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In the present paper, we are mainly concerned with the statistical inference for the functional of nonparametric copula models satisfying linear constraints. The asymptotic properties of the obtained estimates and test statistics are given. Finally, a general notion of bootstrap for the proposed estimates and test statistics, constructed by exchangeably weighting sample, is presented, which is of its own interest. These results are proved under some standard structural conditions on some classes of functions and some mild conditions on the model, without assuming anything about the marginal distribution functions, except continuity. Our theoretical results and numerical examples by simulations demonstrate the merits of the proposed techniques. [ABSTRACT FROM AUTHOR]

A method for determining the lines of variation (LOV) in the initial confidence region under strong nonlinearity in the orbit improvement problem is developed, based on finding the maximum modulus of the normal vector to the level surfaces of the objective function using the least-squares method. The method was tested for three asteroids using their observations on a short arc of the orbit, namely, the points of two variation lines corresponding to the directions of the greatest deformation of the initial confidence region were determined. Approximation of variation lines was performed using third-degree polynomials. Using the obtained approximations, new variables were introduced in which the initial confidence region is almost ellipsoidal, i.e., nonlinearity is nearly absent. The modeling of the initial probability dispersion of trajectories is performed in the space of new variables. The resulting dispersion can be further used to identify collision orbits and estimate the probability of asteroid collisions with Earth. [ABSTRACT FROM AUTHOR]

Model development activities are increasing in pavement research and engineering applications. At the same time, there is ambiguity and a lack of consistency with regard to checking and quantifying credibility and suitability for the intended application. Specifically, usage of the terms Verification and Validation (V&V) is seen to vary across contributions. In this context, this paper was motivated by the desire to provide a best-practice reference that underlines the significance of the V&V terms, clarifies their definition, and promotes a more unified usage. Accordingly, the objective was to offer examples that introduce the V&V jargon, demonstrate basic V&V concepts and processes, and highlight certain fine points. A total of five distinct models were introduced and discussed: (1) load-related responses in asphalt pavements, (2) load-related responses in concrete pavements, (3) crack initiation and propagation in asphalt concrete, (4) linear viscoelasticity of asphalt concrete, and (5) water flow through asphalt concrete pores. A general conclusion from this work is that successful V&V efforts are closely linked to a clear definition of the intended usage, i.e., the specific reality of interest being targeted by the model. It is also concluded that documenting V&V efforts is integral to any computational model development. Doing so communicates to potential users the region of confidence for the model alongside the expected differences with the reality of interest. [ABSTRACT FROM AUTHOR]

Inference after model selection is a very important problem. This article derives the asymptotic distribution of some model selection estimators for autoregressive moving average time series models. Under strong regularity conditions, the model selection estimators are asymptotically normal, but generally the asymptotic distribution is a non normal mixture distribution. Hence bootstrap confidence regions that can handle this complicated distribution were used for hypothesis testing. A bootstrap technique to eliminate selection bias is to fit the model selection estimator β ̂ M S ∗ to a bootstrap sample to find a submodel, then draw another bootstrap sample, and fit the same submodel to get the bootstrap estimator β ̂ M I X ∗ . [ABSTRACT FROM AUTHOR]

Simulating sediment transfer processes in catchments has contributed significantly to solving environmental problems due to its importance in the silting of rivers and reservoirs and for controlling the pollution of water bodies. Among the methods used to improve data collection and modelling, the "sediment fingerprinting approach" uses tracers reflecting the composition of eroded soils and sediments in multivariate statistical analyses and mathematical models for optimizing equation systems. Based on generalized least squares (GLS) method and Mahalanobis distance, this study sought to present a computational framework to solve over-determined systems applied to sediment tracing, systematize the uncertainty analysis and sample number optimization. Hence, this approach takes into account the influence of collinearity among the chemical variables that compose the tracer set to be evaluated by the presence of the variance-covariance matrix. A dataset from the Arvorezinha experimental catchment in southern Brazil was used to validate the modeling, and our findings confirmed the assumption of increased uncertainty as the number of target samples decreases in the sources or eroded sediment samples. Sharing the code files with the PySASF (Python package for Source Apportionment with Sediment Fingerprinting) contributes to improving the technique as it allows other researchers to systematically improve the definition of the number of samples required based on the uncertainty analysis. [ABSTRACT FROM AUTHOR]

