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Conventional direction-of-arrival (DOA) estimation algorithms are sensitive to array imperfections and outliers, making it challenging to realize accurate estimates in real applications. Facing the challenge, we propose a robust variational Bayesian inference based DOA estimation algorithm using the linear sparse array in this paper, where accurate DOA estimation with increased number of degrees of freedom (DOFs) is realized. With the mixture of von Mises model, a prior-grid scheme is further proposed to alleviate the computational burden introduced by the Bayesian variational inference framework. Since there is no restriction on the prior knowledge of the number of sources, the proposed algorithm is friendly to actual scenarios. Simulation results demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This article concentrated on the diagnostics measures to identify outlier observations using the restricted Liu estimator (RLE) of the vector of parameters in linear measurement error models (LMEMs). To this end, the mean shift outlier model (MSOM) has been determined to detect outliers and the corrected score test statistic. Also, using the RLE, generalized leverage matrices were constructed to identify high-leverage observations. Furthermore, to identify the influential observations, different statistics that are single-case deletion methods are used based on RLE. Finally, a simulation study and a real example are provided to illustrate the performance of the influence measures. [ABSTRACT FROM AUTHOR]

Between 2010 and 2020 in the European Union, 30% of road accidents resulted in the death of a pedestrian or a cyclist. Accidents of unprotected pedestrians and cyclists are the reason why it is essential to introduce road safety measures. In our paper, we identify and rank black spots using an innovative reactive approach based on statistics. We elaborate on the mathematical methodological considerations through the processing of real-life empirical data in a Matlab environment. The applied black-spot analysis is based on a Kernel density estimate method, and the importance of the kernel functions and bandwidth are elaborated. Besides, special attention is devoted to the distorting effect of annual average daily traffic. The result of our research is a new methodology by which the real locations of the examined black spots can be determined. Furthermore, the boundaries of the critical sections and the extent of the formation of black spots can be determined by the introduced mathematical methods. With our innovative model, the black spots can be ranked, and the locations having the highest potential for improvement can be identified. Accordingly, optimal measures can be determined considering social-economic and sustainability aspects. [ABSTRACT FROM AUTHOR]

In the field of surface roughness measurement, the development and standardization of robust filters have been progressing in recent years. However, there is no existing filter that satisfies all three requirements of a robust filter (robustness, high-speed processing, and compatibility with the GF). In this paper, a novel robust filter based on L2-norm, L2GF, is proposed. The L2GF shows more robustness than ISO16610-31 and FMGF. The L2GF is the fastest among the robust filters. F0B7 The L2GF, a new robust filter using L2-norm, is proposed. F0B7 The L2GF shows more robustness than ISO16610-31 and FMGF. F0B7 The L2GF is the fastest among the robust filters. [ABSTRACT FROM AUTHOR]

Antalya 2 tide gauge (TG) station is located on the coast of Turkey within the Eastern Mediterranean Sea. Relative sea level trends 6.0 ± 1.5 and 6.44 ± 0.45 mm/year over 1985–2009 at Antalya 2 TG are different from the trend (1.6 ± 1.5 mm/year over 1935–1977) at Antalya TG within 10 km. In order to investigate this trend discrepancy, the monthly mean series at Antalya 2 TG is re-analyzed for offsets, outliers and trend estimation. The Zivot-Andrews method and the Q p outlier test result in one offset at 1994.0417 year with magnitude of 71.24 ± 13.48 mm and nine outliers. The series, corrected for the offset and outliers, de-seasonalized and filled for missed points, is identified as trend-stationary process and analyzed for trend estimation by various models. The optimal model providing the lowest Akaike Information Criteria is polynomial linear trend with multiplicative seasonal Autoregressive Moving Average (ARMA(2,0)x(1,0)12). The estimated relative sea level trend by the optimal model is 1.77 ± 0.65 mm/year. The large trend discrepancy at Antalya 2 TG is accounted for by one offset primarily (∼71%) and nine outliers (∼3%). [ABSTRACT FROM AUTHOR]

