Search

Statistical inference for high-dimensional survival data is important for obtaining valid scientific results in many research areas, including biomedical studies and financial risk management. In this paper, a novel framework for feature selection in Cox model is proposed, which achieves false discovery rate (FDR) control asymptotically. The key step is to construct a sequence of ranking statistics based on two independent estimators of the regression coefficients. The FDR control is reached by choosing a data-driven threshold along the ranking of symmetric-based statistics. The de-sparsified estimator and uneven data splitting strategy are employed to improve the robustness of variable selection results and the power in finite sample analysis. We establish the asymptotic FDR control property for the proposed approach at any designated level. Extensive simulation studies and an empirical application on a P2P loan dataset confirm the robustness of the proposed method in FDR control, and show that it often leads to higher power among competitors. [ABSTRACT FROM AUTHOR]

Image representations based on deep learning models can provide exciting performance for image retrieval, but only using deep learning models cannot exploit global topological properties appropriately. The topological perception theory claims that the visual perception process is from global to local: global topological perception occurs earlier than other local patterns. Simulating the visual perception mechanism together with deep learning models to provide a compact yet discriminative representation remains challenging. Toward this end, we propose a novel image representation method called deep-embedding global feature descriptor. The main highlights include: (1) A frequency statistics ranking method is proposed to yield global topology features by combining global visual features and deep convolutional features. (2) An embedding method is proposed to embed the global topology feature spatially and channel-wise into deep convolutional features. It can reasonably integrate the global topological characteristic with local patterns by simulating the visual perceptual process from global to local. (3) A compact yet discriminative representation is provided by leveraging the advantages of global visual and deep features. Exhaustive experiments on five well-known benchmark datasets show that the proposed method outperforms some recent unsupervised state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Material selection is influential in product design, manufacturing, and marketing. Appropriate material selection maximizes the performance of a product while minimizing its cost, whereas inappropriate material selection creates devastating results such as low performance, low quality, and high cost. Therefore, it is crucial how to choose the most suitable material. Unlike other studies, this study presents an ensemble multi-attribute decision-making approach for material selection. The approach involves four weighting methods—criteria importance through intercriteria correlation, Entropy, the method based on the removal effects of criteria, and statistical variance, five ranking methods—additive ratio assessment, combined compromise solution, multi-attributive border approximation area comparison, range of value, and the technique for order performance by similarity to the ideal solution, Spearman's correlation coefficients, and the Copeland method. Three different problems are considered to show the applicability of the proposed method and to reveal a comprehensive analysis. The results of each problem show valuable implications. The results of the ranking methods are sensitive to attribute weights. No ranking method alone can assure dependable selection for a given problem. Overall, the results reveal the importance of using multiple weighting and ranking methods and the superiority of the proposed integrated approach. [ABSTRACT FROM AUTHOR]

Based on published and new data for explosive events at Stromboli (Italy), we propose an empirical relation that links mass discharge rate (MDR) and at-vent gas jet velocity (Gv). We use 65 simultaneous measurements of MDR and Gv and find two trends in both the cross-correlation and rank order statistics. Cross-correlation gives a power law relation: M D R = 10 (0.015 G v + 2.434) kg/s, R2 = 0.81, and applies to ash-dominated emissions. Combining this relation with the conservation of mass equation allows at-vent plume density and/or vent area to be derived from MDR = GvρA, ρ being plume density and A being vent cross-sectional area. We find that while a vent radius of 2 m and plume density of 0.35 kg/m3 fits with the "normal" activity at Stromboli, a 290 × 2.5 m vent area likely feeds a 10 kg/m3 jet during paroxysmal activity. Initial tests on available data shows promise in extending the correlation beyond Stromboli and/or to events with higher MDR (> 107 kg/s). However, the exact relation will depend on magma composition, temperature and volatile content, as well as conduit radius and vent overpressure. [ABSTRACT FROM AUTHOR]

Background: Metabolic pathway prediction is one possible approach to address the problem in system biology of reconstructing an organism's metabolic network from its genome sequence. Recently there have been developments in machine learning-based pathway prediction methods that conclude that machine learning-based approaches are similar in performance to the most used method, PathoLogic which is a rule-based method. One issue is that previous studies evaluated PathoLogic without taxonomic pruning which decreases its performance. Results: In this study, we update the evaluation results from previous studies to demonstrate that PathoLogic with taxonomic pruning outperforms previous machine learning-based approaches and that further improvements in performance need to be made for them to be competitive. Furthermore, we introduce mlXGPR, a XGBoost-based metabolic pathway prediction method based on the multi-label classification pathway prediction framework introduced from mlLGPR. We also improve on this multi-label framework by utilizing correlations between labels using classifier chains. We propose a ranking method that determines the order of the chain so that lower performing classifiers are placed later in the chain to utilize the correlations between labels more. We evaluate mlXGPR with and without classifier chains on single-organism and multi-organism benchmarks. Our results indicate that mlXGPR outperform other previous pathway prediction methods including PathoLogic with taxonomic pruning in terms of hamming loss, precision and F1 score on single organism benchmarks. Conclusions: The results from our study indicate that the performance of machine learning-based pathway prediction methods can be substantially improved and can even outperform PathoLogic with taxonomic pruning. [ABSTRACT FROM AUTHOR]

This paper proposes a load shedding model for the island microgrid based on the ranking of loads and the power stability index (PSI). Loads are ranked based on the improved analytic hierarchy process (AHP) algorithm. Real-time measurement systems have the function of collecting data for very important, important, and less-important loads at each bus load. From this data, the improved AHP method is applied to rank the loads. The advantage of this method is that the subjectivity is eliminated and not depending on the expertise of the system operator when implementing the traditional AHP method. Besides, the minimum amount of load shedding power is calculated, taking into account both primary and secondary control methods. The objective is to minimize the impact on power consumers and ensure that the frequency returns to an acceptable range. In addition, when implementing load shedding, voltage quality, and stability are considered. The PSI serves as a crucial parameter for assessing the voltage stability of microgrid buses. This index is combined with load ranking weights to obtain combined weights for the load shedding plan. Consequently, the proposed load shedding plan prioritizes minimizing damage to customers, improving voltage quality and stability, and ensuring frequency is within permissible limits. The 16-bus microgrid system is applied to compare with traditional methods and to prove the efficiency of the suggested technique. [ABSTRACT FROM AUTHOR]

