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Air pollution is prevalent throughout the entire world due to the release of various gases such as NOx, PM, SO2, tropospheric ozone (O3), etc. Ground-stage ozone is the predominant issue in smog and is the product of the interplay between sunlight and emissions. The destructive impact on the health of the populace might also still occur in cities with noticeably clean air and where ozone levels hardly ever exceed safe limits. Therefore, the findings of small variations in air quality and the technique of regulating air contamination are thought-provoking. The study employs various techniques to effectively observe and assess strategies for detecting and eliminating outliers in ozone emissions from pollution episodes. This technique helps to describe the sources and exceedance values and enhance the value of monitoring the data. In this study, the data have some missing observations. The method of imputation, the classical statistical technique, the statistical process control (SPC) technique, functional data analysis (FDA), and functional process control help to fill in the data and detect outliers, trend deviations, and changes in ozone concentration at ground level. A comparison study is carried out using these three techniques: classical analysis, SPC, and FDA, and the results show how the statistical process control and functional data methods performed better than the classical technique for the detection of outliers and also in what way this methodology can enable an additional, comprehensive method of defining air pollution control measures and water pollution control measures. [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]

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]

Deep metric learning aims to learn a mapping function that projects input data into a high-dimensional embedding space, facilitating the clustering of similar data points while ensuring dissimilar ones are far apart. The most recent studies focus on designing a batch sampler and mining online triplets to achieve this purpose. Conventionally, hard negative mining schemes serve as the preferred batch sampler. However, most hard negative mining schemes search for hard examples in randomly selected mini-batches at each epoch, which often results in less-optimal hard examples and thus sub-optimal performances. Furthermore, Triplet Loss is commonly adopted to perform online triplet mining by pulling the hard positives close to and pushing the negatives away from the anchor. However, when the anchor in a triplet is an outlier, the positive example will be pulled away from the centroid of the cluster, thus resulting in a loose cluster and inferior performance. To address the above challenges, we propose the Prototype-based Support Example Miner (pSEM) and Triplet Loss (pTriplet Loss). First, we present a support example miner designed to mine the support classes on the prototype-based nearest neighbor graph of classes. Following this, we locate the support examples by searching for instances at the intersection between clusters of these support classes. Second, we develop a variant of Triplet Loss, referred to as a Prototype-based Triplet Loss. In our approach, a dynamically updated prototype is used to rectify outlier anchors, thus reducing their detrimental effects and facilitating a more robust formulation for Triplet Loss. Extensive experiments on typical Computer Vision (CV) and Natural Language Processing (NLP) tasks, namely person re-identification and few-shot relation extraction, demonstrated the effectiveness and generalizability of the proposed scheme, which consistently outperforms the state-of-the-art models. [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]

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]

Outlier detection is an important field in data mining, which can be used in fraud detection, fault detection, and other fields. This article focuses on the problem where the density peak clustering algorithm needs a manual parameter setting and time complexity is high; the first is to use the k nearest neighbors clustering algorithm to replace the density peak of the density estimate, which adopts the KD-Tree index data structure calculation of data objects k close neighbors. Then it adopts the method of the product of density and distance automatic selection of clustering centers. In addition, the central relative distance and fast density peak clustering outliers were defined to characterize the degree of outliers of data objects. Then, based on fast density peak clustering outliers, an outlier detection algorithm was devised. Experiments on artificial and real data sets are performed to validate the algorithm, and the validity and time efficiency of the proposed algorithm are validated when compared to several conventional and innovative algorithms. [ABSTRACT FROM AUTHOR]