The weighted total least-squares (WTLS) estimate for the partial EIV (PEIV) model is very susceptible to outliers. In view of outliers in observations and coefficient matrix, a robust estimate of WTLS which makes using of iteratively reweighted technique (IRTLS) for the PEIV model is developed by combining a new two-step iterated algorithm of the WTLS estimate with the sensitivity-analysis based on the robust M-estimation. The uniformly most powerful test statistics are constructed to determine the down-weighting factor and the variance of unit weight is estimated by least median squares (LMS) method possessing high break-down point. Depended on the PEIV model, two different down-weighting schemes are presented. In the first scheme down-weighting is only implemented for the coefficient matrix and not for observations when the elements of the coefficient matrix are estimated, and the second scheme is contrary. The most attractive aspect is that the suggested computational formulae are the same with the traditional robust least-squares (LS) methods. The two-dimensional affine transformation and liner fitting example are analyzed, and some comparisons are performed with different available weight functions. The proposed approach (Scheme 1) proves to be a powerful tool in detecting outliers for the PEIV model. [ABSTRACT FROM AUTHOR]

This paper investigates the navigational performance of Global Positioning System (GPS) using the variational Bayesian (VB) based robust filter with interacting multiple model (IMM) adaptation as the navigation processor. The performance of the state estimation for GPS navigation processing using the family of Kalman filter (KF) may be degraded due to the fact that in practical situations the statistics of measurement noise might change. In the proposed algorithm, the adaptivity is achieved by estimating the time-varying noise covariance matrices based on VB learning using the probabilistic approach, where in each update step, both the system state and time-varying measurement noise were recognized as random variables to be estimated. The estimation is iterated recursively at each time to approximate the real joint posterior distribution of state using the VB learning. One of the two major classical adaptive Kalman filter (AKF) approaches that have been proposed for tuning the noise covariance matrices is the multiple model adaptive estimate (MMAE). The IMM algorithm uses two or more filters to process in parallel, where each filter corresponds to a different dynamic or measurement model. The robust Huber's M-estimation-based extended Kalman filter (HEKF) algorithm integrates both merits of the Huber M-estimation methodology and EKF. The robustness is enhanced by modifying the filter update based on Huber's M-estimation method in the filtering framework. The proposed algorithm, referred to as the interactive multi-model based variational Bayesian HEKF (IMM-VBHEKF), provides an effective way for effectively handling the errors with time-varying and outlying property of non-Gaussian interference errors, such as the multipath effect. Illustrative examples are given to demonstrate the navigation performance enhancement in terms of adaptivity and robustness at the expense of acceptable additional execution time. [ABSTRACT FROM AUTHOR]

This cross-sectional descriptive study utilized spatial statistics to test for hot/cold spots of SNAP under-participation in Texas. First, we estimated the percentage of SNAP eligible but not enrolled (EBNE) households, by census tract. Next, we utilized the Anselin's Local Moran's I statistic to identify statistically significant hot/cold spots of SNAP EBNE tracts throughout Texas and in Harris County. We found that hot spot census tracts had significantly higher densities, income levels, and lower poverty rates than other census tracts. Using spatial statistics to identify hot/cold spots of SNAP under-participation offers a novel method for understanding area SNAP utilization. [ABSTRACT FROM AUTHOR]

