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The ever-happening disruptive events interrupt the operationalisation of manufacturing organisations resulting in stalling the production flow and depleting societies with products. Advancements in cutting-edge technologies, viz. blockchain, artificial intelligence, virtual reality, digital twin, etc. have attracted the practitioners' attention to overcome such saddled conditions. This study attempts to explore the role of artificial intelligence (AI) in building the resilience of production function at manufacturing organisations during a COVID-19 pandemic. In this regard, a decision support system comprising an integrated voting analytical hierarchy process (VAHP) and Bayesian network (BN) method is developed. Initially, through a comprehensive literature review, the critical success factors (CSFs) for implementing AI are determined. Further, using a multi-criteria decision-making (MCDM) based VAHP, CSFs are prioritised to determine the prominent ones. Finally, the machine learning based BN method is adopted to predict and understand the influential CSFs that help achieve the highest production resilience. The present research is one of the early attempts to know the essence of AI and bridge the interplay between AI and production resilience during COVID-19. This study can support academicians, practitioners, and decision-makers in assessing the AI adoption in manufacturing organisations and evaluate the impact of different CSFs of AI on production resilience. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of stochastic jointly scheduling of resumable jobs and preventive maintenance on a single machine, subject to random breakdowns, to minimise the earliness-tardiness cost. The main objective is to investigate using trending machine learning-based methods compared to stochastic optimisation approaches. We propose two different methods from both fields as we solve the same problem firstly with a stochastic dynamic programming model in an approximation way, then with an attention-based deep reinforcement learning model. We conduct a detailed experimental study according to solution quality, run time, and robustness to analyse their performances compared to those of an existing approach in the literature as a baseline. Both algorithms outperform the baseline. Moreover, the machine learning-based algorithm outperforms the stochastic dynamic programming-based heuristic as we report up to 30.5% saving in total cost, a reduction of computational time from 67 min to less than $ 1s $ 1 s on big instances, and a better robustness. These facts highlight clearly its potential for solving such problems. [ABSTRACT FROM AUTHOR]

AbstractU-statistics play central roles in many statistical learning tools but face the haunting issue of scalability. Despite extensive research on accelerating computation by U-statistic reduction, existing results almost exclusively focused on power analysis. Little work addresses risk control accuracy, which requires distinct and much more challenging techniques. In this paper, we establish the first statistical inference procedure with provably higher-order accurate risk control for incomplete U-statistics. The sharpness of our new result enables us to reveal how risk control accuracy also trades off with speed, for the first time in literature, which complements the well-known variance-speed trade-off. Our general framework converts the challenging and case-by-case analysis for many different designs into a surprisingly principled and routine computation. We conducted comprehensive numerical studies and observed results that validate our theory’s sharpness. Our method also demonstrates effectiveness on real-world data applications. [ABSTRACT FROM AUTHOR]

Human factors contribute in some way to almost 93% of road crashes. Because the unavailability of good pre-crash data, the contribution of human factors to safety-critical events (SCEs) and the prediction of crashes using real-world data is lightly researched. This study provides predictive accuracy by harnessing unique real-world naturalistic driving study (NDS) data, which includes dynamic pre-crash information about driving behavior and performance. After cleaning and preprocessing, a final subsample (N = 9,237) was used and split into training and test samples. For consistent comparison of variables' importance in statistical and machine learning (ML) models, the dominance analysis uncovered the most important predictors used by the ordered Probit model. Next, three non-parametric supervised ML methods, because promising prediction performance and cost-effectiveness, including Naïve Bayes, K-Nearest Neighbors, and Gradient Boosting Tree (GBT) were used. The overall out-of-sample prediction accuracy for the ordered Probit model was 85.75% which was lower than all three ML methods. The GBT showed the highest (91.23%) out-of-sample prediction accuracy. The availability of pre-crash naturalistic data helps significantly improve the prediction accuracy of SCEs as cumulative importance for all available human factors in the GBT classifier was 94%. For practical applications, refer to the article. [ABSTRACT FROM AUTHOR]

Although algorithms are imbued with a sense of objectivity and reliability, numerous high-profile incidents have demonstrated their fallibility. In response, many have called for algorithmic governance that mitigates their potential harms. Further, these incidents have inspired studies that consider algorithms as part of wider sociotechnical systems. In this article, we build on such work and focus on how the specific forms of algorithms may facilitate or constrain the ways in which they become embedded within these systems. More specifically, we suggest that (a) algorithms should be understood as models, with (b) divergent forms, and (c) associated representational qualities. We showcase this approach in three critical case studies of algorithmic models used in government: the SAFFIER II model that underpins the Netherlands government's spending, the Ofqual DCP A-Level grading algorithm that was used (and later abandoned) in lieu of actual secondary school exams in the United Kingdom, and the Risk Classification Model used by the Dutch Tax and Customs Administration to identify social benefit fraud. With the three case studies, we show how the divergent forms of algorithms have implications for their responsiveness and ultimately their solidification in – or dissolution from – socio-technical systems. [ABSTRACT FROM AUTHOR]

Discovering and addressing unknown, including unanticipated, part-to-part variation sources is an important, yet challenging problem in manufacturing variation reduction. The state-of-art methods for solving this problem have focused solely on traditional mass manufacturing settings, in which abundant measurement data of parts with the same design are available. Applying these methods to custom manufacturing processes is problematic because the number of parts with the same design in custom manufacturing is often small. This paper proposes a new variation model that considers custom manufacturing parameters to aggregate measurement data across all custom parts. We also propose to estimate this model via a conditional autoencoder. The advantages of the proposed approach are demonstrated with a simulated toy-building brick example and a real cylindrical machining example. The approach successfully reveals unknown variation patterns even with a relatively small number of parts in these examples. Our approach is also generally applicable to any mainstream manufacturing processes that produce multiple part designs. [ABSTRACT FROM AUTHOR]