BACKGROUND: Moonlighting proteins (MPs) are a subclass of multifunctional proteins in which more than one independent or usually distinct function occurs in a single polypeptide chain. Identification of unknown cellular processes, understanding novel protein mechanisms, improving the prediction of protein functions, and gaining information about protein evolution are the main reasons to study MPs. They also play an important role in disease pathways and drug-target discovery. Since detecting MPs experimentally is quite a challenge, most of them are detected randomly. Therefore, introducing an appropriate computational approach to predict MPs seems reasonable. RESULTS: In this study, we introduced a competent model for detecting moonlighting and non-MPs through extracted features from protein sequences. We attempted to set up a well-judged scheme for detecting outlier proteins. Consequently, 37 distinct feature vectors were utilized to study each proteins impact on detecting MPs. Furthermore, 8 different classification methods were assessed to find the best performance. To detect outliers, each one of the classifications was executed 100 times by tenfold cross-validation on feature vectors; proteins which misclassified 90 times or more were grouped. This process was applied to every single feature vector and eventually the intersection of these groups was determined as the outlier proteins. The results of tenfold cross-validation on a dataset of 351 samples (containing 215 moonlighting and 136 non-moonlighting proteins) reveal that the SVM method on all feature vectors has the highest performance among all methods in this study and other available methods. Besides, the study of outliers showed that 57 of 351 proteins in the dataset could be an appropriate candidate for the outlier. Among the outlier proteins, there were non-MPs (such as P69797) that have been misclassified in 8 different classification methods with 16 different feature vectors. Because these proteins have been obtained by computational methods, the results of this study could reduce the likelihood of hypothesizing whether these proteins are non-moonlighting at all. CONCLUSIONS: MPs are difficult to be identified through experimentation. Using distinct feature vectors, our method enabled identification of novel moonlighting proteins. The study also pinpointed that a number of non-MPs are likely to be moonlighting.

Two central objects in constructive approximation, the Christoffel-Darbouxkernel and the Christoffel function, are encoding ample information about theassociated moment data and ultimately about the possible generating measures.We develop a multivariate theory of the Christoffel-Darboux kernel in C^d, withemphasis on the perturbation of Christoffel functions and their level sets withrespect to perturbations of small norm or low rank. The statistical notion ofleverage score provides a quantitative criterion for the detection of outliersin large data. Using the refined theory of Bergman orthogonal polynomials, weillustrate the main results, including some numerical simulations, in the caseof finite atomic perturbations of area measure of a 2D region. Methods offunction theory of a complex variable and (pluri)potential theory are widelyused in the derivation of our perturbation formulas.

The paint industry comprises an elaborate supply chain involving various activities such as raw material procurement, manufacturing, and distribution. In addition, the accuracy of demand prediction significantly impacts supply chain management. A recurrent neural network (RNN) is a powerful method that learns intricate patterns through vast amounts of historical data and provides a prediction, demonstrating excellent performance in demand prediction. However, standard RNN-based demand predictions are limited by many outliers that occur due to the characteristics of the paint industry. Unexpected events such as a factory fire cause rapid fluctuations in paint demand and are difficult to pre-observe. To overcome these limitations, we propose a novel approach that employs clustering to identify demand time series data with similar characteristics and applies statistical outlier adjustment to allow the prediction model to learn the complex patterns of actual demand. The prediction target is the sum of sales for 15 days in the future, and sales data for four paint products are used to evaluate the proposed approach. Experimental results demonstrate a satisfactory prediction accuracy improvement ranging from 100.9% to 152.4%. In addition, unlike the RNN-based models used for comparison, the proposed method is more robust against actual demand fluctuations.

The K-means algorithm, widely used in cluster analysis, is a centroid-based clustering method known for its high efficiency and scalability. However, in realistic situations, the operating environment is susceptible to contamination issues caused by outliers and distribution departures, which may lead to clustering results from K-means that are distorted or rendered invalid. In this paper, we introduce three other alternative algorithms, including K-weighted-medians, K-weighted-L2-medians, and K-weighted-HLs, to address these issues under the consideration of data with weights. The impact of contamination is investigated by examining the estimation effects on optimal cluster centroids. We explore the robustness of the clustering algorithms from the perspective of the breakdown point, and then conduct experiments on simulated and real datasets to evaluate their performance using two new numerical metrics: relative efficiencies based on generalized variance and average Euclidean distance. The results demonstrate the effectiveness of the proposed K-weighted-HLs algorithm, surpassing other algorithms in scenarios involving both contamination issues.

Abstract 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.

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.

