was identified as associated with altitude by all four methods used in the study, a nonsynonymous SNP in scaffold_31130 in position 28092, a gene encoding a cell membrane protein with uncertain function. Among the studied populations, at least two main groups (clusters), the Altai populations and all others, were significantly genetically different according to the admixture analysis based on any of the three SNP datasets as follows: 761 supposedly selectively neutral SNPs, all 25,143 SNPs and 550 adaptive SNPs. In general, according to the AMOVA results, genetic differentiation between transects or regions or between population samples was relatively low, although statistically significant, based on 761 neutral SNPs (FST = 0.036) and all 25,143 SNPs (FST = 0.017). Meanwhile, the differentiation based on 550 adaptive SNPs was much higher (FST = 0.218). The data showed a relatively weak but highly significant linear correlation between genetic and geographic distances (r = 0.206, p = 0.001). [ABSTRACT FROM AUTHOR]

According to the Indian health line report, 12% of the population suffer from abnormal thyroid functioning. The major challenge in this disease is that the existence of hypothyroid may not propagate any noticeable symptoms in its early stages. However, delayed treatment of this disease may lead to several other health problems, such as fertility issues and obesity. Therefore, early treatment is essential for patient survival. The proposed technology could be used for the prediction of hypothyroid disease and its severity during its early stages. Though several classification and regression algorithms are available for the prediction of hypothyroid using clinical information, there exists a gap in knowledge as to whether predicted outcomes may reach a higher accuracy or not. Therefore, the objective of this research is to predict the existence of hypothyroidism with higher accuracy by optimizing the estimator list of the pycaret classifier model. With this overview, a blunge calibration intelligent feature classification model that supports the assessment of the presence of hypothyroidism with high accuracy is proposed. A hypothyroidism dataset containing 3163 patient details with 23 independent and one dependent feature from the University of California Irvine (UCI) machine-learning repository was used for this work. We undertook dataset preprocessing and determined its incomplete values. Exploratory data analysis was performed to analyze all the clinical parameters and the extent to which each feature supports the prediction of hypothyroidism. ANOVA was used to verify the F-statistic values of all attributes that might highly influence the target. Then, hypothyroidism was predicted using various classifier algorithms, and the performance metrics were analyzed. The original dataset was subjected to dimensionality reduction by using regressor and classifier feature-selection algorithms to determine the best subset components for predicting hypothyroidism. The feature-selected subset of the clinical parameters was subjected to various classifier algorithms, and its performance was analyzed. The system was implemented with python in the Spyder editor of Anaconda Navigator IDE. Investigational results show that the Gaussian naive Bayes, AdaBoost classifier, and Ridge classifier maintained the accuracy of 89.5% for the regressor feature-selection methods. The blunge calibration regression model (BCRM) was designed with naive Bayes, AdaBoost, and Ridge as the estimators with accuracy optimization and with soft blending based on the sum of predicted probabilities of classifiers. The proposed BCRM showed 99.5% accuracy in predicting hypothyroidism. The implementation results show that the Kernel SVM, KNeighbor, and Ridge classifier maintained an accuracy of 87.5% for the classifier feature-selection methods. The blunge calibration classifier model (BCCM) was developed with Kernel SVM, KNeighbor, and Ridge as the estimators, with accuracy optimization and with soft blending based on the sum of predicted probabilities of classifiers. The proposed BCCM showed 99.7% accuracy in predicting hypothyroidism. The main contribution of this research is the design of BCCM and BCRM models that were built with accuracy optimization with soft blending based on the sum of predicted probabilities of classifiers. The BCRM and BCCM models uniqueness's are achieved by updating the estimators list with the effective classifiers and regressors that suit the application at runtime. [ABSTRACT FROM AUTHOR]