This study aims to propose an original approach to the interpretability of the explanatory variables (features) in the well-known expected goals (xG) model for shot analysis in football. To do this, a new original sample of 7801 shots from Italy's Serie A (1 binary outcome and 26 features) for the 2022/2023 and 2023/2024 seasons were used, in which 8 new features of various types were introduced, integrating event data, performance data, and tracking data. Specifically, the performance of 8 statistical and machine learning (algorithmic) classifiers was compared. The focus was on two key aspects related to the field of explainable Artificial Intelligence (xAI), ‘accuracy’ and ‘explainability’, assessed using some appropriate metrics. Considering the accuracy metrics, among the statistical classifiers Binary Regression (BR) with the cloglog link function is the most effective. In contrast, among the algorithmic classifiers, xGBoost has the best performance but is slightly lower than the BR-cloglog. Regarding explainability, the primary contribution to the xG consistently comes from a small set of variables across all classifiers. The most influential features are the proximity to the goal, the shooting angle, and the shooter's visual angle. [ABSTRACT FROM AUTHOR]

Learning to suppress location(s) where a distractor frequently occurs can improve search efficiency, known as distractor-location probability-cueing. However, the impact of the volatility of distractor occurrence – how often distractor-present and – absent events switch – remains poorly understood. To investigate this, we contrasted two volatility regimens in an additional-singleton search paradigm: a low-volatility environment in which distractor-present trials tended to occur in streaks, and a high-volatility environment with more frequent alternations. The distractor appeared 13 times more often at a designated frequent location than any rare locations. We replicated the probability-cueing effect, which was consistent across both volatilty conditions. Interestingly, the target-location effect – slower responses to a target at the frequent distractor location – was robust in the high-volatility condition, but non-significant in the low-volatility condition. We propose a suppression-thresholding account: the activation threshold of the saliency-triggered suppression mechanism is dynamically adjusted based on the volatility and local frequency of distractor occurrence. [ABSTRACT FROM AUTHOR]

Background: Dengue virus causes illnesses with or without warning indicators for severe complications. There are no clear prognostic signs linked to the disease outcomes. Methods: Clinical and laboratory parameters among 102 adult including 17 severe dengue (SD), 33 with warning and 52 without warning signs during early and critical phases were analysed by statistical and machine learning (ML) models. Results: In classical statistics, abnormal ultrasound findings, platelet count and low lymphocytes were significantly linked with SD during the febrile phase, while low creatinine, high sodium and elevated AST/ALT during the critical phase. ML models highlighted AST/ALT and lymphocytes as key markers for distinguishing SD from non-severe dengue, aiding clinical decisions. Conclusion: Parameters like liver enzymes, platelet counts and USG findings were linked with SD.USG testing at an earlier phase of dengue and a point-of-care system for the quantification of AST/ALT levels may lead to an early prediction of SD. [ABSTRACT FROM AUTHOR]

–Devising a dynamic pricing policy with always valid online statistical learning procedures is an important and as yet unresolved problem. Most existing dynamic pricing policies, which focus on the faithfulness of adopted customer choice models, exhibit a limited capability for adapting to the online uncertainty of learned statistical models during the pricing process. In this article, we propose a novel approach for designing a dynamic pricing policy based on regularized online statistical learning with theoretical guarantees. The new approach overcomes the challenge of continuous monitoring of the online Lasso procedure and possesses several appealing properties. In particular, we make the decisive observation that the always-validity of pricing decisions builds and thrives on the online regularization scheme. Our proposed online regularization scheme equips the proposed optimistic online regularized maximum likelihood pricing (OORMLP) pricing policy with three major advantages: encode market noise knowledge into pricing process optimism; empower online statistical learning with always-validity overall decision points; envelope prediction error process with time-uniform non-asymptotic oracle inequalities. This type of non-asymptotic inference results allows us to design more sample-efficient and robust dynamic pricing algorithms in practice. In theory, the proposed OORMLP algorithm exploits the sparsity structure of high-dimensional models and secures a logarithmic regret in a decision horizon. These theoretical advances are made possible by proposing an optimistic online Lasso procedure that resolves dynamic pricing problems at the process level, based on a novel use of non-asymptotic martingale concentration. In experiments, we evaluate OORMLP in different synthetic and real pricing problem settings and demonstrate that OORMLP advances the state-of-the-art methods. for this article are available online. [ABSTRACT FROM AUTHOR]

This article introduces a Factor Augmented Sparse Throughput (FAST) model that uses both latent factors and sparse idiosyncratic components for nonparametric regression. It contains many popular statistical models. The FAST model bridges factor models on one end and sparse nonparametric models on the other end. It encompasses structured nonparametric models such as factor augmented additive models and sparse low-dimensional nonparametric interaction models and covers the cases where the covariates do not admit factor structures. This model allows us to conduct high-dimensional nonparametric model selection for both strong dependent and weak dependent covariates and hence contributes to interpretable machine learning, particularly to the feature selections for neural networks. Via diversified projections as estimation of latent factor space, we employ truncated deep ReLU networks to nonparametric factor regression without regularization and to a more general FAST model using nonconvex regularization, resulting in factor augmented regression using neural network (FAR-NN) and FAST-NN estimators, respectively. We show that FAR-NN and FAST-NN estimators adapt to the unknown low-dimensional structure using hierarchical composition models in nonasymptotic minimax rates. We also study statistical learning for the factor augmented sparse additive model using a more specific neural network architecture. Our results are applicable to the weak dependent cases without factor structures. In proving the main technical result for FAST-NN, we establish a new deep ReLU network approximation result that contributes to the foundation of neural network theory. Numerical studies further support our theory and methods. for this article are available online. [ABSTRACT FROM AUTHOR]

