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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]

It is shown that changes in conditions during an experiment can go unnoticed and how anomalous measurements arise that can lead to incorrect values containing gross errors. It is interpreted that distortion of measurements may be a consequence of incorrect operation of recording devices; if a fault is detected, such values should be discarded. The problems of the uncertainty of information on input data in calculations using classical methods are outlined. The influence of the deviation of external impacts from nominal values, variability of emission intensity, and the nonlinear nature of influence of external factors on the probability of an event were studied. [ABSTRACT FROM AUTHOR]

Outliers present in a data set can severely impact the statistical analysis and lead to erroneous conclusions. Hence, outlier identification is an important task before analysis of data is undertaken. Outliers being different from the rest of the observations in a data set may contain valuable information which can be obtained by carefully examining the identified outliers. While several methods of outlier identification exists for univariate and multivariate data, not that many methods exist for functional data. In sequential identification of outliers from a set of functional data, the corresponding estimation of covariance operator is affected by the outliers that are still present in the data. This leads to degradation in performance of these methods when the proportion of outliers in the data set increases. In this paper we propose a new outlier detection algorithm that uses an adaptive and data driven approach of dimension selection. The proposed method is seen to have better efficiency in an extensive simulation exercise in comparison to the existing method. Three illustrations with real life environmental data sets are also reported. [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]

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]

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]

The teaching-learning process plays an important role in education. To improve this process valuable and timely feedback is taken from the students. That feedback can be used for two main purposes: one is to improve the process according to student expectations and another is to evaluate the teaching faculty's performance for the sake of appraisal. However, some students can give unfair and biased feedback. Such feedback may produce an adverse effect on appraisal. To remove these anomalies generated due to favoritism and biasness in the feedback innovative interquartile range (IQR) based outlier detection and removal technique is implemented in this article. The proposed technique removes the outliers based on IQR and precisely selects the central tendency (mean or median) from the feedback data distribution based on skewness. Then the identified central tendency will be considered to compute the appraisal indicator. To conduct the experiments feedback data is collected from the students via questionnaire. The questionnaire is prepared by expert academicians. The questionnaire contains twenty-one questions which are divided into five categories. By confirming experimental results, the proposed IQR-based outlier detection and removal technique removes the outliers from the data set and improves the performance analysis which intern helpful for teaching faculty performance appraisal. [ABSTRACT FROM AUTHOR]

Summary: The Anderson‐Rubin (AR) test is an important method that allows for reliable inference in the instrumental variable model when the instruments are weak. Yet, the robustness properties of this test have not been formally studied. As it turns out that the AR test is not robust to outliers, we show how to construct an outlier robust alternative—the robust AR test. We investigate the robustness properties of the robust AR test and show that the robust AR statistic asymptotically follows a chi‐square distribution. The theoretical results are illustrated by a simulation study. Finally, we apply the robust AR test to three different case studies that are affected by different types of outliers. [ABSTRACT FROM AUTHOR]

The notion of refugees as a viable source of labor to address skill shortages in the destination country's labor market has rarely been the dominant discourse on refugee entrants. Bella's1 lived experience as a professional woman who arrived as a Syrian conflict refugee to Australia in 2017 presents an outlier in refugee research and challenges conventional scholarly wisdom and public discourse. A combination of human capital, a purposeful use of networks, supported by her desire for recognition and a deep sense of self-worth allowed her to navigate the formalized and structured Australian business landscape. Accordingly, she was able to overcome the stigma of being a refugee: Less worthy of employment status in a position representative of her overseas skills and qualifications. In drawing on an outlier methodology and critical theory, we develop a more nuanced understanding of the agency of skilled and qualified refugee women drawing attention to lessons for business which typically takes a "one size fits all" approach to labor integration. [ABSTRACT FROM AUTHOR]

Abstract 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

Analysis of data is crucial in waste management to improve effective planning from both short- and long-term perspectives. Real-world data often presents anomalies, but in the waste management sector, anomaly detection is seldom performed. The main goal and contribution of this paper is a proposal of a complex machine learning framework for changepoint detection in a large number of short time series from waste management. In such a case, it is not possible to use only an expert-based approach due to the time-consuming nature of this process and subjectivity. The proposed framework consists of two steps: (1) outlier detection via outlier test for trend-adjusted data, and (2) changepoints are identified via comparison of linear model parameters. In order to use the proposed method, it is necessary to have a sufficient number of experts’ assessments of the presence of anomalies in time series. The proposed framework is demonstrated on waste management data from the Czech Republic. It is observed that certain waste categories in specific regions frequently exhibit changepoints. On the micro-regional level, approximately 31.1% of time series contain at least one outlier and 16.4% exhibit changepoints. Certain groups of waste are more prone to the occurrence of anomalies. The results indicate that even in the case of aggregated data, anomalies are not rare, and their presence should always be checked.

