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ABSTRACT Process capability indices (PCIs) play a fundamental role in assessing and quantifying the capability of manufacturing processes to meet customer specifications. To date, the majority of PCIs that are now in use have been examined through the use of novel generalizations of PCIs with symmetric tolerances to check the performance of an industrial manufacturing process. In this paper, we proposed new estimations of some indices with asymmetric tolerances in the presence of outliers. New robust and parametric estimators of some PCIs are introduced to estimate and compare these indices for each normal distribution with the presence of different outliers. Meanwhile, this paper discusses how well the proposed method can be used for non‐normal data. For illustration purpose, the application example is presented. [ABSTRACT FROM AUTHOR]

Several methods for the robust estimation of the variance of a normal random variable are based on trimming. For example, the popular Qn$$ {Q}_n $$ estimator is based on trimming the pairs of samples with the largest absolute distance. Another example is least trimmed squares regression, where all unusually large residuals are removed before the estimation of the variance. Here, in this work, we propose two new classes of estimators that use an optimal linear combination of the trimmed samples to achieve a lower mean squared error (MSE). The first class guarantees the smallest variance among all unbiased estimators that are based on linear combinations, while the second class of estimators guarantees the smallest MSE at the cost of some small bias. Through simulations, we demonstrate that our proposed estimators can have considerably smaller MSE than other robust estimators both in the presence and absence of outliers. [ABSTRACT FROM AUTHOR]

While researchers and practitioners are seamlessly trying to develop methods for minimizing the effect of outliers in control charts, detecting and screening these outliers continue to pose serious challenges. Keeping in view, the researchers rely on robust estimators to modify the detection limits structure so that the chart can be more sensitive against outliers. In this study, we propose a robust S2${S}^2$ control chart based on Pn${P}_n$, MAPD,$\ MAPD,$AADM$AADM$, GMD$\ GMD$, PSD$PSD$, and τau$\tau au$ estimators, whilst the process parameter is estimated from Phase‐I. Through intensive Monte‐Carlo simulations, the study presents how the estimation of parameter(s) and presence of outliers affect the efficacy of the S2${S}^2$ chart, and then how the proposed outlier detectors bring the chart back to normalcy by restoring its efficacy and sensitivity. Average ARL$ARL$ properties are used as the performance measures. The AARL$AARL$ properties establish the superiority of the proposed scheme over MAD$\ MAD$ and Tukey's outlier detectors. The applicability of the study includes the effectiveness of the proposed detectors in industrial data set but is not limited to manufacturing industries. [ABSTRACT FROM AUTHOR]

High-dimensional (HD) data, such as images and profiles, are commonly collected from complex systems and contain significant explanatory and predictive information for effective systems monitoring and control. Therefore, developing accurate and robust predictive models based on HD data is crucial. In the literature, various methods, including linear scalar-on-tensor regression, are developed to model a complex system based on HD data. However, existing estimation techniques ignore the presence of outliers and are prone to biased estimations. This article proposes a robust scalar-on-tensor regression framework that handles multi-dimensional HD input data when the data contain outliers. Our proposed estimation method is constructed using maximum Lq-likelihood estimation instead of the classic maximum likelihood estimation. The asymptotic analysis under the Gaussian distribution assumption and the guideline on choosing the tuning parameter of our proposed method is provided. Several simulations and case studies evaluate the proposed method's efficacy compared to several benchmark methods in the literature. [ABSTRACT FROM AUTHOR]

High-dimensional data is characterized by its sparsity and noise, which can increase the likelihood of overfitting and compromise the model's generalizability performance. In this paper, a novel robust subspace learning method based on stable adaptive spectral clustering is put forward for dimensionality reduction. Firstly, a robust estimator is used to distinguish the role of normal and abnormal samples in constructing the model, thereby small values are assigned to the outliers, then the influence of outliers on the construction of the learning models will be reduced. Secondly, the p-order of L 2 -norm distance is applied as the distance metric, replacing the square of L 2 -norm distance metric. The L 2 , p -norm often commendably tolerates the biases caused by the outliers in sample data, especially when the outliers are away from the normal data distributions. Thirdly, the adaptive stable spectral clustering based on the L 2 , p -norm is proposed to construct similarity matrix of the novel robust subspace to carry out reflexive embedding learning to learn the local and globe features of the raw data. In the subspace, the data is reconstructed to reduce the influence of noise and outliers, and the similarity matrix is structured by the new features, that is more conducive to subspace learning. Three main roles of the objective function of our model are: (1) preserving the consistency between the original data and the estimation; (2) achieving a "clean" subspace and further removing the outliers; (3) avoiding the trivial solution for each node in the graph. Finally, the random forest algorithm is used to classify and predict the learned subspace with different feature selections. Experimental results show that the proposed method is superior to other subspace learning methods in classification performance. The results of noise experiment and statistical analysis demonstrate the effectiveness of the proposed method once again. [ABSTRACT FROM AUTHOR]