Dynamic treatment regimen (DTR) is one of the most important tools to tailor treatment in personalized medicine. For many diseases such as cancer and type 2 diabetes mellitus (T2D), more aggressive treatments can lead to a higher efficacy but may also increase risk. However, few methods for estimating DTRs can take into account both cumulative benefit and risk. In this work, we propose a general statistical learning framework to learn optimal DTRs that maximize the reward outcome while controlling the cumulative adverse risk to be below a pre-specified threshold. We convert this constrained optimization problem into an unconstrained optimization using a Lagrange function. We then solve the latter using either backward learning algorithms or simultaneously over all stages based on constructing a novel multistage ramp loss. Theoretically, we establish Fisher consistency of the proposed method and further obtain non-asymptotic convergence rates for both reward and risk outcomes under the estimated DTRs. The finite sample performance of the proposed method is demonstrated via simulation studies and through an application to a two-stage clinical trial for T2D patients. for this article are available online. [ABSTRACT FROM AUTHOR]

BackgroundMethodResultsCardiovascular diseases (CVDs) include abnormal conditions of the heart, diseased blood vessels, structural problems of the heart, and blood clots. Traditionally, CVD has been diagnosed by clinical experts, physicians, and medical specialists, which is expensive, time-consuming, and requires expert intervention. On the other hand, cost-effective digital diagnosis of CVD is now possible because of the emergence of machine learning (ML) and statistical techniques.In this research, extensive studies were carried out to classify CVD via 19 promising ML models. To evaluate the performance and rank the ML models for CVD classification, two benchmark CVD datasets are considered from well-known sources, such as Kaggle and the UCI repository. The results are analysed considering individual datasets and their combination to assess the efficiency and reliability of ML models on the basis of various performance measures, such as precision, kappa, accuracy, recall, and the F1 score. Since some of the ML models are stochastic, we repeated the simulation 50 times for each dataset using each model and applied nonparametric statistical tests to draw decisive conclusions.The nonparametric Friedman – Nemenyi hypothesis test suggests that the Extra Tree Classifier provides statistically superior accuracy and precision compared with all other models. However, the Extreme Gradient Boost (XGBoost) classifier provides statistically superior recall, kappa, and F1 scores compared with those of all the other models. Additionally, the XGBRF classifier achieves a statistically second-best rank in terms of the recall measures. [ABSTRACT FROM AUTHOR]

Extracting regularities and probabilities from the environment is a fundamental and critical ability in an ever-changing surrounding. Previous findings showed that people are highly efficient in learning these regularities and that emotional stimuli are better learned than neutral ones. Yet, the generality and the underlying mechanism of this benefit are poorly understood. Here, participants viewed a stream of images with negative and neutral valence. Unbeknownst, the items recurred in regularity as triplets. Then, to assess learning, a surprised familiarity test was conducted. The results of Experiment 1, using two sets of stimuli, found better statistical learning for negative triplets than for neutral triplets. Experiment 2 revealed similar benefits even when only a single negative item was in the triplet at the second or third position, suggesting the advantage is not cumulative. We speculated that the predictability of the negative items is driving the effect. Consequently, Experiment 3 confirmed that the memory for neutral items preceding negative items was better than for neutral items preceding neutral items. Together, these findings provide novel insights into the mechanism of how the learning of incidental temporal associations is influenced by negative stimuli and the role of predictability in the negative valence benefit. [ABSTRACT FROM AUTHOR]

Loss reserving generally focuses on identifying a single model that can generate superior predictive performance. However, different loss reserving models specialise in capturing different aspects of loss data. This is recognised in practice in the sense that results from different models are often considered, and sometimes combined. For instance, actuaries may take a weighted average of the prediction outcomes from various loss reserving models, often based on subjective assessments. In this paper, we propose a systematic framework to objectively combine (i.e. ensemble) multiple stochastic loss reserving models such that the strengths offered by different models can be utilised effectively. Our framework contains two main innovations compared to existing literature and practice. Firstly, our criteria model combination considers the full distributional properties of the ensemble and not just the central estimate – which is of particular importance in the reserving context. Secondly, our framework is that it is tailored for the features inherent to reserving data. These include, for instance, accident, development, calendar, and claim maturity effects. Crucially, the relative importance and scarcity of data across accident periods renders the problem distinct from the traditional ensembling techniques in statistical learning. Our framework is illustrated with a complex synthetic dataset. In the results, the optimised ensemble outperforms both (i) traditional model selection strategies, and (ii) an equally weighted ensemble. In particular, the improvement occurs not only with central estimates but also relevant quantiles, such as the 75th percentile of reserves (typically of interest to both insurers and regulators). The framework developed in this paper can be implemented thanks to an R package, ADLP, which is available from CRAN. [ABSTRACT FROM AUTHOR]

Computational Medicine encompasses the application of Statistical Machine Learning and Artificial Intelligence methods on several traditional medical approaches, including biochemical testing which is extremely valuable both for early disease prognosis and long-term individual monitoring, as it can provide important information about a person's health status. However, using Statistical Machine Learning and Artificial Intelligence algorithms to analyze biochemical test data from Electronic Health Records requires several preparatory steps, such as data manipulation and standardization. This study presents a novel approach for utilizing Electronic Health Records from large, real-world databases to develop predictive precision medicine models by exploiting Artificial Intelligence. Furthermore, to demonstrate the effectiveness of this approach, we compare the performance of various traditional Statistical Machine Learning and Deep Learning algorithms in predicting individuals' future biochemical test outcomes. Specifically, using data from a large real-world database, we exploit a longitudinal format of the data in order to predict the future values of 15 biochemical tests and identify individuals at high risk. The proposed approach and the extensive model comparison contribute to the personalized approach that modern medicine aims to achieve. [ABSTRACT FROM AUTHOR]

Data science is booming with big data, and machine learning provides better predictive analyses. However, conventional statistical models can effortlessly interpret the effect estimates, and the prediction models are generally in a closed form. Therefore, this research integrates the logistic regression model's core with the Random Forest structure to create a blended novel machine learning method, the Random Logistic Machine (RLM). In this way, the new approach preserves the statistical and machine learning advantages. Computer simulation studies examined the predictive ability of RLM, random forest, and Logistic Regression under various scenarios. The results showed that the RLM delivers a comparable performance to Random Forests and Logistic Regression. An application to the Breast Cancer Wisconsin (Diagnostic) Data Set also demonstrates the superior performance of the new approach. [ABSTRACT FROM AUTHOR]