Abstract Background Responses of subnational government units are crucial in the containment of the spread of pathogens in a country. To mitigate the impact of the COVID-19 pandemic, the Philippine national government through its Inter-Agency Task Force on Emerging Infectious Diseases outlined different quarantine measures wherein each level has a corresponding degree of rigidity from keeping only the essential businesses open to allowing all establishments to operate at a certain capacity. Other measures also involve prohibiting individuals at a certain age bracket from going outside of their homes. The local government units (LGUs)–municipalities and provinces–can adopt any of these measures depending on the extent of the pandemic in their locality. The purpose is to keep the number of infections and mortality at bay while minimizing the economic impact of the pandemic. Some LGUs have demonstrated a remarkable response to the COVID-19 pandemic. The purpose of this study is to identify notable non-pharmaceutical interventions of these outlying LGUs in the country using quantitative methods. Methods Data were taken from public databases such as Philippine Department of Health, Philippine Statistics Authority Census, and Google Community Mobility Reports. These are normalized using Z-transform. For each locality, infection and mortality data (dataset Y) were compared to the economic, health, and demographic data (dataset X) using Euclidean metric d=(x−y)2, where x∈X and y∈Y. If a data pair (x,y) exceeds, by two standard deviations, the mean of the Euclidean metric values between the sets X and Y, the pair is assumed to be a ‘good’ outlier. Results Our results showed that cluster of cities and provinces in Central Luzon (Region III), CALABARZON (Region IV-A), the National Capital Region (NCR), and Central Visayas (Region VII) are the ‘good’ outliers with respect to factors such as working population, population density, ICU beds, doctors on quarantine, number of frontliners and gross regional domestic product. Among metropolitan cities, Davao was a ‘good’ outlier with respect to demographic factors. Conclusions Strict border control, early implementation of lockdowns, establishment of quarantine facilities, effective communication to the public, and monitoring efforts were the defining factors that helped these LGUs curtail the harm that was brought by the pandemic. If these policies are to be standardized, it would help any country’s preparedness for future health emergencies.

BACKGROUND Composition analysis of copper concentrate is an important method to determine its quality, especially the analysis of the main element copper. Currently, the main analytical methods for the determination of copper content in copper concentrate include iodometry, inductively coupled plasma-optical emission spectrometry (ICP-OES), flame atomic absorption spectrometry (FAAS), X-ray fluorescence spectrometry (XRF), and electrolytic gravimetric methods. OBJECTIVES To ensure the uniformity, accuracy, and reliability of the standard values, interlaboratory comparison activities for the determination of copper, magnesium, lead, and zinc in copper concentrate were organized. METHODS Through the statistical analysis of the test results of the participating laboratories, the technical level and ability of the participating laboratories in the determination of copper, magnesium, lead, and zinc in copper concentrates were evaluated. RESULTS The results showed that most laboratory results were satisfactory, the satisfaction rate of copper in the copper concentrate was 92.9%, and the average satisfaction rate of copper, magnesium, lead, and zinc was 89.0%. The outliers in a few laboratories were mainly attributed to sample pretreatment, lack of understanding and mastering of the analytical methods by the testing personnel, and other related factors, such as the instrument status. CONCLUSIONS Because of incomplete sample decomposition, the alkali leaching method could not be used for pretreatment of copper concentrate. Additionally, the alkali fusion method is not recommended for copper concentrate pretreatment because of its complicated process and matrix interference. Acid dissolution titration is preferred as a pretreatment method for the determination of copper in the copper concentrate. The acid-dissolution ICP-OES method for simultaneous determination of Cu, Mg, Pb, and Zn in the copper concentrate is efficient and rapid with satisfactory results. However, further experiments are required to investigate their stabilities.

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.

Heavy metal concentrations of Cd, As, Pb, Cu, Cr, and Hg were investigated for 86 soil samples in Jinzhou near the Bohai Sea in China, in order to identify what anthropological activities influenced their distribution levels. Ordinary cokriging (OCK) was utilized to map six heavy-metal distributions by incorporating their main environmental influencers. The resultant p values for the six OCK mapping models of 0–2.78% indicated good statistical significance of the models, and the relative mean absolute errors of 4.82–12.53% and relative root mean square errors of 6.23–18.21% indicated allowable predication precision for their concentrations. The contamination distributions by OCK mapping were then graded based on the standards of the China National Environmental Monitoring Center and the Chinese Environmental Protection Administration, which showed that Cu and As contaminations in parts of this area were over the natural level but not polluted, Cr contamination was omnipresent over the natural level in this area and even reached the polluted level in parts of this area. The graded contamination maps that were overlapped with land-use maps and Google satellite maps, as well as the verifications reported in literatures, enabled correlations of the different contamination levels of As, Cu, and Cr with human activities. Resultantly, it indicated that As and Cu contamination over the natural level may be related to agricultural planting and aquacultural activities along the coast of Bohai Bay, with the contaminants transported via watercourses; Cr contamination over the natural level may have been due to vehicle emissions; and, Cr pollution may have been from steel manufacturing and geochemical factories [ABSTRACT FROM AUTHOR]