Design flood estimation is an essential step in many water engineering design tasks such as the planning and design of infrastructure to reduce flood damage. Flood frequency analysis (FFA) is widely used in estimating design floods when the at-site flood data length is adequate. One of the problems in FFA with an annual maxima (AM) modeling approach is deciding how to handle smaller discharge values (outliers) in the selected AM flood series at a given station. The objective of this paper is to explore how the practice of censoring (which involves adjusting for smaller discharge values in FFA) affects flood quantile estimates in FFA. In this regard, two commonly used probability distributions, log-Pearson type 3 (LP3) and generalized extreme value distribution (GEV), are used. The multiple Grubbs and Beck (MGB) test is used to identify low-flow outliers in the selected AM flood series at 582 Australian stream gauging stations. It is found that censoring is required for 71% of the selected stations in using the MGB test with the LP3 distribution. The differences in flood quantile estimates between LP3 (with MGB test and censoring) and GEV distribution (without censoring) increase as the return period reduces. A modest correlation is found (for South Australian catchments) between censoring and the selected catchment characteristics (correlation coefficient: 0.43), with statistically significant associations for the mean annual rainfall and catchment shape factor. The findings of this study will be useful to practicing hydrologists in Australia and other countries to estimate design floods using AM flood data by FFA. Moreover, it may assist in updating Australian Rainfall and Runoff (national guide).

We propose an outlier-resilient Gaussian process regression (GPR) model supported by support vector machine regression (SVMR) for kinetic profile inference. GPR, being a non-parametric regression using Bayesian statistics, has advantages in that it imposes no constraints on profile shapes and can be readily used to integrate different kinds of diagnostics, while it is vulnerable to the presence of even a single outlier among a measured dataset. As an outlier classifier, an optimized SVMR is developed based only on the measurements. Hyper-parameters of the developed GPR model with informative prior distributions are treated in two different ways, i.e. maximum a posteriori (MAP) estimator and marginalization using a Markov Chain Monte Carlo sampler. Our SVMR-supported GPR model is applied to infer ion temperature T _i profiles using measured data from the KSTAR charge exchange spectroscopy system. The GPR-inferred T _i profiles with and without an outlier are compared and show prominent improvement when the outlier is removed by the SVMR. T _i profiles inferred with the MAP estimator and the marginalization scheme are compared. They are noticeably different when observation uncertainties are not small enough, and the marginalization scheme generally provides a smoother profile.

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.

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.

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]

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 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]

Outliers are data points or observations that stand out significantly from the rest of the group in terms of size or frequency. They are also referred to as "abnormal data". Before fitting a forecasting model, outliers are often eliminated from the data set, or if not removed, the forecasting model is altered to account for the presence of outliers. The first scenario covered in the study is the detection of outliers when the parameters have been established. Second, where there are unidentified parameters. This article mentions a number of causes for outlier correction and detection in time series analysis and forecasting. For the objective of the study, a time series of the volume of water entering the Dohuk dam reservoir in Dohuk city was used. The study arrived at the following conclusions after conducting their research: first, whenever the critical value increased, the value of residual standard error (with outlier adjustment) increased. Second, the quantity of outlier values dropped each time the critical value was raised. Third, forecasts with outlier correction perform better than forecasts without outlier adjustment when outliers are present. [ABSTRACT FROM AUTHOR]

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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]

The fraction nonconforming, which follows a binomial distribution, is one of the most critical quality characteristics of attribute processes. In addition, the multivariate binomial process plays an important role in industries due to the enormous diversity of quality characteristics. A multivariate binomial process is deemed out of control when it triggers a signal in a multivariate statistical process control chart. However, it is difficult to determine which quality characteristic triggers the nonconforming shift. In contrast to most current studies that identify the contributors of shifts in multivariate normal processes, this study discusses the contributors of fraction nonconforming shifts in multivariate binomial processes. First, a test that can be applied to detect outliers in a multivariate binomial distribution is proposed. In addition, a stepwise test method that can be used to determine the contributors of fraction nonconforming shifts in a multivariate binomial process is then developed. Numerical results indicate that the method proposed is effective in determining the contributors of fraction nonconforming shifts for a multivariate binomial process. [ABSTRACT FROM AUTHOR]