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

Outliers are a common problem in applied statistics, together with multicollinearity. In this paper, robust Liu estimators are introduced into a partially linear model to combat the presence of multicollinearity and outlier challenges when the error terms are not independent and some linear constraints are assumed to hold in the parameter space. The Liu estimator is used to address the multicollinearity, while robust methods are used to handle the outlier problem. In the literature on the Liu methodology, obtaining the best value for the biased parameter plays an important role in model prediction and is still an unsolved problem. In this regard, some robust estimators of the biased parameter are proposed based on the least trimmed squares (LTS) technique and its extensions using a semidefinite programming approach. Based on a set of observations with a sample size of n, and the integer trimming parameter h ≤ n, the LTS estimator computes the hyperplane that minimizes the sum of the lowest h squared residuals. Even though the LTS estimator is statistically more effective than the widely used least median squares (LMS) estimate, it is less complicated computationally than LMS. It is shown that the proposed robust extended Liu estimators perform better than classical estimators. As part of our proposal, using Monte Carlo simulation schemes and a real data example, the performance of robust Liu estimators is compared with that of classical ones in restricted partially linear models.

Given the increasingly severe global fires, the accurate detection of small and fragmented cropland fires has been a significant challenge. The use of medium-resolution satellite data can enhance detection accuracy; however, key challenges in this approach include accurately capturing the annual and interannual variations of burning characteristics and identifying outliers within the time series of these changes. In this study, we focus on a typical crop-straw burning area in Henan Province, located on the North China Plain. We develop an automated burned-area detection algorithm based on near-infrared and short-wave infrared data from Landsat 5 imagery. Our method integrates time-series outlier analysis using filtering and automatic iterative algorithms to determine the optimal threshold for detecting burned areas. Our results demonstrate the effectiveness of using preceding time-series and seasonal time-series analysis to differentiate fire-related changes from seasonal and non-seasonal influences on vegetation. Optimal threshold validation results reveal that the automatic threshold method is efficient and feasible with an overall accuracy exceeding 93%. The resulting burned-area map achieves a total accuracy of 93.25%, far surpassing the 76.5% detection accuracy of the MCD64A1 fire product, thereby highlighting the efficacy of our algorithm. In conclusion, our algorithm is suitable for detecting burned areas in large-scale farmland settings and provides valuable information for the development of future detection algorithms.

With digitalization of data and the rise of World Wide Web, access to information has been very easy and affordable. Especially the Web and the Internet have boosted research activities by facilitating access to large, publicly available medical datasets under open access scheme. These developments have resulted in explosive amounts of data being generated varying in volume, variety and velocity thus referred to as big data. Availability of such medical big data has catalyzed the research in medical predictive analytics. However, the true value of such data can be derived only after subjecting it to careful processing and analysis before drawing inferences from it. Publicly available medical datasets have noise in the form of missing values, outliers and data inconsistencies, that may affect the results or outcomes negatively. Pre-processing of such data is essential to eliminate noisy elements and refine the data to be suitable for further analysis and processing. This paper signifies the need for data pre-processing and explains the data pre-processing pipeline with various underlying stages constituting it. It also presents a comparative analysis of various data pre-processing techniques for handling missing values and outliers in a dataset. [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]

Analysis of data is crucial in waste management to improve effective planning from both short- and long-term perspectives. Real-world data often presents anomalies, but in the waste management sector, anomaly detection is seldom performed. The main goal and contribution of this paper is a proposal of a complex machine learning framework for changepoint detection in a large number of short time series from waste management. In such a case, it is not possible to use only an expert-based approach due to the time-consuming nature of this process and subjectivity. The proposed framework consists of two steps: (1) outlier detection via outlier test for trend-adjusted data, and (2) changepoints are identified via comparison of linear model parameters. In order to use the proposed method, it is necessary to have a sufficient number of experts' assessments of the presence of anomalies in time series. The proposed framework is demonstrated on waste management data from the Czech Republic. It is observed that certain waste categories in specific regions frequently exhibit changepoints. On the micro-regional level, approximately 31.1% of time series contain at least one outlier and 16.4% exhibit changepoints. Certain groups of waste are more prone to the occurrence of anomalies. The results indicate that even in the case of aggregated data, anomalies are not rare, and their presence should always be checked. [ABSTRACT FROM AUTHOR]