Background: One of the seminal events since 2019 has been the outbreak of the SARS-CoV-2 pandemic. Countries have adopted various policies to deal with it, but they also differ in their socio-geographical characteristics and public health care facilities. Our study aimed to investigate differences between epidemiological parameters across countries. Method: The analysed data represents SARS-CoV-2 repository provided by the Johns Hopkins University. Separately for each country, we estimated recovery and mortality rates using the SIRD model applied to the first 30, 60, 150, and 300 days of the pandemic. Moreover, a mixture of normal distributions was fitted to the number of confirmed cases and deaths during the first 300 days. The estimates of peaks' means and variances were used to identify countries with outlying parameters. Results: For 300 days Belgium, Cyprus, France, the Netherlands, Serbia, and the UK were classified as outliers by all three outlier detection methods. Yemen was classified as an outlier for each of the four considered timeframes, due to high mortality rates. During the first 300 days of the pandemic, the majority of countries underwent three peaks in the number of confirmed cases, except Australia and Kazakhstan with two peaks. Conclusions: Considering recovery and mortality rates we observed heterogeneity between countries. Liechtenstein was the "positive" outlier with low mortality rates and high recovery rates, at the opposite, Yemen represented a "negative" outlier with high mortality for all four considered periods and low recovery for 30 and 60 days. [ABSTRACT FROM AUTHOR]

This work discourages using the logistic regression (LR) model for estimative purposes when the sample size is small. We propose a new model called the "Robust Bayesian Logistic (RBL) model" that minimizes bias in the estimated logistic regression coefficients when the sample size is small, and covariate corruption is suspected. For the prior specification in the proposed RBL model, all the logistic regression coefficients are assigned independent Student's t-distribution with the location parameter 0, scale parameter 1, and degree of freedom 7 for the constant term and degree of freedom of 1 for all other regression coefficients. In our experimental study, the proposed RBL model outperforms the Logistic Regression (LR) model by having a lower mean squared error (MSE) in the regression coefficients estimated for all the sample sizes considered. The proposed RBL model has a lower standard deviation than the LR model for all the estimated regression coefficients on the real-life dataset. We suggest that the proposed RBL model be considered for logistic modeling since it generates stable, consistent, and reliable estimates, especially when the sample size is small. The proposed RBL model is a fully Bayesian method implemented in the R environment using the RJAGS package. [ABSTRACT FROM AUTHOR]

It is well-known that the traditional functional regression model is mainly based on the least square or likelihood method. These methods usually rely on some strong assumptions, such as error independence and normality, that are not always satisfied. For example, the response variable may contain outliers, and the error term is serially correlated. Violation of assumptions can result in unfavorable influences on model estimation. Therefore, a robust estimation procedure of a semi-functional linear model with autoregressive error is developed to solve this problem. We compare the efficiency of our procedure to the least square method through a simulation study and two real data analyses. The conclusion illustrates that the proposed method outperforms the least square method, providing random errors follow the heavy-tail distribution. [ABSTRACT FROM AUTHOR]

Assessment of the adequacy of a proposed linear calibration curve is necessarily subjective in chemical analysis. If the outlier points in calibration are not identified and discarded, the constructed model will not have much validity and does not warrant the accuracy and precision of prediction step. Recognizing of influential points, outlier data, and discarding them is one of the steps in data processing that has been considered in various sciences. The outlier points can arise from (I) bad design of calibration set and (II) gross error in doing experiments. Therefore, we aimed to extract a map that recognizes the following issues: (A) the existence of data with high regression coverage that is far from the rest and will strongly affect the accuracy of the calibration equation, high leverage points; (B) large error in the experimental process: The recorded signal does not match the desired concentration; and (C) points with a concentration lower than the limit of quantification, which is calculated by considering the standard error of regression instead of the standard deviation of blank. The efficiency of the proposed roadmap will be reviewed, and this will give a new perspective on the calibration equation to avoid common mistakes in analytical chemistry. To achieve the above goal, visual and statistically significant tests will be used and all tests will be performed in a simple Microsoft Excel environment. [ABSTRACT FROM AUTHOR]