The extraction of novel information from omics data is a challenging task, in particular, since the number of features (e.g. genes) often far exceeds the number of samples. In such a setting, conventional parameter estimation leads to ill-posed optimization problems, and regularization may be required. In addition, outliers can largely impact classification accuracy. Here we introduce ROSIE, an ensemble classification approach, which combines three sparse and robust classification methods for outlier detection and feature selection and further performs a bootstrap-based validity check. Outliers of ROSIE are determined by the rank product test using outlier rankings of all three methods, and important features are selected as features commonly selected by all methods. We apply ROSIE to RNA-Seq data from The Cancer Genome Atlas (TCGA) to classify observations into Triple-Negative Breast Cancer (TNBC) and non-TNBC tissue samples. The pre-processed dataset consists of 16 , 600 genes and more than 1 , 000 samples. We demonstrate that ROSIE selects important features and outliers in a robust way. Identified outliers are concordant with the distribution of the commonly selected genes by the three methods, and results are in line with other independent studies. Furthermore, we discuss the association of some of the selected genes with the TNBC subtype in other investigations. In summary, ROSIE constitutes a robust and sparse procedure to identify outliers and important genes through binary classification. Our approach is ad hoc applicable to other datasets, fulfilling the overall goal of simultaneously identifying outliers and candidate disease biomarkers to the targeted in therapy research and personalized medicine frameworks. [ABSTRACT FROM AUTHOR]

The idea of the research is to fortify the path coefficients in the structural equations model through the use of the robust correlation coefficients. Three methods of the robust correlation were compared in addition to the Pearson correlation. A comparison has been made between the proposed method and the traditional method through a causal model proposed by the researchers. Several simulation scenarios have been applied in the experimental aspect. In order to test the efficiency of the generated models, samples of different sizes (n=20,100,1000) were used. Furthermore, the explanatory variables in the causal model were contaminated with different contamination percentages. Path parameters were estimated using the four-fold correlation matrix and the results were compared through some statistical criteria. The study concluded that the pathway coefficients method using the Robust Correlation has given the best results compared to the traditional methods. [ABSTRACT FROM AUTHOR]

• 8 methods for identification/exclusion of pathological subpopulations were compared. • The methods were assessed against 256 simulated mixed population scenarios. • Kosmic had most scenarios within error limits of +/-5%. • This was followed by van der Loo 1 and Tukey's rule. • Method performance declined at high proportion/spread of pathological subpopulation. Indirect reference intervals and biological variation studies heavily rely on statistical methods to separate pathological and non-pathological subpopulations within the same dataset. In recognition of this, we compare the performance of eight univariate statistical methods for identification and exclusion of values originating from pathological subpopulations. The eight approaches examined were: Tukey's rule with and without Box-Cox transformation; median absolute deviation; double median absolute deviation; Gaussian mixture models; van der Loo (Vdl) methods 1 and 2; and the Kosmic approach. Using four scenarios including lognormal distributions and varying the conditions through the number of pathological populations, central location, spread and proportion for a total of 256 simulated mixed populations. A performance criterion of ± 0.05 fractional error from the true underlying lower and upper reference interval was chosen. Overall, the Kosmic method was a standout with the highest number of scenarios lying within the acceptable error, followed by Vdl method 1 and Tukey's rule. Kosmic and Vdl method 1 appears to discriminate better the non-pathological reference population in the case of log-normal distributed data. When the proportion and spread of pathological subpopulations is high, the performance of statistical exclusion deteriorated considerably. It is important that laboratories use a priori defined clinical criteria to minimise the proportion of pathological subpopulation in a dataset prior to analysis. The curated dataset should then be carefully examined so that the appropriate statistical method can be applied. [ABSTRACT FROM AUTHOR]