Symbolic interval data analysis (SIDA) has been successfully applied in a wide range of fields, including finance, engineering, and environmental science, making it a valuable tool for many researchers for the incorporation of uncertainty and imprecision in data, which are often present in real-world scenarios. This paper proposed the interval improved fuzzy partitions fuzzy C-means (IIFPFCM) clustering algorithm from the viewpoint of fast convergence that independently combined with Euclidean distance and city block distance. The two proposed methods both had a faster convergence speed than the traditional interval fuzzy c-means (IFCM) clustering method in SIDA. Moreover, there was a problem regarding large and small group division for symbolic interval data. The proposed methods also had better performance results than the traditional interval fuzzy c-means clustering method in this problem. In addition, the traditional IFCM clustering method will be affected by outliers. This paper also proposed the IIFPFCM algorithm to deal with outliers from the perspective of interval distance measurement. From experimental comparative analysis, the proposed IIFPFCM clustering algorithm with the city block distance measure was found to be suitable for dealing with SIDA with outliers. Finally, nine symbolic interval datasets were assessed in the experimental results. The statistical results of convergence and efficiency on performance revealed that the proposed algorithm has better results. [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]

Finding valid correspondences is of considerable significance to image matching, which has been regarded as the key of numerous vision-based tasks. Current methods usually have drawbacks in sets with high proportion of outliers. To address the problem, given a set of putative correspondences in two images, this paper proposes a novel framework (named SAH-Net) to remove outliers and recover camera pose through essential matrix using an end-to-end network. The proposed SAH-Net is hierarchical with a multi-scale structure, which consists of correspondence level and cluster level. First, correspondence level takes advantage of two-view geometry to learn correspondence features. Next, in order to integrate structural information of the scene, correspondences are pooled via a self-attention method. Additionally, SAH-Net applies a spatial correlation operation after the clustering, separating features into segments and learning the spatial characteristics of clustered nodes. Finally, clusters have been integrated with spatial information, and they are recovered to original scale via a learned upsampling operation. Extensive experiments are conducted on remote sensing image registration, general image matching (outdoor and indoor image datasets respectively) and loop closure detection, which demonstrate the excellence of SAH-Net in mismatch removal and relative pose estimation compared to other state-of-the-art competitors. [ABSTRACT FROM AUTHOR]

When dealing with uncertain data, traditional model construction methods often ignore or filter out noise data to improve model performance. However, this simple approach can lead to insufficient data utilization, model bias, reduced detection ability, and decreased robustness of detection models. Outliers can be considered as data that are inconsistent with other patterns at certain specific moments and are not always negative data, so their emergence is not always bad. In the process of data analysis, outliers play a crucial role in sample vector recognition, missing value processing, and model stability verification. In addition, unsupervised models have very high computation costs when recognizing outliers, especially non-parameterized unsupervised models. To solve the above problems, we used semi-supervised learning processes and used similarity as a negative selection criterion to propose a local density verification detection model (Vd-LOD). This model establishes similarity pseudo-labels for multi-label and multi-type samples, verifies the accuracy of outlier values based on local outlier factors, and increases the detector's sensitivity to outliers. The experimental results show that under different parameter settings with varying outlier quantities, Vd-LOD outperforms other detection models in terms of the significant increase in average time consumption caused by verifying the presence of relationships, while also achieving an approximate 6% improvement in average detection accuracy. [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]

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]

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]

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]

Fuzzy transform (FT) is a soft computing method that has many successful applications. Least-squares fuzzy transform (LS-FT) combining L 2 -norm and FT was proposed by Patane in 2011, but it can be severely affected by the presence of outlier. To solve this problem, we proposed least absolute deviation fuzzy transform (LAD-FT) combining L 1 -norm and FT and verified the robustness of outlier through experiments based on the various functions. In the process, we found the solution of LAD-FT for a function of one variable cannot be directly extended to a function of two variables. This paper is a first attempt to prove this problem. We also propose a novel algorithm for applying the LAD-FT to a function of two variables. Since FT is already known as a useful tool for various image processing problems, we validate and compare the performance of FT, LS-FT, and LAD-FT on the three main perspectives, especially, image reconstruction, image denoising, and outlier robustness. Experiments are conducted by many various sizes of images and compression rates and peak signal to noise ratio (PSNR) and structural similarity index (SSIM) are used to measure the difference between two images. Results show that LAD-FT is robust to outlier, FT is superior in image reconstruction and image denoising, and SSIM has better performance than PSNR. [ABSTRACT FROM AUTHOR]