Climate models provide the required input data for global or regional climate impact analysis in temporally aggregated form, often in daily resolution to save space on data servers. Today, many impact models work with daily data; however, sub-daily climate information is becoming increasingly important for more and more models from different sectors, such as the agricultural, water, and energy sectors. Therefore, the open-source Teddy tool (temporal disaggregation of daily climate model data) has been developed to disaggregate (temporally downscale) daily climate data to sub-daily hourly values. Here, we describe and validate the temporal disaggregation, which is based on the choice of daily climate analogues. In this study, we apply the Teddy tool to disaggregate bias-corrected climate model data from the Coupled Model Intercomparison Project Phase 6 (CMIP6). We choose to disaggregate temperature, precipitation, humidity, longwave radiation, shortwave radiation, surface pressure, and wind speed. As a reference, globally available bias-corrected hourly reanalysis WFDE5 (WATCH Forcing Data methodology applied to ERA5) data from 1980–2019 are used to take specific local and seasonal features of the empirical diurnal profiles into account. For a given location and day within the climate model data, the Teddy tool screens the reference data set to find the most similar meteorological day based on rank statistics. The diurnal profile of the reference data is then applied on the climate model. The physical dependency between variables is preserved, since the diurnal profile of all variables is taken from the same, most similar meteorological day of the historical reanalysis dataset. Mass and energy are strictly preserved by the Teddy tool to exactly reproduce the daily values from the climate models. For evaluation, we aggregate the hourly WFDE5 data to daily values and apply the Teddy tool for disaggregation. Thereby, we compare the original hourly data with the data disaggregated by Teddy. We perform a sensitivity analysis of different time window sizes used for finding the most similar meteorological day in the past. In addition, we perform a cross-validation and autocorrelation analysis for 30 globally distributed samples around the world that represent different climate zones. The validation shows that Teddy is able to reproduce historical diurnal courses with high correlations >0.9 for all variables, except for wind speed (>0.75) and precipitation (>0.5). We discuss the limitations of the method regarding the reproduction of precipitation extremes, interday connectivity, and disaggregation of end-of-century projections with strong warming. Depending on the use case, sub-daily data provided by the Teddy tool could make climate impact assessments more robust and reliable. [ABSTRACT FROM AUTHOR]

Uncertainty estimation methods using deep learning approaches strive against separating how uncertain the state of the world manifests to us via measurement (objective end) from the way this gets scrambled with the model specification and training procedure used to predict such state (subjective means) — e.g., number of neurons, depth, connections, priors (if the model is bayesian), weight initialization, etc. This poses the question of the extent to which one can eliminate the degrees of freedom associated with these specifications and still being able to capture the objective end. Here, a novel non-parametric quantile estimation method for continuous random variables is introduced, based on the simplest neural network architecture with one degree of freedom: a single neuron. Its advantage is first shown in synthetic experiments comparing with the quantile estimation achieved from ranking the order statistics (specifically for small sample size) and with quantile regression. In real-world applications, the method can be used to quantify predictive uncertainty under the split conformal prediction setting, whereby prediction intervals are estimated from the residuals of a pre-trained model on a held-out validation set and then used to quantify the uncertainty in future predictions — the single neuron used here as a structureless "thermometer" that measures how uncertain the pre-trained model is. Benchmarking regression and classification experiments demonstrate that the method is competitive in quality and coverage with state-of-the-art solutions, with the added benefit of being more computationally efficient. [ABSTRACT FROM AUTHOR]

The ingenious approach of Chatterjee (2021) to estimate a measure of dependence first proposed by Dette et al. (2013) based on simple rank statistics has quickly caught attention. This measure of dependence has the appealing property of being between 0 and 1, and being 0 or 1 if and only if the corresponding pair of random variables is independent or one is a measurable function of the other almost surely. However, more recent studies (Cao & Bickel 2020 ; Shi et al. 2022b) showed that independence tests based on Chatterjee's rank correlation are unfortunately rate inefficient against various local alternatives and they call for variants. We answer this call by proposing an improvement to Chatterjee's rank correlation that still consistently estimates the same dependence measure, but provably achieves near-parametric efficiency in testing against Gaussian rotation alternatives. This is possible by incorporating many right nearest neighbours in constructing the correlation coefficients. We thus overcome the ' only one disadvantage' of Chatterjee's rank correlation (Chatterjee, 2021 , § 7). [ABSTRACT FROM AUTHOR]

In this work, we reveal some distributional traits of concomitants of order statistics (COSs) arising from the extended Farlie–Gumbel–Morgenstern (FGM) bivariate distribution, which was developed and studied in recent work. The joint distribution and product moments of COSs for this family are discussed. Moreover, some useful recurrence relations between single and product moments of concomitants are obtained. In addition, the asymptotic behavior of the concomitant's rank for order statistics (OSs) is studied. The information measures, differential entropy, Kullback–Leibler (KL) distance, Fisher information number (FIN), and cumulative past inaccuracy (CPI) are theoretically and numerically studied. [ABSTRACT FROM AUTHOR]

The sustainable Supplier Evaluation and Selection and Order Allocation (SSOA) problem has received significant attention in supply chain management due to its potential to enhance a company's performance, improve customer satisfaction, and reduce costs. In this study, an integrated methodology is proposed to address the SSOA problem. The methodology combines multiple techniques to handle the uncertainties associated with supplier evaluation, including a new ranking method based on the concept of Radius of Gyration (ROG) for interval type-2 fuzzy sets. The methodology also incorporates both subjective weights obtained using the Simple Multi-Attribute Rating Technique (SMART) and expert preferences, and objective weights calculated using the Method based on the Removal Effects of Criteria (MEREC) method to determine the weights of evaluation criteria. Some criteria for sustainable development are used to evaluate supplier performance, resulting in type-2 fuzzy sets, which are evaluated using the Weighted Aggregated Sum Product Assessment (WASPAS) method. The ROG-based ranking method is employed to calculate the relative scores of suppliers. Finally, a multi-objective decision-making (MODM) mathematical model is presented to identify suitable suppliers and allocate their order quantities. The methodology is demonstrated in a sustainable SSOA problem and is shown to be efficient and effective, as the ROG-based ranking method allows for more accurate supplier performance evaluation, and the use of the criteria highlights the importance of sustainability in supplier selection and order allocation. The methodology's practicality is further supported by the analysis conducted in this study, which demonstrates the methodology's ability to handle the uncertainties associated with supplier evaluation and selection. The proposed methodology offers a comprehensive approach to the SSOA problem that can effectively handle the uncertainties in supplier evaluation and selection and promote sustainable practices in supply chain management. [ABSTRACT FROM AUTHOR]

Pattern mining is a valuable tool for exploratory data analysis, but identifying relevant patterns for a specific user is challenging. Various interestingness measures have been developed to evaluate patterns, but they may not efficiently estimate user-specific functions. Learning user-specific functions by ranking patterns has been proposed, but this requires significant time and training samples. In this paper, we present a solution that formulates the problem of learning pattern ranking functions as a multi-criteria decision-making problem. Our approach uses an analytic hierarchy process (AHP) to elicit weights for different interestingness measures based on user preference. We also propose an active learning mode with a sensitivity-based heuristic to minimize user ranking queries while still providing high-quality results. Experiments show that our approach significantly reduces running time and returns precise pattern ranking while being robust to user mistakes, compared to state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