In this study, the criteria of choosing a neighboring station by k-nearest neighbor method were evaluated through outlier detection. Monthly average temperature, average relative humidity and total precipitation data were used between 1926 and 2012 at 129 stations in Turkey. The first problem when choosing a neighbor according to the extent of nearness is that 0.04% - 3% of neighbors are more than 140 km away and less than 0.4 correlation because of choosing distant neighbors due to lack of data. The second problem is the more distant neighbors show a significantly higher correlation than the 5 closest neighbors at 0.1 - 3% of monthly series. When the neighbor is selected according to the correlation coefficient, the 1st neighbor of each month can be used for only that month in 2% - 8% of the stations. These percentages decrease for 2nd and 3rd neighbors. In 25% - 86% of stations, the 1st neighbors in certain months can be used for all months, but it should be determined which certain months are they. In addition, in 2.5% of the stations, the 1st neighbor of each variable can be used for only that variable. The percentage decreases for 2nd and 3rd neighbors. In 29% of the stations, the 1st neighbors in certain variables can be used for all the variables, but it should be determined which certain variables are they. Other than these, when the detected outliers are removed, the correlation coefficient becomes mostly 1-36% higher, sometimes 1-5% less than for the series with outliers. [ABSTRACT FROM AUTHOR]

Global Navigation Satellite System (GNSS) precise positioning can be significantly affected by severe multipath effects and outliers in harsh environments, and highly relies on quality control strategies. Previous studies mainly focus on the posterior residuals to check and exclude the outliers in GNSS observations, limited work emphasizes the combined quality control method considering both the prior and posterior knowledge simultaneously. This paper proposed a real-time combined quality control method to process the multipath effects and outliers in harsh environments simultaneously. Specifically, in the prior stage, a modified multipath processing strategy is proposed for both phase and code observations, then a modified detection, identification, and adaptation (DIA) method considering the maximum times of data snooping is studied in the posterior stage. Two dedicated experiments in real harsh environments were carried out to evaluate the performance of the proposed combined quality control method. For the static experiment, the proposed method exhibits smaller positioning errors, the best positioning accuracy, and the highest availability in this study. Specifically, the proposed method exhibits an improved percentage of 55.4 %, 56.3 %, and 59.7 % for positioning accuracy compared to those without the quality control method in the E, N, and U directions, respectively. Besides, the proposed method can further improve the performance of ambiguity resolution with an improved percentage of 32.2 %. For the kinematic experiment, the three-dimensional positioning accuracy of the proposed method is 0.577 m, which exhibits a 40.0 % improvement compared to those without the quality control method. Also, the proposed method exhibits better performance under relatively strong multipath effects. In this sense, the proposed real-time combined quality control method is highly appreciated in terms of positioning availability, accuracy, and ambiguity resolution for GNSS precise positioning, especially in harsh environments. [ABSTRACT FROM AUTHOR]

The Exceptional Events Rule (EER) of the Clean Air Act was intended to address "exceptional" air quality events but, in practice, the way the rule is employed has significant impacts on regulatory data sets that are used to understand and manage air quality. Through this rule, the Environmental Protection Agency is entangled in the politics of classification, striving to separate the unnatural from the natural and the exceptional from the common with important consequences for air quality data and regulation. If deemed exceptional, the data can be removed from the regulatory record, not only altering our understanding of air quality—and, importantly, risk and exposure—but also the management of it. By analyzing regulatory data sets, texts, and appeals, this article explores the consequences of the rule's data exclusions. I argue that the act of producing "natural exceptions" has another unintended consequence: It inadvertently creates exceptional natures; that is, natures that despite their common occurrence are deemed rare. By repeatedly removing data, the EER changes understandings of normal or expected events and becomes a powerful tool for deregulation. Ironically, even as regulations produce exceptional natures, they are simultaneously undermined by them, leading to regular and repeated surprises, unnoticed hazards, undetected exposures, and deregulation. [ABSTRACT FROM AUTHOR]