In this article, we consider an empirical likelihood with vector observations that are martingale differences and prove a Wilks' type theorem under a conditional Lindeberg condition. We then generalize this result to approximate martingale differences. As applications of the first result we discuss the construction of confidence regions for various time series models, including linear and nonlinear autoregressive models, an ARCH(1) model, and nonlinear autoregressive models with heteroskedasticity. The second result is used to derive a Wilks' type theorem for a blockwise empirical likelihood for Markov chains. [ABSTRACT FROM AUTHOR]

AbstractThe scaled-uniform model has recently been considered to illustrate the problems that arise when the minimal sufficient statistic is not complete. We find the scale equivariant interval with the shortest expected length. Numerical computation confirms that the optimal interval outperforms all intervals considered by De Santis and Gubbiotti. We consider, additionally, the scale invariant Bayes posterior interval and the likelihood interval. These intervals also outperform the intervals considered by De Santis and Gubbiotti. Surprisingly, the likelihood interval is very close in performance to the optimal interval. Calculating the optimal interval is numerically challenging, while the likelihood interval is easy to compute. [ABSTRACT FROM AUTHOR]

This study presents a new method for simultaneously quantifying a complex anti-migraine formulation containing five components (ergotamine, propyphenazone, caffeine, camylofin, and mecloxamine) using UV spectrophotometry and chemometric models. The formulation presents analytical challenges due to the wide variation in component concentrations (ERG: PRO: CAF: CAM: MEC ratio of 0.075:20:8:5:4) and highly overlapping UV spectra. To create a comprehensive validation dataset, the Kennard-Stone Clustering Algorithm was used to address the limitations of arbitrary data partitioning in chemometric methods. Three different chemometric models were evaluated: Classical Least Squares (CLS), Partial Least Squares (PLS), and Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS). Among these, MCR-ALS demonstrated excellent performance, achieving recovery values of 98–102% for all components, accompanied by minimal root mean square errors of calibration (0.072–0.378) and prediction (0.077–0.404). Moreover, the model exhibited high accuracy, with relative errors ranging from 1.936 to 3.121%, bias-corrected mean square errors between 0.074 and 0.389, and a good sensitivity (0.2097–1.2898 μg mL−1) for all components. The Elliptical Joint Confidence Region analysis further confirmed the predictive performance of the models, with MCR-ALS consistently showing the smallest ellipses closest to the ideal point (slope = 1, intercept = 0) for most analytes, indicating superior accuracy and precision. The approach's sustainability was rigorously assessed using six advanced metrics, validating its environmental friendliness, economic viability, and practical application. This approach effectively resolves complex pharmaceutical formulations, contributing to sustainable development objectives in quality control processes. [ABSTRACT FROM AUTHOR]

AbstractIn spatial analysis, it is essential to understand and quantify spatial or temporal heterogeneity. This paper focuses on the generalized spatially varying coefficient model (GSVCM), a powerful framework to accommodate spatial heterogeneity by allowing regression coefficients to vary in a given spatial domain. We propose a penalized bivariate spline method for detecting local signals in GSVCM. The key idea is to use bivariate splines defined on triangulation to approximate nonparametric varying coefficient functions and impose a local penalty on L2 norms of spline coefficients for each triangle to identify null regions of zero effects. Moreover, we develop model confidence regions as the inference tool to quantify the uncertainty of the estimated null regions. Our method partitions the region of interest using triangulation and efficiently approximates irregular domains. In addition, we propose an efficient algorithm to obtain the proposed estimator using the local quadratic approximation. We also establish the consistency of estimated nonparametric coefficient functions and the estimated null regions. The numerical performance of the proposed method is evaluated in both simulation cases and real data analysis. [ABSTRACT FROM AUTHOR]