In order to enable users to quickly make a reasonable choice from a large number of manufacturing cloud services with similar functions, on the basis of diversity of the evaluation indicators and the uncertainty of evaluation information, the evaluation method of manufacturing cloud services was proposed in conjunction with grey theory.Firstly, the QoS evaluation model of manufacturing cloud service was constructed and described. Secondly, evaluation structure was established, and the non-quantitative indicator was also defined by intuitionistic fuzzy number. Furthermore, the manufacturing cloud service ranking method based on intuitionistic fuzzy and grey theory was designed. With constructing intuitionistic fuzzy comprehensive matrix and obtaining optimal attribute matrix, the grey correlation coefficients between evaluation attributes and the optimal service attributes were solved for each manufacturing cloud service, and then the correlation degree was determined so as to provide a ranking reference for users. The experimental results show that: Then the proposed algorithm changes with the attribute weight value, and the ranking results of the candidate services also change, indicating that the algorithm has a certain flexibility. Finally, the service quality of the candidate manufacturing cloud service was compared with the proposed algorithm and the intuitive fuzzy TOPSIS method and the entropy right TOPSIS method. The closeness of the proposed algorithm is all greater than the comparison algorithm, which is improved by about 12% compared with the intuitive fuzzy TOPSIS method and about 44% compared with the entropy right TOPSIS method, which verifies its rationality and practicability. [ABSTRACT FROM AUTHOR]

Salivary gland malignancies are rare and are often acompanied by poor prognoses. So, identifying the populations with risk factors and timely intervention to avoid disease progression is significant. This study provides an effective prediction model to screen the target patients and is helpful to construct a cost‐effective follow‐up strategy. We enrolled 249 patients diagnosed with salivary gland tumors and analyzed prognostic risk factors using Cox proportional hazard univariable and multivariable regression models. The patients' data were split into training and validation sets on a 7:3 ratio, and the random survival forest (RSF) model was established using the training sets and validated using the validation sets. The maximally selected rank statistics method was used to determine a cut point value corresponding to the most significant relation with survival. Univariable Cox regression suggested age, smoking, alcohol consumption, untreated, neural invasion, capsular invasion, skin invasion, tumors larger than 4 cm, advanced T and N stage, distant metastasis, and non‐mucous cell carcinoma were risk factors for poor prognosis, and multivariable analysis suggested that female, aging, smoking, untreated, and non‐mucous cell carcinoma were risk factors. The time‐dependent ROC curve showed the AUC of the RSF prediction model on 1‐, 2‐, and 3‐year survival were 0.696, 0.779, and 0.765 respectively in the validation sets. Log‐rank tests suggested that the cut point 7.42 risk score calculated from the RSF was most effective in dividing patients with significantly different prognoses. The prediction model based on the RSF could effectively screen patients with poor prognoses. [ABSTRACT FROM AUTHOR]

Background/Purpose: Systemic lupus erythematosus (SLE) is a multisystem autoimmune disease, with a potential for significant disease damage, morbidity, and mortality. In comparison to the adult population, childhood-onset SLE (cSLE) tends to be more aggressive given the higher preponderance of renal and neuropsychiatric disease and increased disease activity. There is a paucity of literature examining relationship between disease activity, rheumatology follow-up visits, and health care utilization. The objective of this study is to determine whether adherence with outpatient clinic visits would affect disease activity in patients with childhood-onset systemic lupus erythematosus (cSLE). Methods: 41 children <18 years of age at time of diagnosis with SLE who met Systemic Lupus International Collaborative Clinics (SLICC) criteria and not evaluated in clinic within the previous 120-day period were identified as eligible for inclusion. Patients were continuously searched between December 2021 and July 2022 for eligibility evaluation. Through retrospective chart review, we assessed disease activity (SLE Disease Activity Index) at the last clinic visit. The patients were stratified into two cohorts of lower and higher disease activity, with SLE disease activity index (SLEDAI) ≤ 3 and SLEDAI ≥ 4, respectively. Descriptive statistics and Willcox Rank Sum (numerical variables) and Fisher's test (categorical variables) were used to compare these two groups. Results: Clinical, epidemiological, and serological data were compared between the two groups, with observed statistically significant differences to include current use of high dose prednisone associated with higher SLEDAI scores (p = 0.019). In nonparametric analysis, time to follow-up (p < 0.001), hospitalizations (p = 0.017), and Emergency Department visits (ED) (p < 0.001) were found to be associated with higher SLEDAI scores. Conclusion: Our findings suggest that cSLE patients with higher disease activity are at risk for increased health care utilization with respect to ED visits as well as hospitalizations in the setting of follow-up nonadherence. While further studies are required to enhance our understanding of this association, this links the importance of disease-related outcome and routine outpatient visits in this particularly vulnerable patient population. [ABSTRACT FROM AUTHOR]

In today's global environment, supplier selection is one of the critical strategic decisions made by supply chain management. The supplier selection process involves the evaluation of suppliers based on several criteria, including their core capabilities, price offerings, lead times, geographical proximity, data collection sensor networks, and associated risks. The ubiquitous presence of internet of things (IoT) sensors at different levels of supply chains can result in risks that cascade to the upstream end of the supply chain, making it imperative to implement a systematic supplier selection methodology. This research proposes a combinatorial approach for risk assessment in supplier selection using the failure mode effect analysis (FMEA) with hybrid analytic hierarchy process (AHP) and the preference ranking organization method for enrichment evaluation (PROMETHEE). The FMEA is used to identify the failure modes based on a set of supplier criteria. The AHP is implemented to determine the global weights for each criterion, and PROMETHEE is used to prioritize the optimal supplier based on the lowest supply chain risk. The integration of multicriteria decision making (MCDM) methods overcomes the shortcomings of the traditional FMEA and enhances the precision of prioritizing the risk priority numbers (RPN). A case study is presented to validate the combinatorial model. The outcomes indicate that suppliers were evaluated more effectively based on company chosen criteria to select a low-risk supplier over the traditional FMEA approach. This research establishes a foundation for the application of multicriteria decision-making methodology for unbiased prioritization of critical supplier selection criteria and evaluation of different supply chain suppliers. [ABSTRACT FROM AUTHOR]

Selecting the appropriate climate models and determining their strengths and weaknesses are the key steps in examining climate change impacts on water resources. In this paper, the efficiency of monthly, seasonal, and annual precipitation estimations by 21 Global Climate Models (GCMs) provided by the NASA Earth Exchange Global Daily Downscaled Projections dataset (NEX-GDDP) was evaluated against the Observational Precipitation (OP) of 50 synoptic stations located in eight Precipitation Zones (PZs) of Iran. First, the average of GCMs precipitation for the period 1976–2005 was evaluated vs the OP data at both PZ and national levels. Then the efficiency of the GCMs and their Ensemble Average (EA) in estimating monthly, seasonal, and annual precipitation were determined using NSE, NRMSE, KGE, and PBIAS statistics, and subsequently, the GCMs were ranked according to their statistics. The results show that the time variability of the GCMs' average annual precipitation agrees with that of the OP, but it underestimates the extreme values. The agreement of the models' estimations with observation is generally lowest in summer and the highest in winter and spring. Most of the GCMs show a lower ability in precipitation estimation in dry seasons than in wet seasons. EA of the models for all individual months shows the highest errors in the coastal areas of the Caspian Sea, especially for the spring and winter seasons. The examination of the GCMs' efficiency at the national level shows that the EA and GFDL-ESM2G models have the least errors at monthly and annual time scales, respectively. [ABSTRACT FROM AUTHOR]