Classification of imbalanced data is an important challenge in current research. Sampling is an important way to solve the problem of imbalanced data classification, but some traditional sampling algorithms are susceptible to outliers. Therefore, an iF-ADASYN sampling algorithm is proposed in this paper. First, based on the ADASYN algorithm, we introduce the isolation Forest algorithm to overcome its vulnerability to outliers. Then, a calculation method of anomaly index which can delete outliers accurately of minority data is presented. The experimental results of four UCI public imbalanced datasets show that the algorithm can effectively improve the accuracy of the minority class, and increase the stability. In the real thrombus dataset, the AUC value of the iF-ADASYN algorithm is more significant than that of SMOTE and ADASYN algorithms, and the recognition rate of patients with thrombosis increased by 20%. The iF-ADASYN algorithm obtains better resistance to outliers than the original ADASYN algorithm. Meanwhile, it improves the accuracy of minority class decision boundary region division. [ABSTRACT FROM AUTHOR]

Point cloud voxelization methods are considered as significant topic in 3D computer vision field with variant relevant applications. The massive amount of point cloud data depicted a 3D model are extremely required considerable data storage along with processing time and ordinarily corrupted by outliers and noisy data. In this paper, we propose a new scheme for 3D point cloud clustering based on voxelization without pre-knowledge. Mathematical representation of 3D model is adopted in our framework in order to optimize the point cloud and remove the outliers with preserving the main characteristics of the whole 3D model shape. First, we segment the point cloud data points into uniform rectangular blocks (voxels) based on spatial 3D coordinates of vertices. Subsequence, each voxel is handled as cluster with gravity mass as voxel center and specifies the neighboring points to calculate cluster density. Then, the outliers at each voxel are detected based on geometry features of clusters and adjacency matrix representation. Finally, we reconstruct each voxel by removing the detected outliers. The proposed method is validated on different point cloud models with variant size. The experimental results highlighted that the new clustering-based voxelization method is suitable for real time applications due to its lower processing time. We have got a reduction rate close to 46.8% of the original 3D model size and precision percentage 96 %, which exhibits a higher noise removal rate. [ABSTRACT FROM AUTHOR]

Background: We established high-sensitivity cardiac troponin I (hsTnI) 99th percentile upper reference limits (URLs) for the Centaur XPT High-Sensitivity Troponin I assay (Centaur hsTnI; Siemens, Erlangen, Germany) and Atellica IM High-Sensitivity Troponin I assay (Atellica hsTnI; Siemens) and assessed the effect of outlier elimination. Methods: The reference population comprised 380 men and 387 women, satisfying the strict systematic reference population criteria. After reference population verification by the N-terminal pro-B-type natriuretic peptide (NT-proBNP) assay, 99th percentile URLs for Centaur hsTnI and Atellica hsTnI were calculated before and after outlier elimination. Results: The 99th percentile URL for Centaur hsTnI was 60.4 (men, 74.7; women, 57.5) ng/L and that for Atellica hsTnI was 59.6 (men, 75.2; women, 55.1) ng/L. After the elimination of 61 (8.0%) outlier samples in Centaur hsTnI and 58 (7.6%) in Atellica hsTnI, the 99th percentile URLs were 13.5 ng/L (men, 15.3 ng/L; women, 11.9 ng/L) and 13.4 ng/L (men, 15.5 ng/L; women, 12.9 ng/L), respectively, significantly lower than those before outlier elimination. The CVs at the 99th percentile URLs were 5.2% and 3.5%, respectively. The measurable fractions among the reference population were 91.5% and 93.4%, respectively. Performance evaluation of Atellica B-type natriuretic peptide (BNP), Atellica NT-proBNP, Centaur hsTnI, and Atellica hsTnI showed outstanding results. Conclusions: The Korean hsTnI 99th percentile URLs calculated in this study were significantly lower after outlier elimination than before. Centaur hsTnI and Atellica hsTnI meet the “Guideline acceptable” and “Level 3 (second generation, high sensitivity)” requirements, satisfying international standards. [ABSTRACT FROM AUTHOR]