Multivariate time series data, often, modeled using vector autoregressive moving average (VARMA) model. But presence of outliers can violates the stationary assumption and may lead to wrong modeling, biased estimation of parameters and inaccurate prediction. Thus, detection of these points and how to deal properly with them, especially in relation to modeling and parameter estimation of VARMA model is necessary. By detecting outliers, their effect can be eliminated over time and we obtain the modified data. Using this modified data, the proper estimates of the VARMA model are obtained which have the least effect on the outliers. On the other hand, detect of outliers is important in finding an external event over time. For example, by finding outliers in river water monitoring data, flood times can be obtained. The parameter estimation of VAR model is less time consuming than VARMA. On the other hand, under condition of invertibility, VARMA models could be approximated by VAR(p) for large p. Therefore, we use this model to fit and investigate the data generated from VARMA models that contaminated by outliers. Multivariate observations of time series may be contaminated with different types of outliers. However, the effect of different types of outliers in multivariate and univariate case is different, and this observation must be assessed by multivariate approach. In this research, we use a Genetic Algorithm (GA) to develop a procedure for detecting different types of outliers (additive, innovation, level shift and temporary change outliers) in a multivariate time series. GA detects outlier location which minimizes Akaike-like Information Criterion (AIC) and we try to "minimize the number of outliers" and "maximize the likelihood function". GA is a numerical optimization algorithm whose idea is based on natural selection and natural genetics. This algorithm does not require strong assumptions to obtain the optimal value of a function and has the ability to search for the optimal solution from a space with several local optimal. That is, for example, if a function has several relative maxima, GA finds the absolute maximum of this function as well. For minimization of a function, GA operates by first generating, at random or optionally, several minimal solutions to the function that this set of solutions called the initial population and each solution as a chromosome. Then, using reproductive operators, we combine chromosomes and make a jump into them. If the function of newly produced chromosomes is lower than the previous chromosomes, these chromosomes can be added to the initial population or replaced with chromosomes with less function in this population. This process is repeated until convergence occurs or the end number of itteration obtained. Furthermore, we introduce another method of detecting outliers, the Tsay Pena and Pankratz (TPP) method. TPP uses some test statistics based on outliers size and VAR parameters. This method detects outliers in three stages. In stage I, it detects one by one outliers and remove their effects. Iteration done until no outlier found. In stage II, for detected outlier in stage I, the estimation of outliers effects are obtained simultaneously. Then, outliers with insignificant effects are removed. The VAR parameters re-estimated based on modified series of this stage. In stage III, we repeated stage I and II with new VAR parameter estimation. In each iteration of TPP, an outlier is detected and the effect of this outlier is removed from series (modified series). Then the parameter estimation is obtained from the modified series and the next outlier detection is continued using these estimates. This may lead to biased estimates and wrong detection of the next outlier point. In other words, in the TPP method, one detected outlier hides another outlier (masking), or one detected outlier reveals the usual observation as an outlier (swamping). This method often mis-detects the type of outliers. But in each iteration of GA, a random pattern of outliers (for testing) is first generated and a temporary modified series is obtained by removing effect of this pattern from series. Then the estimation of the parameters obtained and the detection of this pattern is tested. This work reduces the effect of the previously identified outliers on the full pattern of the outliers. In fact, if the random pattern of all outliers is correctly generated, almost effect of all of them will be eliminated in the modified series. Therefore using this temporary modified series, the GA obtained more accurate estimates and detected outliers more accurately. The simulation results confirm the validity of the GA method and the percentage of correct outlier detection in this method is higher than the TPP method. GA, of course, needs more time to calculate. Also, although the VAR model is used in both detection methods, the percentage of correct outlier detection in the VARMA model data is similar to the VAR model. Gas-furnace data were analyzed and modeled and it was determined that GA and TPP methods detected similar outliers. Fitting the VAR(6) model on these data shows that the variance of input gas error in modified data of GA to TPP is reduced by 17% and the variance of carbon dioxide error in the modified data of GA to TPP reduced by 43%.