Objectives: This review aimed to harmoniously summarize and compare outlier rates for various cardiac troponin (cTn) assays, including high-sensitivity-cTn (hs-cTn) assays and contemporary cTn (generation of assays prior to hs-cTn ones) assays, from the published studies. Methods: The PRISMA guidelines were utilized to perform this systematic review. Five databases, including PubMed, Scopus, Embase, Cochrane Library, and Web of Science, were searched using specific keywords up to June 30th, 2023. Studies reporting specifically calculated outlier rates for cTn assays when conducting in-vitro diagnosis in human samples were included. Selected studies were then further assessed using the GRADE tool. Results: Thirteen studies were included. The data from the studies were summarized statistically in this review. The results showed substantial evidence of improved analytical robustness or reduced respective mean rates of outliers, critical outliers, and analytical outliers for hs-cTn assays (0.14 %, 0.18 %, and 0.18 %) compared to contemporary cTn assays (0.63 %, 0.71 %, and 0.50 %). Conclusion: The findings offer promisingly provide a comprehensive reference for laboratory scientists and clinical staff in choosing the most suitable cTn assay for patient care regrading outlier rates. Besides, this review reveals the advancements of hs-cTn assays with lower outlier rates than contemporary cTn assays. The emerging challenges for continuously improving analytical robustness of cTn assays are also elaborated.

Diameter at breast height (DBH) is a crucial parameter for forest inventory. However, accurately estimating DBH remains challenging due to the noisy and incomplete cross-sectional points. To address this, this paper proposed a reliable DBH estimation method using terrestrial LiDAR points through polar coordinate transformation and progressive outlier removal. In this paper, the initial center was initially detected by rasterizing the convex hull, and then the Cartesian coordinates were transformed into polar coordinates. In the polar coordinate system, the outliers were classified as low and high outliers according to the distribution of polar radius difference. Both types of outliers were then removed using adaptive thresholds and the moving least squares algorithm. Finally, DBH was estimated by calculating the definite integral of arc length in the polar coordinate system. Twenty publicly available individual trees were adopted for the test. Experimental results indicated that the proposed method performs better than the other four classical DBH estimation methods. Furthermore, several extreme cases scanned using terrestrial LiDAR in practice, such as cross-sectional points with lots of outliers or larger data gaps, were also tested. Experimental results demonstrate that the proposed method accurately calculates DBH even in these challenging cases.

Poverty is defined as an inability of the individual to meet basic needs for a decent life. According to BPS data in 2020, Sulawesi Island ranks fifth as the poorest island in Indonesia. This study aims to find out the mapping of areas and indicators of poverty in Sulawesi Island using Biplot Analysis with Robust Singular Value Decomposition approach for outlier research data. Based on the results of the study, there are five objects that are outlier and the information provided by the biplot amounted 98.45%. District/city that have similar characteristics are divided into 4 groups. The indicator of poverty that has the most diversity is the School Old Expectations Numbers (Var 4) and the one with the least diversity is Poor Households Using Clean Water (Var 8). Indicators of poverty that are positively correlated are Literacy Numbers (Var 1) and Non-Working Poor Population (Var 5), while the negative correlated are The Non-Working Poor Population (Var 5) and Poor Households Using Clean Water (Var 8). There are 19 districts/cities that have literacy values above the average of all districts/cities and 11 districts/cities that have a per capita expenditure value below the average of all districts/cities.

Presence of missing values and occurrence of outliers in time series cause many hindrances in the analysis of data. Several methods are proposed for determining estimates to replace the missing values and outliers. Mean, median, the largest order statistic and time series model based forecast values are used as the estimates for replacing missing values and outliers. But, no recommendations have been made so far for selection of the estimation methods. This paper attempts to compare the performance of six such estimation methods. Among them, time series models are fitted applying the autoregressive moving average method, long short-term memory method and Facebook’s Prophet method. Models are validated using the test data. Time series of Air Quality Index is used for carrying out for comparative study.

Outlier analysis has been widely studied and has produced many methods. However, there is still rare a method to detect outliers for dynamically streaming batch data (online learning). In the present research, a novel online algorithm to detect outliers in such dataset is proposed. Data points are proceeded by applying a modified recursive PCA to predict sequentially parameters of the model; eigenvalues and eigenvectors of the statistical detection model are recursively updated using approximate values by perturbation methods. More specifically, the recursive eigenstructure is obtained from the derivation of the covariance matrix using the first-order perturbation technique. The Mahalanobis distance is then used as an outlier score. Our algorithm performances are evaluated using some metrics, namely accuration, precision, recall, F1-score, AUC-PR, and the execution time. Results show that the proposed online outlier detection is computationally efficient in time and the algorithm's performance effectiveness is comparable to that of the offline outlier detection algorithm via classical PCA.

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]