We propose a novel sequential change point detection method in linear models. Our method uses a given historical data set to determine the prechange model. Significant features are selected using the ranking procedure, which is an innovative approach aimed at revealing the rank of all features in terms of their effects on the model. We establish the asymptotic properties of the test statistic under the null and alternative hypotheses. Simulations are conducted to illustrate the performance of the proposed method. We conclude with a real data application to illustrate the detection procedure. [ABSTRACT FROM AUTHOR]

The comparison of benchmark error sets is an essential tool for the evaluation of theories in computational chemistry. The standard ranking of methods by their mean unsigned error is unsatisfactory for several reasons linked to the non-normality of the error distributions and the presence of underlying trends. Complementary statistics have recently been proposed to palliate such deficiencies, such as quantiles of the absolute error distribution or the mean prediction uncertainty. We introduce here a new score, the systematic improvement probability, based on the direct system-wise comparison of absolute errors. Independent of the chosen scoring rule, the uncertainty of the statistics due to the incompleteness of the benchmark datasets is also generally overlooked. However, this uncertainty is essential to appreciate the robustness of rankings. In the present article, we develop two indicators based on robust statistics to address this problem: Pinv, the inversion probability between two values of a statistic, and Pr, the ranking probability matrix. We demonstrate also the essential contribution of the correlations between error sets in these scores comparisons. [ABSTRACT FROM AUTHOR]

In the present article, Dai et al. ([5]) studies FDR control of the GM and DS methods for generalized linear models (GLMs) under a unified framework. Specifically, Xing, Zhao, and Liu ([16]) develops the Gaussian Mirror (GM) method for controlling FDR in high-dimensional linear regressions. Wang and Ramdas ([15]) uses e-value and develops the e-BH procedure to control FDR. With the maturity of data collection techniques, the analysis of data with high-dimensional input is now routinely conducted. [Extracted from the article]

Background: The role of neutrophil–lymphocyte ratio (NLR) as a predictor for survival in single fraction SBRT-treated non-small cell lung cancer (NSCLC) patients remains unclear. We performed an observational cohort study to determine the role of pretreatment NLR in predicting survival of early-stage NSCLC patients after single fraction SBRT. Methods: A single-institution database of peripheral early-stage NSCLC patients treated with SBRT from February 2007 to May 2022 was queried. Optimal threshold of neutrophil–lymphocyte ratio (NLR) was defined based on maximally selected rank statistics. Cox multivariable analysis (MVA), Kaplan–Meier, and propensity score matching were performed to evaluate outcomes. Results: A total of 286 patients were included for analysis with median follow up of 19.7 months. On Cox multivariate analysis, as a continuous variable, NLR was shown to be an independent predictor of OS (adjusted hazards ratio [aHR] 1.06, 95% CI 1.02–1.10, p = 0.005) and PFS (aHR 1.05, 95% CI 1.01–1.09, p = 0.013). In addition, NLR was associated with DF (aHR 1.11, 95% CI 1.05–1.18, p < 0.001). Maximally selected rank statistics determined 3.28 as the cutoff point of high NLR versus low NLR. These findings were confirmed upon propensity matching. Conclusions: Pretreatment NLR is an independent predictor for survival outcomes of peripheral early-stage NSCLC patients after single fraction SBRT. [ABSTRACT FROM AUTHOR]

The large dynamic and high-speed flight of aerospace vehicle will bring unpredictable conditions to its navigation system, resulting in that its system random noise probability distribution will no longer meet the preconditions of Gaussian distribution preset by the existing filter algorithm, thus reducing the accuracy of the navigation system. So, it is very important to propose an effective method to solve the filter problem of the navigation system in non-Gaussian distribution to improve the accuracy of the navigation system. Therefore, an integrated navigation method of aerospace vehicle based on rank statistics (LRF) has been proposed in this paper. Firstly, based on the flight characteristics of aerospace vehicles, an accurate gravity calculation model has been established to improve the accuracy of system modelling. Then, the state equation and measurement equation of integrated navigation system have been established. In combination with the rank filter algorithm as well as the determined weights, sampling points are calculated and nonlinearly propagated through the transition matrix to achieve an accurate estimation about the predicted values of the state quantities and measurement quantities and the covariance matrix. In turn, it simulates the probability distribution of the system state effectively. Therefore, when the system random noise probability distribution of the aerospace vehicle does not meet the Gaussian distribution due to various interference factors in the actual flight process, the algorithm can simulate the probability distribution of the actual system to the greatest extent, to improve the accuracy of the integrated navigation system and enhance the reliability of the navigation system ultimately. [ABSTRACT FROM AUTHOR]

We develop a new class of distribution-free multiple testing rules for false discovery rate (FDR) control under general dependence. A key element in our proposal is a symmetrized data aggregation (SDA) approach to incorporating the dependence structure via sample splitting, data screening, and information pooling. The proposed SDA filter first constructs a sequence of ranking statistics that fulfill global symmetry properties, and then chooses a data-driven threshold along the ranking to control the FDR. The SDA filter substantially outperforms the Knockoff method in power under moderate to strong dependence, and is more robust than existing methods based on asymptotic p-values. We first develop finite-sample theories to provide an upper bound for the actual FDR under general dependence, and then establish the asymptotic validity of SDA for both the FDR and false discovery proportion control under mild regularity conditions. The procedure is implemented in the R package sdafilter. Numerical results confirm the effectiveness and robustness of SDA in FDR control and show that it achieves substantial power gain over existing methods in many settings. [ABSTRACT FROM AUTHOR]