AbstractBathymetric multibeam echosounder systems (MBES) provide high-resolution mapping of underwater topography but are highly susceptible to errors due to harsh environmental conditions and the measurement process. Traditionally, manual post-processing is required to ensure data quality, a time-consuming, expensive, and subjective task. To address this issue, we propose a surface-based algorithm for pre-processing and cleaning MBES data that reduces manual intervention and improves consistency. A surface-based algorithm models the underwater topography as a surface instead of processing individual points. By assuming a continuous surface for underwater geometry, the algorithm easily identifies observations that deviate significantly from this model. The method combines a hierarchical B-spline surface with iterative robust estimation to automate data cleaning. Preliminary results on example datasets show a balanced outlier detection accuracy of 0.99, with manual processing time reduced from 2 days to just 30 min. [ABSTRACT FROM AUTHOR]

The basis of robust parameter design is the creation of a design that can resist the negative effects caused by uncontrollable or difficult-to-control external and environmental factors, which affect the product parameters in achieving product design during product realization activities. Robustness is the ability of a product or process to be least affected by variabilities caused by external factors. The success of the response surface methodology generally depends on a model chosen to fit the data distribution. Making incorrect assumptions regarding data distribution when creating response surface models can affect the effectiveness of the quality improvement strategy used. Non-normal or contaminated data is a common phenomenon in quality improvement applications. Although non-normal data is common in robust parameter applications, it is often the case that users ignore the underlying distribution shape of the data at the modeling stage and use normal theory techniques naively. This study proposes a dual response surface approach based on robust confidence intervals for cases where the experimental data do not meet normality assumptions or have contaminated data distribution. A new dual response surface methodology is proposed based on modeling the MAD -- t confidence interval, Sn -- t confidence interval, and Qn -- t confidence interval formulations with the response surface methodology. All the proposed methods make the process median unbiased for the mean using the skewness of the experimental data. Two well-known experimental design studies are used to demonstrate the procedure and its advantages. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new confidence interval (CI) for the population mean μ based on robust estimators, which involves the application of the winsorized modified one-step M-estimator (WMOM) and winsorized standard deviation (WSD). The proposed method is modified for the Student's t confidence interval CI under the non-normal distribution. The performances of the proposed confidence interval were evaluated via a Monte-Carlo simulation study by considering the coverage ratio (CR) and the average length (AL) as performance criteria. The simulation study results show the superior performance of the proposed confidence interval (CI) over the classical parametric Student's t for data from a non-normal distribution. Two real data sets were analyzed, and the results agree to some extent with those of the simulation study. The results confirm the suitability of the proposed CI estimator for both normally and non-normally distributed data. [ABSTRACT FROM AUTHOR]

The vector autoregressive (VAR) model has been popularly employed in operational practice to study multivariate time series. Despite its usefulness in providing associated metrics such as the impulse response function (IRF) and forecast error variance decomposition (FEVD), the traditional VAR model estimated via the usual ordinary least squares is vulnerable to outliers. To handle potential outliers in multivariate time series, this paper investigates two robust estimation methods of the VAR model, the reweighted multivariate least trimmed squares and the multivariate MM-estimation. The robust information criteria are also proposed to select the appropriate number of temporal lags. Via extensive simulation studies, we show that the robust VAR models lead to much more accurate estimates than the original VAR in the presence of outliers. Our empirical results include logged daily realized volatilities of six common safe haven assets: futures of gold, silver, Brent oil and West Texas Intermediate (WTI) oil and currencies of Swiss Francs and Japanese Yen. Our sample covers July 2017–June 2020, which includes the history-writing price drop of WTI on April 20, 2020. Our baseline results suggest that the traditional VAR model may significantly overestimate some parameters, as well as IRF and FEVD metrics. In contrast, robust VAR models provide more reliable results, the validity of which is verified via various approaches. Empirical implications based on robust estimates are further illustrated. [ABSTRACT FROM AUTHOR]

The one-inflated positive Poisson Lindley model has been recently introduced as an alternative in modelling positive count data with a large number of ones: a phenomenon known as one-inflation. In the presence of oneinflation, this model has a high tendency to be influenced by outliers, making usual parameter estimations to be less robust. Hence, several estimators: maximum likelihood, method of moments, ordinary least squares, weighted least squares, Cramér-Von Mises, modified Cramér-Von Mises (MCVM) and maximum product of spacing (MPS); for the parameters of the model are also proposed and investigated in terms of unbiasedness, consistency and joint efficiency under the presence and absence of outliers. When the outliers are absent, the MPS estimator is the best estimator and when the outliers are present, the MCVM estimator is the best estimator. Model fittings to two real datasets with one-inflation and outliers support the simulation results and conclude that the MCVM estimator is the best estimator. Based on the best robust estimator, the population size of the number of offenders as well as the likelihood of arrests were estimated. [ABSTRACT FROM AUTHOR]