Identifying influential and outlying data is important as it would guide the effective collection of future data and the proper use of existing information. We develop a unified approach for outlier detection and influence analysis. Our proposed method is grounded in the intuitive value of information concepts and has a distinct advantage in interpretability and flexibility when compared to existing methods: It decomposes the data influence into the leverage effect (expected to be influential) and the outlying effect (surprisingly more influential than being expected); and it applies to all decision problems such as estimation, prediction and hypothesis testing. We study the theoretical properties of three values of information quantities, establish the relationship between the proposed measures and classic measures in the linear regression setting and provide real data analysis examples of how to apply the new value of information approach in the cases of linear regression, generalized linear mixed models and hypothesis testing. [ABSTRACT FROM AUTHOR]

Geometric model fitting is a fundamental task in computer vision, which serves as the pre-requisite of many downstream applications. While the problem has a simple intrinsic structure where the solution can be parameterized within a few degrees of freedom, the ubiquitously existing outliers are the main challenge. In previous studies, random sampling techniques have been established as the practical choice, since optimization-based methods are usually too time-demanding. This prospective study is intended to design efficient algorithms that benefit from a general optimization-based view. In particular, two important types of loss functions are discussed, i.e., truncated and $l_1$ l 1 losses, and efficient solvers have been derived for both upon specific approximations. Based on this philosophy, a class of algorithms are introduced to perform deterministic search for the inliers or geometric model. Recommendations are made based on theoretical and experimental analyses. Compared with the existing solutions, the proposed methods are both simple in computation and robust to outliers. Extensive experiments are conducted on publicly available datasets for geometric estimation, which demonstrate the superiority of our methods compared with the state-of-the-art ones. Additionally, we apply our method to the recent benchmark for wide-baseline stereo evaluation, leading to a significant improvement of performance. Our code is publicly available at https://github.com/AoxiangFan/EifficientDeterministicSearch. [ABSTRACT FROM AUTHOR]

The conventional fuzzy C-means (FCM) is sensitive to the initial cluster centers and outliers, which may cause the centers deviate from the real centers when the algorithm converges. To improve the performance of FCM, a method of initializing the cluster centers based on probabilistic suppression is proposed and an improved local outlier factor is integrated into the model of FCM. Firstly, the probability of an object as cluster center is defined by its local density, and all initial centers are obtained by the cluster center's probability and probability suppression function incrementally. Next, an improved local outlier factor is reconstructed according to the local distribution of an object, and its reciprocal is regarded as the contribution degree of an object to cluster center. Then, the improved local outlier factor is integrated into FCM to alleviate the negative effect caused by outliers. Finally, experiments on synthetic and real-world datasets are provided to demonstrate the clustering performance and anti-noise ability of proposed method. [ABSTRACT FROM AUTHOR]