Structural Equation Modeling is a statistical methodology commonly used in the social and administrative sciences and all other. In this research, the researcher made a comparison between methods of estimation Unweighted Least Squares with Mean and Variance Adjusted( ULSMV) and weighted Least Squares with Mean and Variance Adjusted (WLSMV). When we have a five-way Likert scale, the data is treated as ordinal using the polychoric matrix as inputs for the weighted methods with robust corrections. With robust standard errors ULSMV and WLSMV.No study compared these methods and the impact of outliers on them. where a robust algorithm is proposed to clean the data from the outlier, as this proposed algorithm calculates the robust correlation matrix Reweighted Fast Consistent and High Breakdown (RFCH), which consists of several steps and has been modified by taking the clean data before calculating the RFCH correlation matrix, where these data are data clean from outlier to add in the methods and to calculate a correlation matrix for each method where the purpose is to keep the ordinal data to calculate the polychoric matrix, which is robust to the violation of the assumption of normal distribution.By conducting a simulation experiment on different sample sizes and the degree of distribution to observe the accuracy of the proposed method for obtaining clean data. On methods ULSMV and WLSMV before and after the treatment process by calculating the absolute bias rate For the standard errors and the estimated parameters, in addition to studying the extent of their effect on the quality of fit indicators for each of the chi-square index, Comparative fit index (CFI), Tucker-Lewis Index (TLI), and Root-Mean-Squared-Error-of Approximation( RMSEA), Standardized Root Mean square Residual (SRMR), , with the robust corrections in the chi-square index for each of the methods WLSMV and ULSMV the accuracy of the proposed.

Summary: The method of maximum likelihood flops when there is linear dependency (multicollinearity) and outlier in the generalized linear models. In this study, we combined the ridge estimator with the transformed M‐estimator (MT) and the conditionally unbiased bounded influence estimator (CE). The two new estimators are called the robust MT estimator and Robust‐CE. A Monte Carlo study revealed that the proposed estimators dominate for the generalized linear models with Poisson response and log link function. The real‐life application results support the simulation outcome. [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]

Collecting time-series receive signal strength (RSS) observations and averaging them is a common method for dealing with RSS fluctuation. However, outliers in the time-series observations affect the averaging process, making this method less efficient. The Z-score method based on the median absolute deviation (MAD) scale estimator has been used to detect outliers, but it is only efficient with symmetrically distributed observations. Experimental analysis has shown that time-series RSS observations can have a symmetric or asymmetric distribution depending on the nature of the environment in which the measurement was taken. Hence, the use of the Z-score method with the MAD scale estimator will not be efficient. In this paper, the  S n  scale estimator is proposed as an alternative to MAD to be used with the Z-score method in detecting outliers in time-series RSS observations. Performance comparison using an online RSS dataset shows that the Z-score with MAD and  S n  as scale estimators falsely detected about 50% and 13%, respectively, of the RSS observations as outliers. Furthermore, the average absolute RSS median deviations between raw and outlier-free observations are 3 dB and 0.25 dB, respectively, for the MAD and  S n  scale estimators, corresponding to a range error of about 2 m and 0.5 m. [ABSTRACT FROM AUTHOR]

In surface roughness measurements, the presence of spikes in the primary profile can disable a normal filter from extracting the shape and waviness components. The robust spline filter (RSF) has been proposed to solve this problem. However, because ISO 16610-32, an RSF standard, has a significant deficiency, it was withdrawn in the Technical Specifications stage. The other proposed RSFs only show the method and a few examples with normal roughness and spike. Therefore, because the characteristics of each RSF have not been clarified, which RSF should be used for which primary profiles is not clear. This is one of the reasons for the delay in establishing ISO. In addition, the measurement sites need the establishment of robust spline filter selection guidelines. Therefore, in this paper, the characteristics of RSFs are clarified. Additionally, the points to be considered in using RSFs are summarized. Finally, the filter that should be selected for the specific application is identified. These results are expected to contribute to the widespread use of RSFs in the industrial world, avoid confusion at the measurement site, and help establish a new RSF standard. [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]

This paper proposes a recursive filter for discrete-time linear dynamic systems subject to output outliers or heavy-tailed noises. First, we introduce a weight matrix in the conventional MAP estimation. It is shown that this matrix plays an influential role in the innovation whitening and asymptotic variance of the modified MAP estimation and, consequently, can be used in outlier detection. Then, we propose two different constrained optimization problems to obtain this weight. These constraints, stemming from environmental noise characteristics, help to obtain the weight matrix more precisely, which increases the filtering performance significantly. In the first approach, we introduce a convex optimization problem to minimize the estimation upper bound of the error covariance matrix. The second approach converts the modified MAP estimation to a min–min optimization problem with a concave cost function. Consequently, to reduce the effect of outliers in estimation, a semidefinite program (SDP) is proposed for outlier detection. At last, simulation results show the effectiveness and verify the performance of the proposed filter for dynamic systems in the presence of measurement outliers. • An improved Kalman filter is proposed for linear systems subject to measurement outliers. • The proposed filter is obtained by introducing a weight matrix into the MAP structure. • The weight affects both innovation whitening and error distribution's asymptotic variance. • The weight matrix is obtained by minimizing the covariance of the estimation error. • To decreases the outliers effect, an SDP is proposed for outlier detection. [ABSTRACT FROM AUTHOR]