Simple Summary: Growth traits have been widely studied as economically important traits in the beef cattle industry. However, traditional studies often miss how these traits change under different environmental conditions, only focusing on single genetic changes that affect traits directly. In our study, we analyzed how genetics and environment interact to affect growth in beef cattle, considering four growth traits and two environmental factors. This analysis uncovered several genetic markers for growth traits that are not usually evident in standard studies, showing that some genes have effects that can be obliterated by environmental conditions Further testing showed whether these genetic markers are grouped in specific genes or functional pathways, helping us understand how genetics can influence growth under different environmental conditions. By uncovering novel genetic loci, genes, and candidate biological mechanisms associated with growth traits, our study provides valuable information for selection prediction and breeding decisions in the beef cattle industry. Complex traits are widely considered to be the result of a compound regulation of genes, environmental factors, and genotype-by-environment interaction (G × E). The inclusion of G × E in genome-wide association analyses is essential to understand animal environmental adaptations and improve the efficiency of breeding decisions. Here, we systematically investigated the G × E of growth traits (including weaning weight, yearling weight, 18-month body weight, and 24-month body weight) with environmental factors (farm and temperature) using genome-wide genotype-by-environment interaction association studies (GWEIS) with a dataset of 1350 cattle. We validated the robust estimator's effectiveness in GWEIS and detected 29 independent interacting SNPs with a significance threshold of 1.67 × 10−6, indicating that these SNPs, which do not show main effects in traditional genome-wide association studies (GWAS), may have non-additive effects across genotypes but are obliterated by environmental means. The gene-based analysis using MAGMA identified three genes that overlapped with the GEWIS results exhibiting G × E, namely SMAD2, PALMD, and MECOM. Further, the results of functional exploration in gene-set analysis revealed the bio-mechanisms of how cattle growth responds to environmental changes, such as mitotic or cytokinesis, fatty acid β-oxidation, neurotransmitter activity, gap junction, and keratan sulfate degradation. This study not only reveals novel genetic loci and underlying mechanisms influencing growth traits but also transforms our understanding of environmental adaptation in beef cattle, thereby paving the way for more targeted and efficient breeding strategies. [ABSTRACT FROM AUTHOR]

In structural equation modeling, when multiple imputation is used for handling missing data, model fit evaluation involves pooling likelihood-ratio test statistics across imputations. Under the normality assumption, the two most popular pooling approaches were proposed by Li et al. (Statistica Sinica, 1(1), 65–92, 1991) and Meng and Rubin (Biometrika, 79(1), 103–111, 1992). When the assumption of normality is violated, it is not clear how well these pooling approaches work with the test statistics generated from various robust estimators and multiple imputation methods. Jorgensen and colleagues (2021) implemented these pooling approaches in their R package semTools; however, no systematical evaluation has been conducted. In this simulation study, we examine the performance of these approaches in working with different imputation methods and robust estimators under nonnormality. We found that the naïve pooling approach based on Meng and Rubin (Biometrika, 79(1), 103–111, 1992; D3SN) worked the best when combining with the normal-theory-based imputation and either MLM or MLMV estimator. [ABSTRACT FROM AUTHOR]

Complex traits are widely considered to be the result of a compound regulation of genes, environmental factors, and genotype-by-environment interaction (G × E). The inclusion of G × E in genome-wide association analyses is essential to understand animal environmental adaptations and improve the efficiency of breeding decisions. Here, we systematically investigated the G × E of growth traits (including weaning weight, yearling weight, 18-month body weight, and 24-month body weight) with environmental factors (farm and temperature) using genome-wide genotype-by-environment interaction association studies (GWEIS) with a dataset of 1350 cattle. We validated the robust estimator’s effectiveness in GWEIS and detected 29 independent interacting SNPs with a significance threshold of 1.67 × 10−6, indicating that these SNPs, which do not show main effects in traditional genome-wide association studies (GWAS), may have non-additive effects across genotypes but are obliterated by environmental means. The gene-based analysis using MAGMA identified three genes that overlapped with the GEWIS results exhibiting G × E, namely SMAD2, PALMD, and MECOM. Further, the results of functional exploration in gene-set analysis revealed the bio-mechanisms of how cattle growth responds to environmental changes, such as mitotic or cytokinesis, fatty acid β-oxidation, neurotransmitter activity, gap junction, and keratan sulfate degradation. This study not only reveals novel genetic loci and underlying mechanisms influencing growth traits but also transforms our understanding of environmental adaptation in beef cattle, thereby paving the way for more targeted and efficient breeding strategies.

This study investigates the effectiveness of robust estimators of location and dispersion, used in proficiency testing and listed in ISO 13528:2015, in outlier detection. The models utilize (a) kernel density plots, (b) Z-factors, (c) Monte Carlo simulations, and (d) distributions derived from at most two contaminating distributions and one main Gaussian. The simulation parameters cover a wide range of those commonly encountered in proficiency testing (PT) schemes, so the results presented are of fairly general application. We chose a functional sub-optimal solution by grouping and classifying the model settings, resulting in five matrices readily usable for selecting the best robust estimator. Whenever at most half of the distribution of each contaminating population is outside the central distribution, there is only one optimal estimator. For all other cases, the five matrices provide the appropriate robust statistic. The proposed method applies to 95.1% of 144 results for an existing PT for cement. These actual datasets indicate that the Hampel estimator for the mean and the Q-method for the standard deviation provide the most appropriate performance statistic in 86.1% of the cases.