Singular spectrum analysis (SSA) is a nonparametric method for separating time series data into a sum of small numbers of interpretable components (signal + noise). One of the steps of the SSA method, which is referenced to Embedding, is extremely sensitive to contamination of outliers which are often founded in time series analysis. To reduce the effect of outliers, SSA based on Singular Spectrum Decomposition (SSD) method is proposed. In this article, the ability of SSA based on SSD and basic SSA are compared in time series reconstruction in the presence of outliers. It is noteworthy that the matrix norm used in Basic SSA is the Frobenius norm or L2-norm. There is a newer version of SSA that is based on L1-norm and called L1-SSA. It was confirmed that L1-SSA is robust against outliers. In this regard, this research is also introduced a new version of SSD based on L1-norm which is called L1-SSD. A wide empirical study on both simulated and real data verifies the efficiency of basic SSA based on SSD and L1-norm in reconstructing the time series where polluted by outliers. [ABSTRACT FROM AUTHOR]

Outliers identification is essential in data analysis since it can make wrong inferential statistics. This study aimed to compare the performance of Boxplot, Generalized Extreme Studentized Deviate (Generalized ESD), and Sequential Fences method in identifying outliers. A published dataset was used in the study. Based on preliminary outlier identification, the data did not contain outliers. Each outlier detection method's performance was evaluated by contaminating the original data with few outliers. The contaminations were conducted by replacing the two smallest and largest observations with outliers. The analysis was conducted using SAS version 9.2 for both original and contaminated data. We found that Sequential Fences have outstanding performance in identifying outliers compared to Boxplot and Generalized ESD. [ABSTRACT FROM AUTHOR]

Abstract 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 protein’s 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.

A mais recente versão da teoria da confiabilidade tem sido utilizada para descrever a capacidade de um sistema de medição em detectar, identificar e remover outliers a um certo nível de probabilidade. Entretanto, as aplicações desta teoria têm sido direcionadas para redes simuladas de nivelamento. Aqui, por outro lado, aplicamos a teoria no contexto de redes baseadas nos sistemas de posicionamento por satélites GNSS (Global Navigation Satellite System), a partir de dados reais coletados em campo. Testamos se as covariâncias entre as componentes da linha base têm efeito sobre a confiabilidade. Verificamos que as covariâncias entre as componentes da linha base aumentam a taxa de sucesso na identificação de outlier e, portanto, aumentam a confiabilidade da rede. O menor outlier identificável – ao nível de 80% de correta identificação – teve uma redução média de ~30% para as componentes ΔX e ΔY, e ~14% para ΔZ em comparação ao cenário com covariâncias nulas. O aumento do nível de significância melhora a confiabilidade em ambos os cenários (covariâncias nulas e não-nulas) na mesma proporção. Porém, para altos níveis de significância (α > 0,1) e sistemas com boa redundância (ri > 0,5), a confiabilidade para um modelo estocástico com covariâncias nulas se aproxima do caso em que as covariâncias não são nulas. Na ausência de um modelo estocástico mais realista (covariâncias não-nulas) e para sistemas com boa redundância local (ri > 0,5), deve-optar por regiões críticas maiores ( k < 2,8).