Ultrasound images are often contaminated by speckle noise during the acquisition process, which influences the performance of subsequent applications. Hence, it is necessary to design an effective algorithm for despeckling to obtain a clearer ultrasound image. According to the low-rank property of ultrasound images and the statistical property of similar image patch matrices, a nonlocal low-rank model with an improved data fidelity function (LRDF) is introduced in this paper, which integrates the weighted nuclear norm minimization (WNNM) and an improved data fidelity term. The advantage of WNNM is that it can adaptively assign weights on different singular values to preserve more details in restored images. The fidelity term deduced from log-compressed images fits better to ultrasonic data. We adopt the alternating direction method of multipliers (ADMM) to solve this nonconvex optimization problem. The experimental results on simulated images and real medical ultrasound images verify the reweighting strategy is helpful in this application and demonstrate the excellent performance of the proposed method compared with other five state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Objective: Despite controversy over its origin and definition, the significance of tumour deposit (TD) has been underestimated in the tumour node metastasis (TNM) staging system for colon cancer, especially in stage III patients. We aimed to further confirm the prognostic value of TD in stage III colon cancer and to establish a more accurate 'coN' staging system combining TD and lymph node metastasis (LNM). Methods: Information on stage III colon cancer patients with a definite TD status was retrospectively collected from the Surveillance, Epidemiology and End Results (SEER) database between 2010 and 2017. The effect of TD on prognosis was estimated using Cox regression analysis. Maximally selected rank statistics were used to select the optimal cut‐off value of TD counts. The predictive power of conventional N staging and the new coN staging was evaluated and compared by Akaike's information criterion (AIC), Harrell's concordance index (C‐index) and time‐dependent receiver operating characteristic (ROC) curves. Clinicopathological data of stage III colon cancer patients in the Xiangya database from 2014 to 2018 were collected to validate the coN staging system. Results: A total of 39,185 patients with stage III colon cancer were included in our study: 38,446 in the SEER cohort and 739 in the Xiangya cohort. The incidence of TD in stage III colon cancer was approximately 30% (26% in SEER and 30% in the Xiangya database). TD was significantly associated with poorer overall survival (OS) (HR = 1.37, 95% CI 1.31–1.44, p < 0.001 in SEER). The optimal cut‐off value of TD counts was 4, and the patients were classified into the TD0 (count = 0), TD1 (count = 1–3) and TD2 (count ≥ 4) groups accordingly. The estimated 5‐year OS was significantly different among the three groups (69.4%, 95% CI 68.8%–70.0% in TD0; 60.5%, 95% CI 58.9%–62.2% in TD1 and 42.6%, 95% CI 39.2%–46.4% in TD2, respectively, p < 0.001). The coN system integrating LNM and TD was established, and patients with stage III colon cancer were reclassified into five subgroups (coN1a, coN1b, coN2a, coN2b and coN2c). Compared with conventional N staging, the coN staging Cox model had a smaller AIC (197097.581 vs. 197358.006) and a larger C‐index (0.611 vs. 0.601). The AUCs of coN staging at 3, 5 and 7 years were also greater than those of conventional N staging (0.6305, 0.6326, 0.6314 vs. 0.6186, 0.6197, 0.6160). Concomitant with the SEER cohort results, the coN staging Cox model of the Xiangya cohort also had a smaller AIC (2883.856 vs. 2906.741) and a larger C‐index (0.669 vs. 0.633). Greater AUCs at 3, 5 and 7 years for coN staging were also observed in the Xiangya cohort (0.6983, 0.6774, 0.6502 vs. 0.6512, 0.6368, 0.6199). Conclusions: Not only the presence but also the number of TDs is associated with poor prognosis in stage III colon cancer. A combined N staging system integrating LNM and TD provides more accurate prognostic prediction than the latest AJCC N staging in stage III colon cancer. [ABSTRACT FROM AUTHOR]

Since real-world data are often inaccurate and working with fuzzy data and Z-numbers are very important and necessary, and in the real world, we need to rank and compare data, in this article, we introduce a new method for ranking Z-numbers. This ranking algorithm is based on centroid point. We evaluate the distance between the centroid points, and based on this distance, we rank the Z-numbers. The algorithm of this method has simple calculations and it provides satisfactory results for data in different conditions; we use this method in two practical examples. First, by ranking the return on assets of Tehran stock exchanges, with this method, we can choose the most risky stock and the least risky stock among the available stocks. The advantage of this method over conventional fuzzy methods is considering uncertainty and allocating credit in the opinion of experts to estimate fuzzy parameters, and in the second example, we ranked the factors which affect the productivity of tourism security. Then, by using the proposed algorithm, we introduce the most important and the least important factors for tourist security budgeting. [ABSTRACT FROM AUTHOR]

We study the ranking of individuals, teams, or objects, based on pairwise comparisons between them, using the Bradley-Terry model. Estimates of rankings within this model are commonly made using a simple iterative algorithm first introduced by Zermelo almost a century ago. Here we describe an alternative and similarly simple iteration that provably returns identical results but does so much faster--over a hundred times faster in some cases. We demonstrate this algorithm with applications to a range of example data sets and derive a number of results regarding its convergence. [ABSTRACT FROM AUTHOR]

This article studies expected performance and practical feasibility of the most commonly used classes of source-level likelihood-ratio (LR) systems when applied to a trace–reference comparison problem. The article compares performance of these classes of LR systems (used to update prior odds) to each other and to the use of prior odds only, using strictly proper scoring rules as performance measures. It also explores practical feasibility of the classes of LR systems. The present analysis allows for a ranking of these classes of LR systems: from specific-source feature-based to common-source anchored or non-anchored score-based. A trade-off between performance and practical feasibility is observed, meaning that the best performing class of LR systems is the hardest to realize in practice, while the least performing class is the easiest to realize in practice. The other classes of LR systems are in between the two extremes. The one positive exception is a common-source feature-based LR system, with good performance and relatively low experimental demands. This article also argues against the claim that some classes of LR systems should not be used, by showing that all systems have merit (when updating prior odds) over just using the prior odds (i.e. not using the LR system). [ABSTRACT FROM AUTHOR]

Analysis of variance is a cornerstone of statistical hypothesis testing. When data lies beyond the assumption of univariate normality, nonparametric methods including rank based statistics and permutation tests are enlisted. The permutation test is a versatile exact nonparametric significance test that requires drastically fewer assumptions than similar parametric tests. The main downfall of the permutation test is high computational cost making this approach laborious for comparing multiple samples of complex data types and completely infeasible in any application requiring speedy results such as high throughput streaming data. We rectify this problem through application of concentration inequalities and thus propose a computation free permutation test—that is, a permutation-less permutation test. This general framework is applied to multivariate and matrix-valued, but with a special emphasis on functional data. We improve these concentration bounds via a novel incomplete beta transform. Our theory is extended from two-sample to k-sample testing through the use of weakly dependent Rademacher chaoses and modified decoupling inequalities. Our methodology is tested on classic functional datasets including the Berkeley growth curves and the phoneme dataset. We further analyze a novel dataset of 12 spoken vowel sounds that was collected to illustrate to power of the analytic permutation test. for this article are available online. [ABSTRACT FROM AUTHOR]

Attribute selection has been proved to be an effective trick to strengthen the classification capability of Bayesian network classifiers, such as Averaged n -Dependence Estimators (AnDE). However, conventional mutual information-based attribute ranking considers only the correlation between the attribute and the class, regardless of the redundancies among the attributes. In this paper, we propose a new ranking approach, called Maximin Conditional Mutual Information (MMCMI), which first minimises the conditional mutual information for any unsorted attribute with regard to the sorted attribute sequence, and then maximise the minimal conditional mutual information within all unsorted attributes. When ranking the very first attribute, the mutual information with the class is maximised within all attributes. Extensive empirical results demonstrate that the MMCMI ranking approach together with attribute selection framework achieves significantly superior classification performance and less classification time with respect to regular AnDE and the mutual information counterparts. [ABSTRACT FROM AUTHOR]