Objective: The prevalence of Driving Under the Influence (DUI) of alcohol or drugs has become a prominent factor in the occurrence of severe road crashes worldwide. Driving often occurs after visiting, and presumably drinking, at Alcohol-Serving Establishments (ASEs), and is thus of interest as a possible source of DUI events. Methods: We apply statistical and machine learning models to the Victorian Integrated Survey of Travel and Activity (VISTA) to identify factors that contribute to driving in trips from ASEs in Australia's state of Victoria. Results: Our results highlight that approximately 10% of individuals who traveled to ASEs as car passengers switched to driving after leaving there. It was also observed that travel distance shorter than 1 km and activity duration between 3 and 4 h positively impacts the mode switching from car driver to other modes in ASEs trips. Further findings illustrate a decline in driving after midnight, with an increase in the use of public transport and taxis. Individuals prefer driving for long-distance ASEs trips and walking for short distances. Going home also increased the likelihood of driving, whereas engaging in other social activities did not. Longer stays at ASEs and leaving vehicles overnight reduce the propensity to drive, likely due to increased alcohol consumption during these times. Conclusions: These findings suggest behavioral adjustments that can mitigate driving under the influence. Specifically, people may walk for short-distance trips and use public transport or taxis for longer ASEs trips. [ABSTRACT FROM AUTHOR]

This study aims to extend statistical learning (SL) research to multilinguals and provide an insight into what could facilitate word segmentation. We studied how the number of cues available in the input as well as the number of languages spoken influence SL and word segmentation. We used two SL tasks: one involving the tracking of transitional probabilities (TPs) between syllables of words, and another involving the tracking of two congruent cues-syllables and tones – in an artificial tone language. Data was collected from monolinguals, bilinguals, trilinguals, and quadrilinguals. Our results indicate that all language groups demonstrated similar SL capacity when segmenting words using TPs of syllables. However, when an additional cue was added, bilinguals, trilinguals, and quadrilinguals outperformed monolinguals. Interestingly, quadrilinguals also outperformed bilinguals. Performance was best for all groups when the input afforded two cues. This study suggests that while experience with multiple languages does not affect core SL ability, it enhances the tracking of multiple cues. The study further indicates that SL is affected by the number of cues available in the input as we found that performance was facilitated by the presence of two congruent cues. [ABSTRACT FROM AUTHOR]

This study addresses the potential for improving the accuracy, scope, and value of sales research through the application of data mining and machine learning algorithms. By examining prior research, identifying opportunities for improvement, and assessing gaps that can benefit from machine learning, research and application are made more accessible for sales researchers and managers. Machine learning can address important sales research questions that cannot be answered with the same accuracy or efficiency as traditional research methods. This study demonstrates the benefits of the methods through an example of application to the prediction of salesforce performance based on behavioral, attitudinal, and demographic data. This includes future research ideas, usage cases, and applications where machine learning could advance sales research and management. Machine learning and predictive analytics methods have multiple applications, including in B2C and B2B market contexts and for companies and independent sales teams. [ABSTRACT FROM AUTHOR]

In this article, we propose a distributed optimization approach to analyzing distributed-stored data based on linear regression with covariates missing at random. To solve distributed statistical learning problems, we construct a communication-efficient surrogate weighted loss function for the global weighted loss function based on inverse probability-weighted and communication-efficient surrogate loss method. Combining the advantages of the surrogate weighted loss function with alternating direction method of multipliers algorithm, a distributed algorithm is developed for the calculation of our proposed estimator. Under some mild assumptions, we establish the asymptotic properties of the proposed estimator. Simulation studies are performed to access the finite-sample performance of our method. We further illustrate the advantages of our method by a real-world dataset from Beijing Municipal Environmental Monitoring Center. [ABSTRACT FROM AUTHOR]

Sparse regression and classification estimators that respect group structures have application to an assortment of statistical and machine learning problems, from multitask learning to sparse additive modeling to hierarchical selection. This work introduces structured sparse estimators that combine group subset selection with shrinkage. To accommodate sophisticated structures, our estimators allow for arbitrary overlap between groups. We develop an optimization framework for fitting the nonconvex regularization surface and present finite-sample error bounds for estimation of the regression function. As an application requiring structure, we study sparse semiparametric additive modeling, a procedure that allows the effect of each predictor to be zero, linear, or nonlinear. For this task, the new estimators improve across several metrics on synthetic data compared to alternatives. Finally, we demonstrate their efficacy in modeling supermarket foot traffic and economic recessions using many predictors. These demonstrations suggest sparse semiparametric additive models, fit using the new estimators, are an excellent compromise between fully linear and fully nonparametric alternatives. All of our algorithms are made available in the scalable implementation grpsel. for this article are available online. [ABSTRACT FROM AUTHOR]

Simulation-based teaching can be a valuable method for learning statistical concepts. Its practical implementation for health-related subjects is seldomly evaluated. We propose a simulation-based approach to teach interaction effects in a postgraduate biostatistics course. We describe the steps involved in organizing and implementing a simulation-based activity and evaluate its execution. Mainly master and doctoral students in medical sciences (public health/epidemiology) participated to a 3-hr long online workshop on interaction effects. We presented one main learning activity, broken down into six progressive steps, from visualizing an interaction effect to investigating samples leading to erroneous inference. A total of 85 people attended of which 53 filled out an evaluation survey. Most (89%) of the respondents reported that the proposed activities helped them better understand interaction effects. A qualitative content analysis of free-text answers indicated that learning activities focusing on interaction effects in this way were useful for developing an understanding of both the meaning of statistical models and their interpretations. We conclude that a simulation-based approach can be a useful and interactive tool to create a valuable learning experience. The materials provided in this article can serve as a starting point for teachers who wish to implement such a method. [ABSTRACT FROM AUTHOR]