Abstract Background RNA sequencing (RNA-Seq) has been widely applied in oncology for monitoring transcriptome changes. However, the emerging problem that high variation of gene expression levels caused by tumor heterogeneity may affect the reproducibility of differential expression (DE) results has rarely been studied. Here, we investigated the reproducibility of DE results for any given number of biological replicates between 3 and 24 and explored why a great many differentially expressed genes (DEGs) were not reproducible. Results Our findings demonstrate that poor reproducibility of DE results exists not only for small sample sizes, but also for relatively large sample sizes. Quite a few of the DEGs detected are specific to the samples in use, rather than genuinely differentially expressed under different conditions. Poor reproducibility of DE results is mainly caused by high variation of gene expression levels for the same gene in different samples. Even though biological variation may account for much of the high variation of gene expression levels, the effect of outlier count data also needs to be treated seriously, as outlier data severely interfere with DE analysis. Conclusions High heterogeneity exists not only in tumor tissue samples of each cancer type studied, but also in normal samples. High heterogeneity leads to poor reproducibility of DEGs, undermining generalization of differential expression results. Therefore, it is necessary to use large sample sizes (at least 10 if possible) in RNA-Seq experimental designs to reduce the impact of biological variability and DE results should be interpreted cautiously unless soundly validated.

This short communication considers mitigating the negative effects of possibly unreliable path delay measurements acquired in non-line-of-sight (NLOS) environments on the positioning performance, a problem deserving further investigation within the expanding research area of elliptic localization. We present CASTELO, a Convex Approximation based Solution To Elliptic Localization with Outliers , to achieve such a goal. Our proposal corresponds to a mixed semidefinite (SD)/second-order cone (SOC) programming formulation derived from an error-mitigated nonlinear least squares (LS) location estimator, presenting itself as a remedy for the neglect of positivity of NLOS biases suffered by the majority of currently fashionable outlier-handling approaches. In terms of analytical discussions, we provide rationales supporting the incorporation of the SOC constraints, which serve to tighten the problem obtained after SD relaxation, and conduct a complexity analysis for the ultimate mixed SD/SOC programming formulation. Simulations are carried out to confirm the strong ability of CASTELO to attain reliable elliptic localization in the presence of NLOS outliers. [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.

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.

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 Sn 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 Sn 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 Sn scale estimators, corresponding to a range error of about 2 m and 0.5 m.

The article presents the statistical processing of the test results of the mass fraction of the chlorine ion (Cl) obtained from two accredited testing laboratories. The stages of statistical processing of the results of interlaboratory comparison tests are as follows: elimination of gross errors, determination of the uniformity of the sample, calculation of the assigned value, evaluation of the quality of the measurement results. The presence of gross errors in the sample was determined by the Grubbs test, and the uniformity of the sample was confirmed by the Fisher’s test and Student’s test. The assigned value was calculated by a robust method using algorithm A with an iterative scale. The quality of laboratory measurement results was evaluated using the z-score. The z-score value for each participant showed that the results of measuring the mass fraction of chlorine ion in cement samples were acceptable. The quality of the results of these laboratories is satisfactory and does not require corrective measures. These results of the test quality assessment can be used for the organizers of interlaboratory tests, as well as for accredited laboratories that check their qualifications.

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

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

Missing data and outliers usually arise in longitudinal studies. Ignoring the effects of missing data and outliers will make the classical generalized estimating equation approach invalid. The longitudinal cohort study of rheumatoid arthritis patients was designed to investigate whether the Health Assessment Questionnaire score was associated with baseline covariates and changed with time. There exist dropouts and outliers in the data. In order to analyze the data, we develop a robust estimating equation approach. To deal with the responses missing at random, we extend a doubly robust method. To achieve robustness against outliers, we utilize an outlier robust method, which corrects the bias induced by outliers through centralizing the covariate matrix in the estimating equation. The doubly robust method for dropouts is easy to combine with the outlier robust method. The proposed method has the property of robustness in the sense that the proposed estimator is not only doubly robust against model misspecification for dropouts when there is no outlier in the data, but also robust against outliers. Consistency and asymptotic normality of the proposed estimator are established under regularity conditions. A comprehensive simulation study and real data analysis demonstrate that the proposed estimator does have the property of robustness. [ABSTRACT FROM AUTHOR]