With the development of 3D sensors, 3D point cloud data can now be obtained conveniently. Therefore, it is crucial to process point cloud data automatically. Region growing is a commonly used algorithm to segment point clouds, which greatly depends on the accuracy of points’ normals and requires tuning two thresholds; i.e., the increment of curvature ( $\sigma _{th}$ ) and normal angles ( $\theta _{th}$ ). In this paper, we improve the region growing algorithm in two ways: Accurate normal estimation and strengthening the region growing criteria. For the first aspect, principal component analysis (PCA) is utilized to estimate the initial normals of the point cloud. Then, the points are divided into regular points (RP) and sharp feature points (SFP), according to their initial normals. A robust estimator-based PCA is then applied to refine the SFP normals. For the latter aspect, non-connected and non-coplanar points are detected and ignored when region grows. Finally, the segmentation performance of the proposed method is evaluated using internal and external indices. The results indicate that the proposed method can accurately estimate the point normals within an acceptable time, and obtain a better result than the classic PCA-based region growing algorithm and advanced DetMM-based methods.

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]

In this paper, we explore the idea of grouping under the massive data framework, to propose a median-of-means non-parametric type estimator for the Proportional Hazard Transform (PHT), which has been widely used in finance and insurance. Under certain conditions on the growth rate of subgroups, the consistency and asymptotic normality of the proposed estimators are investigated. Furthermore, we construct a new method to test PHT based on the empirical likelihood method for the median in order to avoid any prior estimate of the variance structure for the proposed estimator, as it is difficult to estimate and often causes much inaccuracy. Numerical simulations and real-data analysis are designed to show the present estimator's performance. The results confirm that the new put-forward estimator is quite robust with respect to outliers. [ABSTRACT FROM AUTHOR]

This study investigates the effectiveness of robust estimators of location and dispersion, used in proficiency testing and listed in ISO 13528:2015, in outlier detection. The models utilize (a) kernel density plots, (b) Z-factors, (c) Monte Carlo simulations, and (d) distributions derived from at most two contaminating distributions and one main Gaussian. The simulation parameters cover a wide range of those commonly encountered in proficiency testing (PT) schemes, so the results presented are of fairly general application. We chose a functional sub-optimal solution by grouping and classifying the model settings, resulting in five matrices readily usable for selecting the best robust estimator. Whenever at most half of the distribution of each contaminating population is outside the central distribution, there is only one optimal estimator. For all other cases, the five matrices provide the appropriate robust statistic. The proposed method applies to 95.1% of 144 results for an existing PT for cement. These actual datasets indicate that the Hampel estimator for the mean and the Q-method for the standard deviation provide the most appropriate performance statistic in 86.1% of the cases. [ABSTRACT FROM AUTHOR]

In this study, we propose the robust ratio-type estimator of finite population variance considering the minimum covariance determinant (MCD) and the minimum volume ellipsoid (MVE) robust covariance matrices in simple and stratified random sampling. The mean square errors (MSE) equations are obtained for the robust ratio-type estimator. The conditions for which the proposed robust ratio-type estimator is more efficient as compared to competing estimators have been discussed. The MCD and MVE are robust against outliers. Thus, when there is an outlier in the data, simulations and empirical results show that the proposed robust ratio-type estimators under simple and stratified random sampling have a lower mean square error than the traditional estimators. In addition, we support theoretical results with contaminated real examples and simulation studies. [ABSTRACT FROM AUTHOR]

Data used in deep learning is notoriously problematic. For example, data are usually combined from diverse sources, rarely cleaned and vetted thoroughly, and sometimes corrupted on purpose. Intentional corruption that targets the weak spots of algorithms has been studied extensively under the label of "adversarial attacks." In contrast, the arguably much more common case of corruption that reflects the limited quality of data has been studied much less. Such "random" corruptions are due to measurement errors, unreliable sources, convenience sampling, and so forth. These kinds of corruption are common in deep learning, because data are rarely collected according to strict protocols—in strong contrast to the formalized data collection in some parts of classical statistics. This article concerns such corruption. We introduce an approach motivated by very recent insights into median-of-means and Le Cam's principle, we show that the approach can be readily implemented, and we demonstrate that it performs very well in practice. In conclusion, we believe that our approach is a very promising alternative to standard parameter training based on least-squares and cross-entropy loss. [ABSTRACT FROM AUTHOR]