This study contributes to the call for influencing practice by increasing attention to how learning environments can be designed to support learning in statistical inference. We report on a design experiment in secondary school (students 14–16 years old), that resulted in a set of lessons with the learning goal of teaching students how to apply concepts and principles of hypothesis testing for making an inference as to whether or not students in secondary school can taste the difference between two brands of cola soda. The design experiment resulted in four design principles for a simulation-based approach for learning hypothesis testing in secondary school. The design principles highlight the combination of practical and digital simulations of samplings. They stress the need for using random generators that allow for high reliability in collecting sample data and introduce a simulation-based method for determining p-values, i.e. to quantify how likely or surprising a sample result, or a result more extreme, is under a null hypothesis. [ABSTRACT FROM AUTHOR]

Abstract– Distributed statistical learning has become a popular technique for large-scale data analysis. Most existing work in this area focuses on dividing the observations, but we propose a new algorithm, DDAC-SpAM, which divides the features under a high-dimensional sparse additive model. Our approach involves three steps: divide, decorrelate, and conquer. The decorrelation operation enables each local estimator to recover the sparsity pattern for each additive component without imposing strict constraints on the correlation structure among variables. The effectiveness and efficiency of the proposed algorithm are demonstrated through theoretical analysis and empirical results on both synthetic and real data. The theoretical results include both the consistent sparsity pattern recovery as well as statistical inference for each additive functional component. Our approach provides a practical solution for fitting sparse additive models, with promising applications in a wide range of domains. for this article are available online. [ABSTRACT FROM AUTHOR]

In practice, and especially when training deep neural networks, visual recognition rules are often learned based on various sources of information. On the other hand, the recent deployment of facial recognition systems with uneven performances on different population segments has highlighted the representativeness issues induced by a naive aggregation of the datasets. In this paper, we show how biasing models can remedy these problems. Based on the (approximate) knowledge of the biasing mechanisms at work, our approach consists in reweighting the observations, so as to form a nearly debiased estimator of the target distribution. One key condition is that the supports of the biased distributions must partly overlap, and cover the support of the target distribution. In order to meet this requirement in practice, we propose to use a low dimensional image representation, shared across the image databases. Finally, we provide numerical experiments highlighting the relevance of our approach. [ABSTRACT FROM AUTHOR]

Urban plantation dynamics in different topographical and climatic conditions in Odisha were evaluated using linear model selection and regularisation techniques. The main objective was to evaluate how and to what extent the urban plantations respond to various climatic and edaphic conditions. The relationship between vegetation growth and climatic and soil parameters was studied using four statistical learning tools, subset selection, ridge regression, lasso, and partial least squares regression, and their performance was compared to a multiple regression model. The test MSE for the subset selection, ridge regression, lasso, and partial least squares regression models was evaluated to be 16,261.54, 12245.11, 16263.79 and 14,317.21, respectively. Results proved that statistical learning methods, namely subset selection, lasso, ridge regressions and partial least squares regression, were more accurate than multiple linear regression. From the results, it can be safely concluded that temperature shows greater correlation with the growth parameters. Precipitation also plays a vital role in vegetation dynamics. Soil parameters indicate a positive correlation with that of the growth. [ABSTRACT FROM AUTHOR]

When a distractor frequently appears in a certain region of a search display, it interferes less with performance, reflecting learned distractor suppression. While this effect is well established, it remains unknown whether this suppression is ongoing (tonic) or deactivated upon task completion (phasic). To address this, the present eye-tracking study examined the time course of distractor suppression. Participants searched for and compared two targets to determine whether to make a response or withhold responding (“No-go” trials). The No-go trials allowed observation of post-decision suppression via eye movements. Response-critical target features either remained present for the whole trial (Experiment 1) or were removed halfway through (Experiment 2). In Experiment 1, distractors in the frequent region were substantially less likely to attract eye movements, including after inspection of both targets. However, in Experiment 2, the post-target distractor-region effect disappeared when the response-critical information was removed, indicating that suppression operates phasically and flexibly. [ABSTRACT FROM AUTHOR]

The performance of long-term prediction models is currently impeded due to the mismatch between the nonstationary representations of statistical learning models and the underlying dynamics from real-world systems, which results in low long-term prediction accuracies for many real-world applications. We present a Recurrent Gated Unit-based Mixture Kriging Machine Bayesian Filtering (ReGU-MKMBF) approach for characterizing nonstationary and nonlinear behaviors of one ubiquitous real-world process—dynamic intermittency. It models the transient dynamics in the state space as recurrent transitions between localized stationary segments/attractors. Then, a case study on predicting the onset of pathological symptoms associated with Electrocardiogram signals is presented. The results suggest that ReGU-MKMBF improves the forecasting performance by extending the prediction time horizon with an order of magnitudes while maintaining high accuracies on the foreseen estimates. Implementing the presented approach can subsequently change the current scheme of online monitoring and aftermath mitigation into a prediction and timely prevention for telecardiology. [ABSTRACT FROM AUTHOR]

Probabilistic models such as logistic regression, Bayesian classification, neural networks, and models for natural language processing, are increasingly more present in both undergraduate and graduate statistics and data science curricula due to their wide range of applications. In this article, we present a one-week course module for students in advanced undergraduate and applied graduate courses on variational inference, a popular optimization-based approach for approximate inference with probabilistic models. Our proposed module is guided by active learning principles: In addition to lecture materials on variational inference, we provide an accompanying class activity, an R shiny app, and guided labs based on real data applications of logistic regression and clustering documents using Latent Dirichlet Allocation with R code. The main goal of our module is to expose students to a method that facilitates statistical modeling and inference with large datasets. Using our proposed module as a foundation, instructors can adopt and adapt it to introduce more realistic case studies and applications in data science, Bayesian statistics, multivariate analysis, and statistical machine learning courses. [ABSTRACT FROM AUTHOR]