Mountainous regions are often faced with various challenges of monitoring and predicting accurate rainfall due to complex topography. Satellite precipitation estimates serve as rich repositories of data, which are highly valuable for varied applications. However, it is essential to prioritize satellite estimates based on their performance in capturing the weather patterns over mountainous terrains like Northwest Himalayas (NWH). The present study has spatially ranked five satellite estimates, namely, APHRODITE-V1901, CHIRPS-V2.0, CMORPH-V1.0, PERSIANN-CDR-V1, and TMPA-3B42-V7 against the Indian Meteorological Department observed data at 0.25° × 0.25° resolution over the period 1998–2018. An ensemble methodology that incorporates the ranking methods like GRA, MAUT, TOPSIS, and WASPAS and the statistical metrics is proposed. The analysis is based on precipitation indices such as consecutive wet days, R20mm, Rx1day, Rx5day, total precipitation, R95p, R99p, and SDII. The results show that, in order to monitor rainfall across the NWH region, APHRODITE-V1901 secured the first ranking, followed by PERSIANN-CDR-V1, while CHIRPS-V2.0 received the last rank. Further, NWH is divided into five regions, i.e., region I, region II, region III, region IV, and region V concerning elevation. The ensemble merged ranking method is applied in each of the classified regions. The results reveal that APHRODITE-V1901 secured the first rank in region II, CHIRPS-V2.0 in region IV, CMORPH-V1.0 in region I, PERSIANN-CDR-V1 in region V, and TMPA-3B42-V7 in region III. The analysis of the extreme rainfall events is performed towards the end using the best satellite estimates for the designated locations. [ABSTRACT FROM AUTHOR]

Open in new tab Download slide OBJECTIVES The Berlin Heart EXCOR (BHE) offers circulatory support across all paediatric ages. Clinically, the necessary care and the outcomes differ in various age groups. The EUROMACS database was used to study age- and size-related outcomes for this specific device. METHODS All patients <19 years of age from the EUROMACS database supported with a BHE between 2000 and November 2021 were included. Maximally selected rank statistics were used to determine body surface area (BSA) cut-off values. Multivariable Cox proportional hazard regression using ridge penalization was performed to identify factors associated with outcomes. RESULTS In total, 303 patients were included [mean age: 2.0 years (interquartile range: 0.6–8.0, males: 48.5%)]. Age and BSA were not significantly associated with mortality (n  = 74, P  = 0.684, P  = 0.679). Factors associated with a transplant (n  = 175) were age (hazard ratio 1.07, P  = 0.006) and aetiology other than congenital heart disease (hazard ratio 1.46, P  = 0.020). Recovery rates (n  = 42) were highest in patients with a BSA of <0.53 m2 (21.8% vs 4.3–7.6% at 1 year, P  = 0.00534). Patients with a BSA of ≥0.73 m2 had a lower risk of early pump thrombosis but a higher risk of early bleeding compared to children with a BSA of <0.73 m2. CONCLUSIONS Mortality rates in Berlin Heart-supported patients cannot be predicted by age or BSA. Recovery rates are remarkably high in the smallest patient category (BSA <0.53 m2). This underscores that the BHE is a viable therapeutic option, even for the smallest and youngest patients. [ABSTRACT FROM AUTHOR]

The January 2022 edition of webometric ranking placed Yaba College of Technology as number one from 152 polytechnics in Nigeria. The ranking weight is 66 for country ranking, 8162 world ranking, Impact, Openness, and Excellence of 9698, 4558, and 7190 respectively. The negative variation and low webometric ranking of Yaba College of Technology that happened to be the first higher institution of learning in Nigeria with the slogan the first and still the best is a point of concern and motivates this research work. This research work collected data to evaluate the indicators for webometric ranking among the students and staff of Yaba College of Technology, a total of 346 were sampled students 44.51 % and Staff 55.49 %. The discussion and analysis of data obtained revealed that the poor webometric ranking is due to inadequacy of the necessary ICT infrastructure to encourage robust web presence; non-availability of up-to-date and scanty content on the Polytechnics website; Non-frequent usage of the Polytechnic website by the staff and students of the Polytechnic; the inadequate number of external networks (subnets) links with Polytechnic website; insufficient number of the top-cited publications in high impact Journals from the staff of the Yaba College of Technology; and Scanty number of the profile of staff from the Polytechnic on Google Scholar and ResearchGate, etc. among others. This research work opined that low webometric ranking could result in the following negative impact on the polytechnics lowering the esteem of the Polytechnic in the eyes of stakeholders, potential students and funding agencies, academic exchange with reputable institutions from other parts of the world for teaching, learning and research may writhe. The consequence of our findings recommendations was made to improve webometric ranking in future. [ABSTRACT FROM AUTHOR]

Obesity has reached epidemic proportions in the United States, but little is known about the mechanisms of weight gain and weight loss. Integration of omics data is becoming a popular tool to increase understanding in such complex phenotypes. Biomarkers come in abundance, but small sample size remains a serious limitation in clinical trials. In the present study, we developed a strategy to screen predictors from a multiomics, high-dimensional, and longitudinal dataset from a small cohort of 10 women with obesity who were provided an identical very-low calorie diet. Our proposal explores the combinatorial space of potential predictors from transcriptomics, microbiome, metabolome, fecal bile acids, and clinical data with the application of the first-order Spearman partial correlation coefficient. Two statistics are proposed for screening predictors, the partial association score, and the persistent significance. We applied our strategy to predict rates of weight loss in our sample of participants in a hospital metabolic facility. Our method reduced an initial set of 42,000 biomarker candidates to 61 robust predictors. The results show baseline fecal bile acids and regulation in RT-polymerase chain reaction as the most predictive data sources in forecasting the rate of weight-loss. In summary, the present study proposes a strategy based on nonparametric statistics for ranking and screening predictors of weight loss from a multiomics study. The proposed biomarker screening strategy warrants further translational clinical investigation in obesity and other complex clinical phenotypes. [ABSTRACT FROM AUTHOR]

A spatial sampling design determines where sample locations are placed in a study area so that population parameters can be estimated with good precision. Spatially balanced designs draw samples with good spatial spread and provide precise results for commonly used estimators when surveying natural resources. In this article, we propose a new sampling strategy that incorporates ranking information from nearby locations into a spatially balanced sample. If the population exhibits spatial trends, our simple local ranking strategy can improve the precision of commonly used estimators. Numerical results on several test populations with different spatial structures show that local ranking can improve the performance of a spatially balanced design. To show that local ranking is simple and effective in practice, we provide an example application for the health and productivity assessment of a Shiraz vineyard in South Australia. Supplementary materials accompanying this paper appear online. [ABSTRACT FROM AUTHOR]

In this article, we introduce a new nonparametric Shewhart − type control chart based on rank statistics. The setup of the proposed distribution − free monitoring scheme is presented in detail, while a real data example is studied in order to shed light on the implementation of the new control chart. Explicit formulae for the basic characteristics of the proposed rank-based chart are deduced. A detailed numerical study carried out depicts the performance of the new monitoring scheme for both in-control and out-of-control situations. [ABSTRACT FROM AUTHOR]