The state estimation ensures the secure and reliable operation of modern-day power systems by accurate monitoring of its states, voltage magnitudes and phase angles. A robust estimator can detect erroneous measurements, also known as bad-data, and eliminate those automatically during the estimation process. Least measurement rejected (LMR) is one of such robust estimators which has been found significantly successful in dealing with bad-data of various categories. In the LMR algorithm, a certain tolerance needs to be imposed on each measurement and the performance of the estimator mostly depends upon it. Different approaches to tolerance selection have been tried in the literature. The novelty of this paper lies in proposing an original approach of improving the accuracy of LMR by appropriate selection of tolerance. The double-layer selection process assigns a unique number to each measurement, based on the meter quality. LMR, with six different versions of tolerance values, is used from the literature, under different bad-data scenarios, to check whether the proposed tuned LMR (TLMR) performs better than those or not. Besides, some widely used robust estimators are simulated to prove the efficacy and robustness of the proposed approach. It has been observed that the estimation accuracy has been improved significantly than the older versions after being subjected to the proposed mechanism. Moreover, TLMR has successfully outperformed the other estimators while dealing with white noise, bad-data and leverage point scenarios. [ABSTRACT FROM AUTHOR]

Air travel management is a case-special process since it includes different types of uncertainties such as ungovernable passenger mobility, variable costs as well as extraordinary restrictions like the Covid-19 pandemic. Therefore, the use of robust and reproducible statistical evaluations under uncertainty is required. The cornerstone of this study is the adaptation of bootstrapping and the robust Minimum Covariance Determinant (MCD)-based parameter estimation under a heterogeneous process. In addition, the study includes a novel bootstrapping regression implementation. The methodological developments have been tested by Serbia's air transport data. The results showed that combining robust estimator and bootstrapping provides some advantages for determining outliers and also making advanced diagnostics. Thus, a state-of-the-art approach based on accuracy, reproducibility, and transparency has been introduced and its usability in the air travel mobility process has been exhibited.

In biomedicine, binary classification problems are involved in diagnostic but also, for instance, in personalized medicine. The objective is to use information for correctly allocating subjects in groups. Frequently, this information implies high-dimensional data. An adequate classification rule is a trade-off between the sensitivity and the specificity. The ROC curve helps to understand, evaluate and compare the accuracy of classification processes. We propose a procedure for estimating the optimal classification rules based on a penalized estimator of the underlying probability distribution functions. We study its asymptotic properties. Through Monte Carlo simulations, we compare our proposal with a support vector machine-based ROC curve. We illustrate its practical use in a real-world problem. Results suggest that, despite some techniques promise to improve the results provided by traditional methods, in the binary classification problem, the limit is the actual relationship among the density functions. [ABSTRACT FROM AUTHOR]

In this note we provide a novel semi-parametric best linear unbiased estimator (BLUE) of location and its corresponding variance estimator under the assumption the random variate is generated from a symmetric location-scale family of distributions. The approach follows in a two-stage fashion and is based on the exact bootstrap estimate of the covariance matrix of the order statistic. We generalize our approach to add a robustness component in order to derive a trimmed BLUE of location under a semi-parametric symmetry assumption. [ABSTRACT FROM AUTHOR]

Energy storage technologies are being used excessively in industrial applications and in automobiles. Battery state of charge (SOC) is an important metric to be monitored in these applications to ensure proper and safe functionality. Since SOC cannot be measured directly, this paper puts forth a novel machine learning architecture to improve on the existing methods of SOC estimation. This method consists of using combined stacked bi-directional LSTM and encoder–decoder bi-directional long short-term memory architecture. This architecture henceforth represented as SED is implemented to overcome the nonparallel functionality observed in traditional RNN algorithms. Estimations were made utilizing different open-source datasets such as urban dynamometer driving schedule (UDDS), highway fuel efficiency test (HWFET), LA92 and US06. The least Mean Absolute Error observed was 0.62% at 25 °C for the HWFET condition, which confirms the good functionality of the proposed architecture. [ABSTRACT FROM AUTHOR]

In this paper, a robust H ∞ design approach is proposed to accomplish the state estimation task for a class of continuous-time gene regulatory networks (GRNs) in the finite-time framework. For the GRNs under investigation, the distributed inter-node time delays and intra-node time delays are, respectively, taken into account in the feedback regulation function and the translation process. Moreover, the parameter uncertainty with convex polytopic constraint is introduced to characterize the unavoidable modeling errors induced by parameter sensitivity, calibration accuracy, abrupt structural changes and failures, etc. The primary objective of this paper is to design a robust state estimator for the considered GRNs based on the measured concentrations of messenger ribonucleic acid and protein such that the finite-time boundedness and the H ∞ performance are guaranteed. By resorting to the Lyapunov stability theory, the Jensen's inequality and the Gronwall inequality, some sufficient conditions are established in terms of linear matrix inequalities. Subsequently, a set of parameter matrices with special structures is designed by means of the convex optimization techniques. Finally, a numerical example is provided to demonstrate the finite-time boundedness and the robust performance of the developed state estimation approach. [ABSTRACT FROM AUTHOR]