Some outliers might be undetected even after using the outlier detection procedures. In this paper, an accommodation procedure for such undetected outliers is discussed, which is done by robust modified maximum likelihood (MML) estimation of Type II censored sample for a Spatial Autoregressive (SAR) model. A new method is proposed for determining the number of observation to be censored to obtain robust parameter estimates. Next, a Monte-Carlo simulation study is carried to assess the robustness of the obtained MML estimators for both the normal and the contaminated normal models. Also, asymptotic variances, covariances, and distributional results are obtained for the MML estimators. [ABSTRACT FROM AUTHOR]

Globally, food and medicinal plants have been documented, but their use patterns are poorly understood. Useful plants are non-random subsets of flora, prioritizing certain taxa. This study evaluates orders and families prioritized for medicine and food in Kenya, using three statistical models: Regression, Binomial, and Bayesian approaches. An extensive literature search was conducted to gather information on indigenous flora, medicinal and food plants. Regression residuals, obtained using LlNEST linear regression function, were used to quantify if taxa had unexpectedly high number of useful species relative to the overall proportion in the flora. Bayesian analysis, performed using BETA.INV function, was used to obtain superior and inferior 95% probability credible intervals for the whole flora and for all taxa. To test for the significance of individual taxa departure from the expected number, binomial analysis using BINOMDIST function was performed to obtain p-values for all taxa. The three models identified 14 positive outlier medicinal orders, all with significant values (p < 0.05). Fabales had the highest (66.16) regression residuals, while Sapindales had the highest (1.1605) R-value. Thirty-eight positive outlier medicinal families were identified; 34 were significant outliers (p < 0.05). Rutaceae (1.6808) had the highest R-value, while Fabaceae had the highest regression residuals (63.2). Sixteen positive outlier food orders were recovered; 13 were significant outliers (p < 0.05). Gentianales (45.27) had the highest regression residuals, while Sapindales (2.3654) had the highest R-value. Forty-two positive outlier food families were recovered by the three models; 30 were significant outliers (p < 0.05). Anacardiaceae (5.163) had the highest R-value, while Fabaceae had the highest (28.72) regression residuals. This study presents important medicinal and food taxa in Kenya, and adds useful data for global comparisons. [ABSTRACT FROM AUTHOR]

As a relatively advanced method, the subspace clustering algorithm by block diagonal representation (BDR) will be competent in performing subspace clustering on a dataset if the dataset is assumed to be noise-free and drawn from the union of independent linear subspaces. Unfortunately, this assumption is far from reality, since the real data are usually corrupted by various noises and the subspaces of data overlap with each other, the performance of linear subspace clustering algorithms, including BDR, degrades on the real complex data. To solve this problem, we design a new objective function based on BDR, in which l2,1 norm of the reconstruction error is introduced to model the noises and improve the robustness of the algorithm. After optimizing the objective function, we present the corresponding subspace clustering algorithm to pursue a self-expressive coefficient matrix with a block diagonal structure for a noisy dataset. An affinity matrix is constructed based on the coefficient matrix, and then fed to the spectral clustering algorithm to obtain the final clustering results. Experiments on several artificial noisy image datasets show that the proposed algorithm has robustness and better clustering performance than the compared algorithms. [ABSTRACT FROM AUTHOR]

To circumvent the problem of multicollinearity in regression models, a ridge-type estimator is recently proposed in the literature, which is named as the Kibria-Lukman estimator (KLE). The KLE has better properties than the conventional ridge regression estimator. However, the presence of outliers in the data set may have some adverse effects on the KLE. To address this issue, the present article proposes a robust version of the KLE based on the M-estimator. This article also proposes some robust methods to estimate the shrinkage parameter k. The Monte Carlo simulation study and a real-life data is used to gauge the performance of the proposed methods where the mean squared error is used as the evaluation criterion. The numerical results witness the supremacy of the proposed estimator in the presence of outliers. [ABSTRACT FROM AUTHOR]