Motivated by a real problem in a big hospital in China, we study a daily surgery scheduling problem with operating room eligibility and dedicated surgeon. We model the problem as a parallel machine scheduling problem with machine and resource constraints to minimise the makespan, and innovatively propose an adaptive composite dispatching method to deal with such a strongly NP-hard problem. The dispatching rule is a combination of three popular rules LPT, LFJ and LRW, each of which can deal with some special features of the scheduling problems, and the scaling parameters are estimated through a statistical model learned from historical data. The adaptive composite dispatching method is easy-to-implement, fast, adaptable, robust and flexible. To examine the performance of the proposed solution approach, we first carry out a series of computational experiments showing that the adaptive composite dispatching method works very well compared to the optimal solution. Using a real data set, we further conduct a case study showing that our solution approach can improve the current practice by significantly shortening the makespan and reducing the overnight work. [ABSTRACT FROM AUTHOR]

AbstractMultistage manufacturing processes with identical stages provide three-dimensional process data in which the first dimension represents the process (control/sensing) variable, the second is the stage, and the third is the measurement/sampling/data acquisition time point. Diagnosing quality faults in such processes often requires the simultaneous identification of crucial process variables and stages associated with product quality anomalies. Most existing diagnosis methods convert 3D data into a 2D matrix, resulting in loss of information and reduced diagnostic accuracy and stability. To address this challenge, we propose a penalized tensor regression model that regresses the product quality index against its 3D process data. For the estimation of high-dimensional regression coefficients with the limited amount of historical data, we apply the CANDECOMP/PARAFAC and Tucker decompositions to the coefficient tensor, which significantly reduces the number of parameters to be estimated. Based on the decompositions, a new regularization term is designed to enable the joint identification of critical process variables and stages. To estimate the parameters, we develop the block coordinate proximal descent algorithm and provide its convergence guarantee. Numerical studies demonstrate that the proposed methods can enhance diagnostic stability and on average improve the diagnostic accuracy by around 20% over existing benchmarks. [ABSTRACT FROM AUTHOR]

Statistical learning (SL) approaches to reading maintain that proficient reading requires assimilation of the rich statistical regularities in the writing system. Reading skills in developing first- and second-language readers in English have been shown to be predicted by individual differences in sensitivity to regularities in mappings from orthography to phonology (O-P) and semantics (O-S), with good readers relying more on O-P consistency, and less on O-S associations. However, SL and its relation to reading has been primarily studied in English readers in high-income Western countries. We examine individual differences in sensitivity to regularities in emergent French readers in rural agricultural communities in Côte d'Ivoire (N = 134). We show that, in contrast to previous studies, in this cohort better readers are leveraging semantic associations more strongly, while individual differences in sensitivity to orthographic consistency did not predict reading skill. Relatively little variance in reading skill was explained by sensitivity to regularities, and we discuss these findings in terms of literacy acquisition in low-literacy and low-exposure contexts. This showcases the importance of cross-linguistic and cross-cultural research to back up universal theories of literacy, and suggests that current SL accounts of reading must be updated to account for this variance in reading skills. [ABSTRACT FROM AUTHOR]

The usual goal of supervised learning is to find the best model, the one that optimizes a particular performance measure. However, what if the explanation provided by this model is completely different from another model and different again from another model despite all having similarly good fit statistics? Is it possible that the equally effective models put the spotlight on different relationships in the data? Inspired by Anscombe's quartet, this article introduces a Rashomon Quartet, that is a set of four models built on a synthetic dataset which have practically identical predictive performance. However, the visual exploration reveals distinct explanations of the relations in the data. This illustrative example aims to encourage the use of methods for model visualization to compare predictive models beyond their performance. [ABSTRACT FROM AUTHOR]

Thanks to their ability to capture complex dependence structures, copulas are frequently used to glue random variables into a joint model with arbitrary marginal distributions. More recently, they have been applied to solve statistical learning problems such as regression or classification. Framing such approaches as solutions of estimating equations, we generalize them in a unified framework. We can then obtain simultaneous, coherent inferences across multiple regression-like problems. We derive consistency, asymptotic normality, and validity of the bootstrap for corresponding estimators. The conditions allow for both continuous and discrete data as well as parametric, nonparametric, and semiparametric estimators of the copula and marginal distributions. The versatility of this methodology is illustrated by several theoretical examples, a simulation study, and an application to financial portfolio allocation. for this article are available online. [ABSTRACT FROM AUTHOR]

We consider the problem of testing the equality of conditional distributions of a response variable given a vector of covariates between two populations. Such a hypothesis testing problem can be motivated from various machine learning and statistical inference scenarios, including transfer learning and causal predictive inference. We develop a nonparametric test procedure inspired from the conformal prediction framework. The construction of our test statistic combines recent developments in conformal prediction with a novel choice of conformity score, resulting in a weighted rank-sum test statistic that is valid and powerful under general settings. To our knowledge, this is the first successful attempt of using conformal prediction for testing statistical hypotheses beyond exchangeability. Our method is suitable for modern machine learning scenarios where the data has high dimensionality and large sample sizes, and can be effectively combined with existing classification algorithms to find good conformity score functions. The performance of the proposed method is demonstrated in various numerical examples. for this article are available online. [ABSTRACT FROM AUTHOR]

Earthquake early warning systems assess the size of an earthquake based on initial ground motion and provide warnings before the arrival of large waves. We employ the iterative regression to simultaneously estimate the station correlations and the corresponding linear relations for magnitude. We report the standard error reductions of the magnitude estimation based on the initial peak P-wave acceleration (${P_a}$ P a ), velocity (${P_v}$ P v ), and displacement (${P_d}$ P d ) as $14.52\% $ 14.52 % , $7.63\% $ 7.63 % , and $7.58\% $ 7.58 % , respectively. Factor analysis of the stationary correction reveals its correlation with several factors. This study shows that station correction can help to improve the precision of magnitude estimation in the future. [ABSTRACT FROM AUTHOR]