We tackle the problem of discovering novel classes in an image collection given labelled examples of other classes. We present a new approach called AutoNovel to address this problem by combining three ideas: (1) we suggest that the common approach of bootstrapping an image representation using the labelled data only introduces an unwanted bias, and that this can be avoided by using self-supervised learning to train the representation from scratch on the union of labelled and unlabelled data; (2) we use ranking statistics to transfer the model’s knowledge of the labelled classes to the problem of clustering the unlabelled images; and, (3) we train the data representation by optimizing a joint objective function on the labelled and unlabelled subsets of the data, improving both the supervised classification of the labelled data, and the clustering of the unlabelled data. Moreover, we propose a method to estimate the number of classes for the case where the number of new categories is not known a priori. We evaluate AutoNovel on standard classification benchmarks and substantially outperform current methods for novel category discovery. In addition, we also show that AutoNovel can be used for fully unsupervised image clustering, achieving promising results. [ABSTRACT FROM AUTHOR]

Acoustic characteristics of a phoneme in one language cannot be directly accepted or extrapolated to characterize same or similar phoneme in another language to the accuracies required for the language-specific applications. Language-specific features are useful to enhance the capabilities of speech recognition systems, realistic speech synthesis systems in different languages and for language identification tasks. Though there are few limited results available on the characterization of vowels and plosives in Telugu language, there is no explicit study carried out to acoustically characterize the lateral approximants of Telugu. This research work aims at such an independent acoustic–phonetic study of Telugu laterals from the speech waveforms. The salient features of this study are accurate determination of temporal and spectral features using high sampling rate speech data. The analysis features include lateral segment duration, linear prediction spectral envelopes, formants and antiformants estimated using customized MATLAB programs. Formant tracks were built and laid down on the spectral envelopes for visual inspection and for identifying the consonantal segment in the laterals. Duration of lateral segment and the movement of formants across the lateral segment and neighbouring vowel context were quantified for word-initial and intervocalic positions of lateral approximants. The quantitative analysis used the rank statistics, and visualization used the Box–Jenkins plots and the scatter plots of formants and antiformants in F1–F2 and F1–F3 planes. Descriptive analysis is carried out on lateral segment durations and the first three formant shifts. [ABSTRACT FROM AUTHOR]

Joint ranking statistics are used to distinguish real from random coincidences, ideally considering whether shared parameters are consistent with each other as well as whether the individual candidates are distinguishable from noise. We expand on previous works to include additional shared parameters, we use galaxy catalogues as priors for sky localization and distance, and avoid some approximations previously used. We develop methods to calculate this statistic both in low-latency using HEALPix sky maps, as well as with posterior samples. We show that these changes lead to a factor of one to two orders of magnitude improvement for GW170817-GRB 170817A depending on the method used, placing this significant event further into the foreground. We also examined the more tenuous joint candidate GBM-GW150914, which was largely penalized by these methods. Finally, we performed a simplistic simulation that argues these changes could better help distinguish between real and random coincidences in searches, although more realistic simulations are needed to confirm this. [ABSTRACT FROM AUTHOR]

We investigate an asymptotic behaviour of probabilities of large deviations for normalized combinatorial sums. We find a zone in which these probabilities are equivalent to the tail of the standard normal law. Our conditions are similar to the classical Bernstein conditions. The range of the zone of the normal convergence can be of a power order. • Large deviations of combinatorial sums are normal under Bernstein's condition. • Linear rank statistics are combinatorial sums. • Spearman's rank correlation coefficient is a combinatorial sum. • Conjugate distributions method yields large deviation results for combinatorial sums. [ABSTRACT FROM AUTHOR]

We develop purely nonparametric methods for the analysis of repeated measures designs with missing values. Hypotheses are formulated in terms of purely nonparametric treatment effects. In particular, data can have different shapes even under the null hypothesis and therefore, a solution to the nonparametric Behrens-Fisher problem in repeated measures designs will be presented. Moreover, global testing and multiple contrast test procedures as well as simultaneous confidence intervals for the treatment effects of interest will be developed. All methods can be applied for the analysis of metric, discrete, ordinal, and even binary data in a unified way. Extensive simulation studies indicate a satisfactory control of the nominal type-I error rate, even for small sample sizes and a high amount of missing data (up to 30%). We apply the newly developed methodology to a real data set, demonstrating its application and interpretation. [ABSTRACT FROM AUTHOR]

Comparative judgments abound in sports. Fans and pundits bandy about which of two players or teams is bigger, faster, stronger, more talented, less injury prone, more reliable, safer to bet on, riskier to trade for, and so on. Arguably, of most interest are judgments of a coarser type: which of two players or teams is, all-things-considered, just plainbetter? Conventionally, it is accepted that such comparisons can be appropriately captured and expressed by sports rankings. Rankings play an important role in sports arguablybecauseof the conventional acceptance that rankings capture and express all-things-considered relations between the ranked teams or players. Standard ranking practices rely on a number of widely held assumptions. I discuss three of the most important and argue thatat least oneof them must be false. If this is right, the strong and growing commitment to using rankings to determine participation in tournaments and the awarding of championships is mistaken. At the least, given the conventional wisdom about rankings, my argument provides good reasons to be skeptical thatanyparticular ranking ‘gets it right’. At the limit, it suggests that our most basic assumptions about all-things-considered athletic quality are wrong. [ABSTRACT FROM PUBLISHER]

• Generalization of binary sequences to multivariate improves data representation. • The weighted-probability of pattern occurrence is constructed based on complexity. • The MDS based on the novel dissimilarity measure is constructed. • The MDS creates better clustering results when compared with other methods. • The method has an acceptable running time and possesses robustness to noisy data. In this paper, a new dissimilarity measure for more accurate feature extraction and clustering is put forward. The method is proposed from the perspective of the weighted-probability distribution of dispersion patterns and their rank order statistics, where an effective quantization procedure is provided and the loss of information can be reduced. The proposed dissimilarity is applied in the multidimensional scaling (MDS) method to investigate simulated and reality-based signals. The comparative experiment shows that the clustering results of the proposed technique are clearer and more appropriate. State-of-the-art techniques and conventional methods are both included in the comparative experiments. In particular, for the heartbeat signals, it is discovered that the distribution of the weighted-probabilities of the dispersion patterns can discriminate subjects with different physiological conditions and exhibit visible changes of the subject's dynamical features when aging and disease attacks are taken place, which can be regarded as a microscopic insight of the dynamical mechanisms. [ABSTRACT FROM AUTHOR]

Aim: Sarcopenia has a high prevalence and can be an adverse predictor in patients with chronic liver diseases (CLDs). We sought to assess the prevalence of sarcopenia and its prognostic significance in patients with CLDs at multiple centers in Japan. Methods: In this retrospective study, we collated the data of 1624 patients with CLDs (976 men). The diagnosis of sarcopenia was determined by the sarcopenia assessment criteria of the Japan Society of Hepatology. Predictors of mortality were identified using univariate and multivariate analyses. Results: Muscle weakness and skeletal muscle loss occurred in 33.5% and 29.3% of all subjects, respectively, while sarcopenia occurred in 13.9% of all patients. Patients with sarcopenia had a poorer prognosis among all patients, patients with hepatocellular carcinoma (HCC), and those without HCC by log‐rank test. The multivariate Cox proportional hazards model identified female gender (hazard ratio [HR], 0.59; p = 0.03), alcoholic liver disease (HR, 4.25; p < 0.01), presence of HCC (HR, 6.77; p < 0.01), Child–Pugh classes A (HR, 1.42; p < 0.05), B (HR, 2.70; p < 0.01), and C (HR, 6.30; p < 0.01), and muscle weakness (HR, 2.24; p < 0.01) as significant adverse predictors. The cut‐off values of handgrip strength (HGS) for prognosis determined by maximally selected rank statistics were calculated as 27.8 kg for men and 18.8 kg for women. Conclusion: Reduced HGS in patients with CLD was an independent adverse predictor of mortality, with cut‐off values of 27.8 kg for men and 18.8 kg for women. [ABSTRACT FROM AUTHOR]