The iterative closest point (ICP) algorithm and its variants are a fundamental technique for rigid registration between two point sets, with wide applications in different areas from robotics to 3D reconstruction. The main drawbacks for ICP are its slow convergence, as well as its sensitivity to outliers, missing data, and partial overlaps. Recent work such as Sparse ICP achieves robustness via sparsity optimization at the cost of computational speed. In this paper, we propose a new method for robust registration with fast convergence. First, we show that the classical point-to-point ICP can be treated as a majorization-minimization (MM) algorithm, and propose an Anderson acceleration approach to speed up its convergence. In addition, we introduce a robust error metric based on the Welsch’s function, which is minimized efficiently using the MM algorithm with Anderson acceleration. On challenging datasets with noises and partial overlaps, we achieve similar or better accuracy than Sparse ICP while being at least an order of magnitude faster. Finally, we extend the robust formulation to point-to-plane ICP, and solve the resulting problem using a similar Anderson-accelerated MM strategy. Our robust ICP methods improve the registration accuracy on benchmark datasets while being competitive in computational time. [ABSTRACT FROM AUTHOR]

Modeling the photovoltaic (PV) array can analyze the influence of temperature, irradiance, and other factors on the I‐V characteristic curve. The model can be used to replace the actual PV array to implement various PV experiments for reducing experimental costs and saving experimental time. Parameter estimation can make the parameters of the PV array model more accurate and make the outputs of model consistent with the outputs of the actual device. This work focuses on PV array under partial shading condition problems. The main contribution is to use different types of robust estimators for parameter estimation and present a comparative analysis of the traditional weighted least squares (WLS) estimator and eight robust estimators, including the Quasi‐weighted least squares (QWLS) estimator, Correntropy, etc. The performance of these estimators is analyzed in the simulation, where both random errors and gross errors are considered. The results showed that the robust estimators have better performance than the traditional WLS estimator especially when the measurement data contain gross errors. The robust estimators can be used in the parameter estimation for PV array model to estimate the accurate parameters. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

In this study, it was aimed to evaluate the performance of different estimators that will be used in regression analysis, which is one of the multivariate statistical methods in the presence of outliers in the data set. Sixth month live weight was estimated with various body measurements for Saanen kids taken from a private farm. In the data set, the use and performance of robust estimators were evaluated because the least squares method did not provide reliable results in the case of outliers. M (for Huber and Tukey bisquare) estimator, MM estimator and LTS estimator were used as robust used in the presence of outliers. MSE, RMSE, rRMSE, MAPE, MAD, R2, R2adj and AIC were used as model comparison criteria in the study. As a result of the study, in the case of outlier in the data set, Huber type M estimator can be recommended. [ABSTRACT FROM AUTHOR]

This paper proposes a robust estimation approach of the popular information share using a robust vector error correction model. Via simulation studies, we show that the proposed measures lead to more accurate estimates than the existing measures in the presence of outliers. The proposed measures are then investigated using high‐frequency crude oil prices data of the West Texas Intermediate and Brent over November 2019–October 2020. The results suggest that the abnormally large price movement in April 2020 may cause biased estimates of the ordinary measures, whereas the robust measures produce rather consistent results. [ABSTRACT FROM AUTHOR]

RANSAC (RANdom SAmple Consensus) is a widely used robust estimator for estimating a geometric model from feature matches in an image pair. Unfortunately, it becomes less effective when initial input feature matches (i.e., input data) are corrupted by a large number of outliers. In this paper, we propose a new robust estimator (called TRESAC) for model estimation, where data subsets are sampled with the guidance of the triplet relationships, which involve high relevance and local geometric consistency. Each triplet consists of three data, whose relationships satisfy spatial consistency constraints. Therefore, the triplet relationships can be used to effectively initialize and refine the sampling process. With the advantage of the triplet relationships, TRESAC significantly alleviates the influence of outliers and also improves the computational efficiency of model estimation. Experimental results on four challenging datasets show that TRESAC can achieve superior performance on both estimation accuracy and computational efficiency against several other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Multivariate data is collected in many fields, such as chemometrics, econometrics, financial engineering and genetics. In multivariate data, heteroscedasticity and collinearity occur frequently. And selecting material predictors is also a key issue when analyzing multivariate data. To accomplish these tasks, multivariate linear regression model is often constructed. We thus propose row-sparse elastic-net regularized multivariate Huber regression model in this paper. For this new model, we proof its grouping effect property and the property of resisting sample outliers. Based on the KKT condition, an accelerated proximal sub-gradient algorithm is designed to solve the proposed model and its convergency is also established. To demonstrate the accuracy and efficiency, simulation and real data experiments are carried out. The numerical results show that the new model can deal with heteroscedasticity and collinearity well. [ABSTRACT FROM AUTHOR]