Subsampling plays a crucial role in tackling problems associated with the storage and statistical learning of massive datasets. However, most existing subsampling methods are model-based, which means their performances can drop significantly when the underlying model is misspecified. Such an issue calls for model-free subsampling methods that are robust under diverse model specifications. Recently, several model-free subsampling methods have been developed. However, the computing time of these methods grows explosively with the sample size, making them impractical for handling massive data. In this article, an efficient model-free subsampling method is proposed, which segments the original data into some regular data blocks and obtains subsamples from each data block by the data-driven subsampling method. Compared with existing model-free subsampling methods, the proposed method has a significant speed advantage and performs more robustly for datasets with complex underlying distributions. As demonstrated in simulation experiments, the proposed method is an order of magnitude faster than other commonly used model-free subsampling methods when the sample size of the original dataset reaches the order of 107. Moreover, simulation experiments and case studies show that the proposed method is more robust than other model-free subsampling methods under diverse model specifications and subsample sizes. [ABSTRACT FROM AUTHOR]

The nested data structure is prevalent for cognitive measure experiments due to repeatedly taken observations from different brain locations within subjects. The analysis methods used for this data type should consider the dependency structure among the repeated measurements. However, the dependency assumption is mainly ignored in the cognitive neuroscience data analysis literature. We consider both statistical, and machine learning methods extended to repeated data analysis and compare distinct algorithms in terms of their advantage and disadvantages. Unlike basic algorithm comparison studies, this article analyzes novel neuroscience data considering the dependency structure for the first time with several statistical and machine learning methods and their hybrid forms. In addition, the fitting performances of different algorithms are compared using contaminated data sets, and the cross-validation approach. One of our findings suggests that the GLMM tree, including random term indices indicating the location of functional near-infrared spectroscopy optodes nested within experimental units, shows the best predictive performance with the lowest MSE, RMSE, and MAE model performance metrics. However, there is a trade-off between accuracy and speed since this algorithm is required the highest computational time. [ABSTRACT FROM AUTHOR]

This research explores the intricate-coupling relationship between typhoons and the ionosphere by the high-frequency surface wave radar's (HFSWR) over-the-horizon capabilities. This letter describes the gravity wave features in travelling ionospheric disturbances (TIDs) and ionospheric drift information. Further discussion is raised on the chaotic properties of gravity wave features using the Lyapunov exponents and chaotic attractors, particularly as they are influenced by typhoon. To predict these gravity wave features more accurately, a new model is developed by synergizing phase space reconstruction theory and statistical learning theory, a chaotic prediction model for gravity wave features constructed on a third-order Volterra filter. The experimental findings confirm that the gravity waves features detected via HFSWR present the hyperchaotic behaviour. More importantly, the innovative gravity wave prediction model based on chaotic attractor can capture the dynamics of the original chaotic system. Notably, the model's prediction accuracy has been improved by 98.6% after the introduction of phase space reconstruction modelling. [ABSTRACT FROM AUTHOR]

The book 'Post-Shrinkage Strategies in Statistical and Machine Learning for High Dimensional Data' by Syed Ejaz Ahmed, Feryaal Ahmed, and Bahadir Yüzbaşı explores the challenges of analyzing high-dimensional data and offers strategies for obtaining valuable insights in the field of data science. The book covers various tools, techniques, challenges, and opportunities in data analysis, with a focus on both theory and practical applications. It addresses bias issues in machine learning and statistical practices and provides a comprehensive resource for students, researchers, and practitioners. The book fills a critical gap in the field and is dedicated to the memory of Don A. Fraser and Kjell Doksum. [Extracted from the article]

When searching for a target, humans can reduce interference from a colour distractor by using different strategies, such as down-weighting the distractor dimension or the most likely distractor location. Less clear is how the heterogeneity among the nontarget features, and the similarity of target and distractor modulate distractor interference and thereby distractor location learning. In three tasks, we examined this question by analyzing target reports and selection errors. In the mixed-feature task, participants searched for a singleton shape among homogeneously shaped nontargets, forcing participants to use singleton-detection mode. In two fixed-feature tasks, participants needed feature-search mode to find the target among heterogeneously shaped nontargets, which were either gray (gray fixed-feature task) or coloured (colour fixed-feature task). As expected, the results in the mixed-feature task showed evidence for distractor interference and distractor location learning with suppression of the high-probability location below a baseline level. In the fixed-feature tasks, the results differed: We observed distractor interference in the gray fixed-feature task, but a slight distractor-presence benefit in the colour fixed-feature task, possibly indicating up-weighting of the target on the featural level. Distractor location learning occurred in the first session of the colour fixed-feature task, but results in the gray fixed-feature task indicate that the participants relied on other strategies to handle distraction. Interestingly, we found overnight learning between sessions in all three tasks, which was more pronounced in the fixed-feature tasks. These results indicate that participants can flexibly adjust their attentional strategy based on the nontarget features to reduce distractor interference. [ABSTRACT FROM AUTHOR]

Traffic crashes vary in the manner in which the collision occurs (collision type), and countermeasures to reduce crashes might vary significantly based on this collision type. The inherent complexity in their mechanism has motivated this study to identify significant factors influencing collision types, with the goal of better countermeasure deployment. The objective of this work is to compare the performances of statistical and machine learning (ML) models in classifying crashes based on collision type, and assess their generalizability and interpretability. Discrete choice models, Bayesian classifiers, tree-based algorithms, and support vector machines are among the data-driven methods considered for comparison. Results indicate that tree-based algorithms perform consistently well and offer a higher interpretability, with out-of-distribution robustness. However, while ML models provide a flexible framework for modeling large data volumes, statistical models provide additional interpretability on the effect of critical variables on crash mechanisms – which is relevant from a safety management standpoint. [ABSTRACT FROM AUTHOR]