Binary classification is one of the central problems in machinelearning research and, as such, investigations of its general statistical properties are of interest. We studied the ranking statistics of items in binary classification problems and observed that there is a formal and surprising relationship between the probability of a sample belonging to one of the two classes and the Fermi-Dirac distribution determining the probability that a fermion occupies a given single-particle quantum state in a physical system of noninteracting fermions. Using this equivalence, it is possible to compute a calibrated probabilistic output for binary classifiers. We show that the area under the receiver operating characteristics curve (AUC) in a classification problem is related to the temperature of an equivalent physical system. In a similar manner, the optimal decision threshold between the two classes is associated with the chemical potential of an equivalent physical system. Using our framework, we also derive a closed-form expression to calculate the variance for the AUC of a classifier. Finally, we introduce FiDEL (Fermi-Dirac-based ensemble learning), an ensemble learning algorithm that uses the calibrated nature of the classifier's output probability to combine possibly very different classifiers. [ABSTRACT FROM AUTHOR]

In 2015, the United Nations established the Agenda 2030 for sustainable development, addressing the major challenges the world faces and introducing the 17 Sustainable Development Goals (SDGs). How are countries performing in their challenge toward sustainable development? We address this question by treating countries and Goals as a complex bipartite network. While network science has been used to unveil the interconnections among the Goals, it has been poorly exploited to rank countries for their achievements. In this work, we show that the network representation of the countries-SDGs relations as a bipartite system allows one to recover aggregate scores of countries' capacity to cope with SDGs as the solutions of a network's centrality exercise. While the Goals are all equally important by definition, interesting differences self-emerge when non-standard centrality metrics, borrowed from economic complexity, are adopted. Innovation and Climate Action stand as contrasting Goals to be accomplished, with countries facing the well-known trade-offs between economic and environmental issues even in addressing the Agenda. In conclusion, the complexity of countries' paths toward sustainable development cannot be fully understood by resorting to a single, multipurpose ranking indicator, while multi-variable analyses shed new light on the present and future of sustainable development. [ABSTRACT FROM AUTHOR]

Controlling and reducing process variability is an essential aspect for maintaining the product or service quality. Even though most practitioners believe that an increasing process variability is often a more severe concern than a shift in location, barely a few research paid attention to the cost‐efficient monitoring of process variability. Some of the existing studies addressed the dispersion aspect, assuming that the quality characteristic is Gaussian. Non‐normal and complex distributions are not uncommon in modern production processes, time to event processes, or processes involving service quality. Unfortunately, we find no literature on economically designed nonparametric (distribution‐free) schemes for monitoring process variability. This article introduces two Shewhart‐type cost‐optimized nonparametric schemes for monitoring the variability of any unknown but continuous processes to fill the research gap. The proposed monitoring schemes are based on two popular two‐sample rank statistics for differences in scale parameters, known as the Ansari–Bradley statistic and the Mood statistic. We assess their actual performance for a set of process scenarios and illustrate the design along with the implementation steps. We discuss a practical problem related to product quality management. It is expected that the proposed schemes will be beneficial in various industrial operations. [ABSTRACT FROM AUTHOR]

Unsupervised salient image generation without the aid of prior assumptions has many applications in computer vision. We present three unique real-time saliency generation algorithms that provide state-of-the-art performance for greyscale and colour images. Our fastest method run under 50 ms per frame on average. Our algorithm introduces a novel weighted histogram of orientation feature to supplement image intensity for monochromatic image manifold ranking. We also provide a method of dimensional reduction for the non-normalized optimal affinity matrix (OAM) using principal components analysis; this novel technique allows faster computation and stabilization of the OAM inversion process. We compare our methods with 18 traditional and recent techniques using three standard and custom datasets including ECSSD, DUT-OMRON and MSRA10K totalling 32,536 images for colour and greyscale variations. The results show our method to be more than 10 × faster than the RC and GMR models and having similar or better precision performances. [ABSTRACT FROM AUTHOR]

Rank statistics { R 1 , ... , R n } of actual variates { X 1 , ... , X n } play an important role in university undergraduate nonparametric statistics courses. This article derives explicit expressions of the correlation coefficients between Xi and Rj for not only i = j but also i ≠ j , for iid continuous variables X 1 , ... , X n with a distribution function F X (·) of X and n ≥ 2 : (a) ρ X i , R i = n − 1 n + 1 ρ X , F X (X) ∈ (0 , n − 1 n + 1 ] for any i, revealing that the correlation can be as close to one as expected, while may also unexpectedly decrease approaching zero for other distributions of X; (b) ρ X i , R j = − 1 n − 1 ρ X i , R i ∈ [ − 1 n 2 − 1 , 0) for any i ≠ j , inferring a negligible negative association with ranks from other data; (c) the partial correlation coefficient between Xi and Ri on Xj for any i ≠ j equals ρ (X i , R i) · X j = ρ X i , R i / 1 − ρ X j , R i 2 ∈ (ρ X i , R i , n − 1 n 2 − 2 ] , invariably exceeding ρ X i , R i . Implications of the results necessitate more relevant interpretation of ranks in sharing information of data. [ABSTRACT FROM AUTHOR]

Ranked data arise in many areas of application ranging from the ranking of up-regulated genes for cancer to the ranking of academic statistics journals. Complications can arise when rankers do not report a full ranking of all entities; for example, they might only report their top-M ranked entities after seeing some or all entities. It can also be useful to know whether rankers are equally informative, and whether some entities are effectively judged to be exchangeable. Revealing subgroup structure in the data may also be helpful in understanding the distribution of ranker views. In this paper, we propose a flexible Bayesian nonparametric model for identifying heterogeneous structure and ranker reliability in ranked data. The model is a weighted adapted nested Dirichlet (WAND) process mixture of Plackett–Luce models and inference proceeds through a simple and efficient Gibbs sampling scheme for posterior sampling. The richness of information in the posterior distribution allows us to infer many details of the structure both between ranker groups and between entity groups (within-ranker groups). Our modeling framework also facilitates a flexible representation of the posterior predictive distribution. This flexibility is important as we propose to use the posterior predictive distribution as the basis for addressing the rank aggregation problem, and also for identifying lack of model fit. The methodology is illustrated using several simulation studies and real data examples. for this article are available online. [ABSTRACT FROM AUTHOR]