A common problem with matching algorithms, in photogrammetry and computer vision, is the imperfection of finding all correct corresponding points, so-called inliers, and, thus, resulting in incorrect or mismatched points, so-called outliers. Many algorithms, including the well-known randomized random sample consensus (RANSAC)-based matching, have been developed focusing on the reduction of outliers. RANSAC-based methods, however, have limitations such as increased false positive rates of outliers, and, consequently resulting in fewer inliers, an unnecessary high number of iterations, and high computational time. Such deficiencies possibly result from the random sampling process, the presence of noise, and incorrect assumptions of the initial values. This paper proposes a modified version of RANSAC-based methods, called Empowered Locally Iterative SAmple Consensus (ELISAC). ELISAC improves RANSAC by utilizing three basic modifications individually or in combination. These three modifications are (a) to increase the stability and number of inliers using two Locally Iterative Least Squares (LILS) loops (Basic LILS and Aggregated-LILS), based on the new inliers in each loop, (b) to improve the convergence rate and consequently reduce the number of iterations using a similarity termination criterion, and (c) to remove any possible outliers at the end of the processing loop and increase the reliability of results using a post-processing procedure. In order to validate our proposed method, a comprehensive experimental analysis has been done on two datasets. The first dataset contains the commonly-used computer vision image pairs on which the state-of-the-art RANSAC-based methods have been evaluated. The second dataset image pairs were captured by a drone over a forested area with various rotations, scales, and baselines (from short to wide). The results show that ELISAC finds more inliers with a faster speed (lower computational time) and lower error (outlier) rates compared to M-estimator SAmple Consensus (MSAC). This makes ELISAC an effective approach for image matching and, consequently, for 3D information extraction of very high and super high-resolution imagery acquired by space-borne, airborne, or UAV sensors. In particular, for applications such as forest 3D modeling and tree height estimations where standard matching algorithms are problematic due to spectral and textural similarity of objects (e.g., trees) on image pairs, ELISAC can significantly outperform the standard matching algorithms. [ABSTRACT FROM AUTHOR]

Multi-rate systems exist widely in the field of computer process control. Research on the multi-rate issue of linear systems is relatively mature and is gradually turning to multi-rate issues of nonlinear systems. Concurrently, irregular process disturbances (impulse noise, outliers, non-zero mean noise) and varying transmission delay problems are often overlooked or considered alone, which is lacking generality. Thus, the main objective of this paper is to formulate and solve the identification problem of multi-rate nonlinear Wiener models with time-varying delay and irregular process disturbances simultaneously. The probability graph model of the Wiener process is constructed. Under the expectation–maximization (EM) framework, the Scale outlier model and Location outlier model (both based on Gaussian mixture distribution) are separately introduced to model the contaminated output data. The time-varying delay at each sampling instant is assumed as a uniform distribution. An auxiliary model is applied to acquire the unmeasured middle variable. Then, a robust EM identification algorithm is developed and its convergence is analyzed. The validity of the developed approach is illustrated through two numerical examples and a simulation of the continuous stirred tank reactor. • The parameter estimation of Wiener model under time-varying delay, multi-rate sampling and irregular interference (impulse noise, outliers) is studied for the first time. Under the expectation–maximization framework, the Scale outlier model is introduced to model the contaminated output data. The time-varying delay at each sampling instant is assume to obey the uniform distribution. The proposed method can estimate the model parameter and time varying delay simultaneously. • For the presence of non-zero mean noise, the Location outlier model is introduced to model the contaminated output data. The proposed method can further estimate the bias of non-zero noise. • The probability graph model of the Wiener process is constructed. The idea of an auxiliary model is applied to acquire the unmeasured middle variable. • The identification convergence of the proposed algorithm is analyzed. [ABSTRACT FROM AUTHOR]

The article focuses on the development of an algorithm that can predict wind speed potential in the presence of outlier observations to assist in the generation of wind energy. Topics include the importance of renewable energy due to the negative effects of fossil fuels on the environment, the challenges of modeling wind speeds due to outliers, and the development of the modified fuzzy regression with a noise cluster as significant top performing technique for predicting wind speed potential.

This paper deals with a method of quantifying the harmonic contribution of each harmonic source to system voltage distortion. Assessing the harmonic contribution of individual harmonic sources is essential for mitigating and managing system harmonic levels. Harmonic contributions can be evaluated using the principle of voltage superposition with equivalent voltage models for harmonic sources. In general, the parameters of equivalent voltage models are estimated numerically because it is difficult to measure them directly. In this paper, we present an effective method for estimating equivalent model parameters based on the random sample consensus (RANSAC) and recursive least square (RLS) with a variable forgetting factor. The procedure for quantifying harmonic contributions using equivalent models is also introduced. Additionally, we propose a network diagram of harmonic contributions that makes it easy to understand the harmonic distortion contributions of all harmonic sources. [ABSTRACT FROM AUTHOR]