AbstractPurposeMethodResultConclusionEarly intervention based on principles of cross-situational statistical learning (CSSL) for late-talking children has shown promise. This study explored whether parents could be trained to deliver this intervention protocol with fidelity and if they found the intervention to be acceptable.Mothers of four English-speaking children aged 18–30 months who scored <10th centile for expressive vocabulary were recruited to an 8-week group training program. Parents were taught principles of CSSL and asked to perform 16 home treatment sessions (30 minutes each) in total, providing auditory bombardment of target words in full sentences at high dose number and syntactic variability, using a range of physical exemplars. Home diaries and two videotaped sessions measured treatment fidelity. Pre- and post-treatment questionnaires measured acceptability.One parent discontinued the study after the second group training session. Three parents completed 15/16 group training sessions and reported completing 87% of home sessions. Two parents demonstrated implementing the intervention as per the target dose number by the first fidelity session (Weeks 2/3), and the third parent was very close to meeting target dose number by the second fidelity session (Weeks 7/8).Parents can be trained to deliver an intervention based on cross-situational statistical learning principles. [ABSTRACT FROM AUTHOR]

This research explores the factors contributing to information sensitivity in debt markets, focusing on the potential influences of uncertainty, economic performance, and journalist-dependent language. Building upon the foundational work of Dang et al. (Ignorance, debt and financial crises. Yale University Unpublished Working Paper, 2018), we analyze the mechanisms underlying the transition from information-insensitive to information-sensitive states—a shift with implications for potential financial crises. Leveraging machine learning techniques and daily data on variables such as default probability, information acquisition, and newspaper articles, we discern specific narrative triggers embedded within the news. Our analysis underscores the pivotal role of economic states and journalist language in inducing information sensitivity—a phenomenon intricately tied to different psychological thinking processes. [ABSTRACT FROM AUTHOR]

Handling large datasets and calculating complex statistics on huge datasets require important computing resources. Using subsampling methods to calculate statistics of interest on small samples is often used in practice to reduce computational complexity, for instance using the divide and conquer strategy. In this article, we recall some results on subsampling distributions and derive a precise rate of convergence for these quantities and the corresponding quantiles. We also develop some standardisation techniques based on subsampling unstandardised statistics in the framework of large datasets. It is argued that using several subsampling distributions with different subsampling sizes brings a lot of information on the behaviour of statistical learning procedures: subsampling allows to estimate the rate of convergence of different algorithms, to estimate the variability of complex statistics, to estimate confidence intervals for out-of-sample errors and interpolate their values at larger scales. These results are illustrated on simulations, but also on two important datasets, frequently analysed in the statistical learning community, EMNIST (recognition of digits) and VeReMi (analysis of Network Vehicular Reference Misbehavior). [ABSTRACT FROM AUTHOR]

Children's health studies support an association between maternal environmental exposures and children's birth outcomes. A common goal is to identify critical windows of susceptibility—periods during gestation with increased association between maternal exposures and a future outcome. The timing of the critical windows and magnitude of the associations are likely heterogeneous across different levels of individual, family, and neighborhood characteristics. Using an administrative Colorado birth cohort we estimate the individualized relationship between weekly exposures to fine particulate matter (PM2.5) during gestation and birth weight. To achieve this goal, we propose a statistical learning method combining distributed lag models and Bayesian additive regression trees to estimate critical windows at the individual level and identify characteristics that induce heterogeneity from a high-dimensional set of potential modifying factors. We find evidence of heterogeneity in the PM2.5—birth weight relationship, with some mother—child dyads showing a three times larger decrease in birth weight for an IQR increase in exposure (5.9–8.5 μ g / m 3 PM2.5) compared to the population average. Specifically, we find increased vulnerability for non-Hispanic mothers who are either younger, have higher body mass index or lower educational attainment. Our case study is the first precision health study of critical windows. for this article are available online. [ABSTRACT FROM AUTHOR]

We establish a high-dimensional statistical learning framework for individualized asset allocation. Our proposed methodology addresses continuous-action decision-making with a large number of characteristics. We develop a discretization approach to model the effect of continuous actions and allow the discretization frequency to be large and diverge with the number of observations. We estimate the value function of continuous-action using penalized regression with our proposed generalized penalties that are imposed on linear transformations of the model coefficients. We show that our proposed Discretization and Regression with generalized fOlded concaVe penalty on Effect discontinuity (DROVE) approach enjoys desirable theoretical properties and allows for statistical inference of the optimal value associated with optimal decision-making. Empirically, the proposed framework is exercised with the Health and Retirement Study data in finding individualized optimal asset allocation. The results show that our individualized optimal strategy improves the financial well-being of the population. for this article are available online. [ABSTRACT FROM AUTHOR]

Habituation shows organisms’ ability to ignore irrelevant repetitive stimuli, allowing attentional resources to be allocated to the relevant information. Here we investigated the role of contextual information in generating expectations that control habituation to visual onset distractors, expectations that arise from associative learning. Experiment 1 showed that the habituated capture response is restored if the onset distractor is momentarily omitted in the same context of habituation, but not if such omission is experienced in another context. Experiment 2 showed that the pre-exposure to a context in which a distractor is later encountered reduced participants’ capacity to ignore it, thus attesting a latent learning phenomenon affecting habituation of capture. Finally, Experiment 3 showed that ignoring a new distractor is easier in a context in which habituation has already taken place for a different distractor. Taken together, these results attest the context-specific nature of habituation and suggest that learning to ignore salient visual stimuli is based on expectations encoded through associative learning. [ABSTRACT FROM AUTHOR]

Stimuli that appear abruptly in the visual field or differ from the surrounding stimuli based on a given visual feature can capture attention and interfere with the visual search process if they are not targets. When both types of distractors appear with higher likelihood at a given location, observers can learn to reduce their impact (distractor-location effect). In the case of feature-singleton distractors, this can imply a cost for processing targets that appear at locations associated with a high distractor probability (target-location effect). This has been proposed as evidence that distractor interference originates in the competition between distractors and targets within an attentional map, whose inputs can be modified by experience. In this study, we conduct a series of experiments that confirmed previous finding related to feature-singleton distractors, but consistently showed that learning to predict the spatial occurrence of luminance transients induces a distractor-location effect in the absence of a target-location effect. Combining this finding with the fact that interference by luminance transients were larger when distractors were far from the target position, we suggest that different mechanisms are responsible for the reduction of distractor interference in the case of color singletons and luminance transients. [ABSTRACT FROM AUTHOR]