This study aims at developing models in analyzing the results of proficiency testing (PT) schemes for a limited number of participants. The models can determine the best estimators of location and dispersion using unsatisfactory results as a criterion by combining: (a) robust and classical estimators; (b) kernel density plots; (c) Z-factors; (d) Monte Carlo simulations; (e) distributions derived from the addition of one or two contaminating distributions and one main Gaussian. The standards ISO 13258:2015, ISO 5725:2:1994, and EN ISO/IEC 17043:2010 are the basis of the analysis. The study describes an algorithm solving the optimization problem for (a) Gaussian, bimodal or trimodal distributions; (b) participating labs from 10 to 30; (c) fraction of the contaminating population up to 0.10; (d) variation coefficient of the main distribution equal to 2; (e) equal standard deviations of all the distributions, and provide figures with the optimal estimators. We also developed a generalized algorithm using kernel density plots and the previous algorithm, which is not subject to restrictions (b)–(e) and implemented in the results of a PT for the 28-day strength of cement with 12–13 participants. Optimal estimators' figures and the generalized algorithm are helpful for a PT expert in choosing robust estimators. [ABSTRACT FROM AUTHOR]

The classical Hotelling T² control chart using classical mean and covariance estimators is not ecient in case of outliers existence in data. To overcome this issue, robust mean and covariance estimators are used in literature. Hence, a robust Hotelling T² control chart is proposed based on the adaptive reweighted minimum covariance determinant estimator which is a good option to the classical multivariate T² chart in case of outliers presence. The new proposed chart's performance is evaluated by false alarm rates and probability of detection/percentage of outliers detection, later a comparison is made with the performance of the classical Hotelling T² chart and the chart obtained using the minimum covariance determinant estimator. Simulation and real data application results are indicated that proposed control chart has better performance in comparison to robust control chart based on the minimum covariance determinant especially in terms of false alarm rates and it performs better than classical chart in terms of probability of detection. [ABSTRACT FROM AUTHOR]

In this paper, we introduce the median-of-means approach for estimating unknown parameters in multiple linear regression model and the regression model with autocorrelated error. The proposed estimators are not only robust against the outliers of data but also proved to be consistent. Extensive numerical simulations and one real dataset are presented to show the performances of new estimators. [ABSTRACT FROM AUTHOR]

The periodic behavior of real data can be manifested in the time series or in its characteristics. One of the characteristics that often manifests the periodic behavior is the sample autocovariance function. In this case, the periodically correlated (PC) behavior is considered. One of the main models that exhibits PC property is the periodic autoregressive (PARMA) model that is considered as the generalization of the classical autoregressive moving average (ARMA) process. However, when one considers the real data, practically the observed trajectory corresponds to the "pure" model with the additional noise which is a result of the noise of the measurement device or other external forces. Thus, in this paper we consider the model that is a sum of the periodic autoregressive (PAR) time series and the additive noise with finite-variance distribution. We present the main properties of the considered model indicating its PC property. One of the main goals of this paper is to introduce the new estimation method for the considered model's parameters. The novel algorithm takes under consideration the additive noise in the model and can be considered as the modification of the classical Yule–Walker algorithm that utilizes the autocovariance function. Here, we propose two versions of the new method, namely the classical and the robust ones. The effectiveness of the proposed methodology is verified by Monte Carlo simulations. The comparison with the classical Yule–Walker method is presented. The approach proposed in this paper is universal and can be applied to any finite-variance models with the additive noise. [ABSTRACT FROM AUTHOR]

• Design of a robust, transductive estimator for domain adaptation whose target risk is never worse than that of the source estimator. • Our estimator need not make strong assumptions on the relationship between domains, such as covariate shift. • For discriminant analysis, our robust estimator always achieves a strictly smaller target risk than the source estimator. Consider a domain-adaptive supervised learning setting, where a classifier learns from labeled data in a source domain and unlabeled data in a target domain to predict the corresponding target labels. If the classifier's assumption on the relationship between domains (e.g. covariate shift, common subspace, etc.) is valid, then it will usually outperform a non-adaptive source classifier. If its assumption is invalid, it can perform substantially worse. Validating assumptions on domain relationships is not possible without target labels. We argue that, in order to make domain-adaptive classifiers more practical, it is necessary to focus on robustness; robust in the sense that an adaptive classifier will still perform at least as well as a non-adaptive classifier without having to rely on the validity of strong assumptions. With this objective in mind, we derive a conservative parameter estimation technique, which is transductive in the sense of Vapnik and Chervonenkis, and show for discriminant analysis that the new estimator is guaranteed to achieve a lower risk on the given target samples compared to the source classifier. Experiments on problems with geographical sampling bias indicate that our parameter estimator performs well. [ABSTRACT FROM AUTHOR]

Control Charts are one of the most powerful tools used to detect aberrant behavior in industrial processes. A valid performance measure for a control chart is the average run length (ARL); which is the expected number of runs to get an out of control signal. At the same time, robust estimators are of vital importance in order to estimate population parameters. Median absolute deviation (MAD) and quantiles are such estimators for population standard deviation. In this study, alternative control charts to the Tukey control chart based on the robust estimators are proposed. To monitor the control chart's performance, the ARL values are compare for many symmetric and skewed distributions. The simulation results show that the in-control ARL values of proposed control charts are higher than Tukey's control chart in all cases and more efficient to detect the process mean. However, the out- of- control ARL values for the all control charts are worse when the probability distribution is non-normal. As a result, it is recommended to use control chart based on the estimator Qn for the process monitoring performance when data are from normal or non-normal distribution. An application example using real-life data is provided to illustrate the proposed control charts, which also supported the results of the simulation study to some extent.