In many industrial applications, physical equipment for measuring may suffer from transient malfunctions, which can cause data outliers. This article addresses the problem of guaranteed set-membership estimation of uncertain linear systems subject to outliers in measurements. To solve this problem, a robust set-membership estimator is designed using the forward-backward contraction procedure and a zonotopic filter. The setting up of these techniques offers two main advantages. On the one hand, outliers can be identified and discarded, which helps to resume the guaranteed property and avoid deadlock situations. On the other hand, the proposed method uses set-inversion to fuse the available historical data, so as to attenuate the effect of uncertainties. Numerical examples are provided to show the efficiency of the proposed estimator. [ABSTRACT FROM AUTHOR]

Water quality control aids in preventing pollution, public health, and the preservation and improvement of the biological integrity of water bodies. Water quality involves many variables and observations, some of which are outside of the acceptable range. An observation that apart from the rest of the data or looks diverge from other observation of the sample in which it occurs. In this paper, we proposed two methodologies for detecting outliers for the Yamuna River water quality data with three variables Chemical Oxygen Demand (COD), Bio-chemical Demand Oxygen (BOD) and PH, at three different locations did comparison of these two methodologies. These two methodologies are based on Descriptive Statistics and Statistical Process Control (SPC). A few outliers are present in the data. The outcome shows how far the outlier detection method has progressed and better knowledge of the various outlier methodologies and provide a clear path for future outlier detection methods for researchers. [ABSTRACT FROM AUTHOR]

Evaluation of the performance of the techniques To compare the performance of different techniques, the fractional error of the derived I CV i SB I i i sb is determined as below. HT ht Graph where I CV i SB I derived i sb and I CV i SB I actual i sb are the derived and actual I CV i SB I i i sb respectively. The coefficient of variation for the remaining data, I CV i SB I rr i sb , is calculated and from this the I CV i SB I i i sb is obtained as follows: HT ht Graph (3) 4) Modified result ratio method Here, a modification of the result ratio method [[19]] with more stringent outlier exclusion is examined. In general, the EMS, result ratio and modified result ratio yielded estimates of I CV i SB I i i sb within ±0.1 fractional error. [Extracted from the article]

The presence of an outlier at the starting point of a univariate time series negatively influences the forecasting accuracy. The starting outlier is effectively removed only by making it equal to the second time point value. The forecasting accuracy is significantly improved after the removal. The favorable impact of the starting outlier removal on the time series forecasting accuracy is strong. It is the least favorable for time series with exponential rising. In the worst case of a time series, on average only 7 % to 11 % forecasts after the starting outlier removal are worse than they would be without the removal. [ABSTRACT FROM AUTHOR]

In this paper, we discuss a logical approach to classification of data and identification of outlier in them. We propose an algorithm for finding all possible classes for a given subject area based on the classifier logical function. We analyze these classes and identify the most pronounced patterns corresponding to the classification principles. [ABSTRACT FROM AUTHOR]

Recently, Big Data analytics has been one of the most emerging topics in the business field. Data is collected, processed and analyzed to gain useful insight for their organization. Big Data analytics has the potential to improve the quality of life and help to achieve Sustainable Development Goals (SDG). To ensure that SDG goals are achieved, we must utilize existing data to meet those targets and ensure accountability. However, data quality is often left out when dealing with data. Any types of errors presented in the dataset should be properly addressed to ensure the analysis provided is accurate and truthful. In this paper, we have addressed the concept of data quality diagnosis to identify the outlier presented in the dataset. The cause of the outlier is further discussed to identify potential improvements that can be done to the dataset. In addition, recommendations to improve the quality of data and data collection systems are provided. [ABSTRACT FROM AUTHOR]