Energy storage technologies are being used excessively in industrial applications and in automobiles. Battery state of charge (SOC) is an important metric to be monitored in these applications to ensure proper and safe functionality. Since SOC cannot be measured directly, this paper puts forth a novel machine learning architecture to improve on the existing methods of SOC estimation. This method consists of using combined stacked bi-directional LSTM and encoder–decoder bi-directional long short-term memory architecture. This architecture henceforth represented as SED is implemented to overcome the nonparallel functionality observed in traditional RNN algorithms. Estimations were made utilizing different open-source datasets such as urban dynamometer driving schedule (UDDS), highway fuel efficiency test (HWFET), LA92 and US06. The least Mean Absolute Error observed was 0.62% at 25 °C for the HWFET condition, which confirms the good functionality of the proposed architecture.

Parameter estimation is a very important issue in lifetime models. The data type encountered in practice is usually in the form of progressive censored data. Parameter estimation based on progressive censored samples is different from the classical full sample case. The aim of this study is to estimate the shape parameter of the Weibull distribution based on the progressive censored sample. For this purpose, the shape parameter of the Weibull distribution was estimated using a simple linear regression model. Least Squares (OLS) and robust estimation method M (Huber, Tukey and Hampel) estimators are used as estimation method. The efficiencies of these estimation methods were compared with the Monte-Carlo simulation study according to whether the data set contains outliers or not. As a result, M estimators was determined as the most effective estimators for the estimation of the shape parameter of the Weibull distribution based on the progressive censored sample. [ABSTRACT FROM AUTHOR]

This paper proposed new adjusted Hotelling's T² control chart for individual observations. For this objective, bootstrap method for producing the individual observations were employed. To do so, both arithmetic mean vector and the covariance matrix in the traditional Hotelling's T² chart were substituted by the trimmed mean vector and the covariance matrix of the robust scale estimators Qn, respectively which, in turn, its performance is carried out by simulated. In fact, the calculation of false alarms and the probability of detection outlier is used for determining the validity of this modified chart. The findings revealed a considerable significance in its performance. [ABSTRACT FROM AUTHOR]

This study aims at developing models in analyzing the results of proficiency testing (PT) schemes for a limited number of participants. The models can determine the best estimators of location and dispersion using unsatisfactory results as a criterion by combining: (a) robust and classical estimators; (b) kernel density plots; (c) Z-factors; (d) Monte Carlo simulations; (e) distributions derived from the addition of one or two contaminating distributions and one main Gaussian. The standards ISO 13258:2015, ISO 5725:2:1994, and EN ISO/IEC 17043:2010 are the basis of the analysis. The study describes an algorithm solving the optimization problem for (a) Gaussian, bimodal or trimodal distributions; (b) participating labs from 10 to 30; (c) fraction of the contaminating population up to 0.10; (d) variation coefficient of the main distribution equal to 2; (e) equal standard deviations of all the distributions, and provide figures with the optimal estimators. We also developed a generalized algorithm using kernel density plots and the previous algorithm, which is not subject to restrictions (b)–(e) and implemented in the results of a PT for the 28-day strength of cement with 12–13 participants. Optimal estimators’ figures and the generalized algorithm are helpful for a PT expert in choosing robust estimators.

In this paper, we introduce the median-of-means approach for estimating unknown parameters in multiple linear regression model and the regression model with autocorrelated error. The proposed estimators are not only robust against the outliers of data but also proved to be consistent. Extensive numerical simulations and one real dataset are presented to show the performances of new estimators. [ABSTRACT FROM AUTHOR]

Most of the research work on ratio, product, and regression estimators are usually based on conventional measures such as mean, quartiles, semi-interquartile range, semi-interquartile average, coefficient of skewness, coefficient of kurtosis, etc. The efficiency of these conventional measures is doubtful in the presence of extreme values in the data. In this paper, we propose an enhanced family of estimators for estimating the population variance using unconventional location measures such as tri-mean, Hodges-Lehmann, and decile mean of an auxiliary variable. The performance of the proposed family of estimators is compared with the existing estimators using a simulation study and two real populations. Also, the robustness of the proposed estimators was examined using an environment protection data with extreme values. The results showed that the proposed family performs better than its competitors not only in simple conditions but is also robust in the presence of extreme values. [ABSTRACT FROM AUTHOR]