Background: Glucose-6-Phosphate Dehydrogenase deficiency (G6PDd) is a common genetic enzymopathy that can induce haemolysis triggered by various factors, including some anti-malarial drugs. Although many Point-of-Care (PoC) tests, such as Standard G6PD™ are available to detect G6PDd, its pooled diagnostic test accuracy (DTA) remains unknown. Methods: To estimate the DTA of StandG6PD-BS at various thresholds of G6PDd, a systematic review with a DTA meta-analysis were conducted, searching EMBASE, MEDLINE, and SciELO databases up to April 4, 2024.The included studies were those that measured G6PD activity using StandG6PD-BS (reference test) and spectrophotometry (gold standard) in patients suspected of having G6PDd. The risk of bias (RoB) of the studies was assessed using the QUADAS-2 tool and the certainty of evidence (CoE) with the GRADE approach. For the estimation of within-study DTA, a random-effect bivariate meta-analysis was performed to determine the pooled sensitivity and specificity for 30%, 70%, and 80% enzyme levels' thresholds, and a graphical analysis of the heterogeneity using crosshair and Confidence Regions on receiver operating characteristic (ROC) space plots. Results: After screening 2496 reports, four studies were included with 7864 participants covering all thresholds. Two studies had high RoB in QUADAS-2 domains 2 and 3, and the others had low RoB, with low, moderate, and high heterogeneity at the 30%, 70%, and 80% thresholds, respectively. The pooled sensitivity was 99.1%, 95.7%, and 90% for 30%, 70%, and 80% thresholds, respectively. The pooled specificity was 97.4%; 92.9%; and 89.0% for 30%, 70%, and 80% thresholds, respectively. Conclusion: StandG6PD-BS is a PoC test with high sensitivity and specificity to detect G6PDd at different thresholds. [ABSTRACT FROM AUTHOR]

Anisotropic specular appearances are ubiquitous in the environment: brushed stainless steel pans, kettles, elevator walls, fur, or scratched plastics. Real‐time rendering of these materials with image‐based lighting is challenging due to the complex shape of the bidirectional reflectance distribution function (BRDF). We propose an anisotropic specular image‐based lighting method that can serve as a drop‐in replacement for the standard bent normal technique [Rev11]. Our method yields more realistic results with a 50% increase in computation time of the previous technique, using the same high dynamic range (HDR) preintegrated environment image. We use several environment samples positioned along the major axis of the specular microfacet BRDF. We derive an analytic formula to determine the two closest and two farthest points from the reflected direction on an approximation of the BRDF confidence region boundary. The two farthest points define the BRDF major axis, while the two closest points are used to approximate the BRDF width. The environment level of detail is derived from the BRDF width and the distance between the samples. We extensively compare our method with the bent normal technique and the ground truth using the GGX specular BRDF. [ABSTRACT FROM AUTHOR]

When the bootstrap sample size is moderate, bootstrap confidence regions tend to have undercoverage. Improving the coverage is known as calibrating the confidence region. Consider testing H 0 : θ = θ 0 versus H 1 : θ ≠ θ 0 . We reject H 0 only if θ 0 is not contained in a large-sample 95% confidence region. If the confidence region has 3% undercoverage for the data set sample size, then the type I error is 8% instead of the nominal 5%. Hence, calibrating confidence regions is also useful for testing hypotheses. Several bootstrap confidence regions are also prediction regions for a future value of a bootstrap statistic. A new bootstrap confidence region uses a simple prediction region calibration technique to improve the coverage. The DD plot for visualizing prediction regions can also be used to visualize some bootstrap confidence regions. [ABSTRACT FROM AUTHOR]

The existent methods for constructing ambiguity sets in distributionally robust optimization often suffer from over-conservativeness and inefficient utilization of available data. To address these limitations and to practically solve multi-stage distributionally robust optimization (MDRO), we propose a data-driven Bayesian-type approach that constructs the ambiguity set of possible distributions from a Bayesian perspective. We demonstrate that our Bayesian-type MDRO problem can be reformulated as a risk-averse multi-stage stochastic programming problem and subsequently investigate its theoretical properties such as consistency, finite sample guarantee, and statistical robustness. Moreover, the reformulation enables us to employ cutting planes algorithms in dynamic settings to solve the Bayesian-type MDRO problem. To illustrate the practicality and advantages of the proposed model and algorithm, we apply it to a distributionally robust inventory control problem and a distributionally robust hydrothermal scheduling problem, and compare it with usual formulations and solution methods to highlight the superior performance of our approach. [ABSTRACT FROM AUTHOR]

We introduce a new methodology to conduct simultaneous inference of the non‐parametric component in partially linear time series regression models where the non‐parametric part is a multi‐variate unknown function. In particular, we construct a simultaneous confidence region (SCR) for the multi‐variate function by extending the high‐dimensional Gaussian approximation to dependent processes with continuous index sets. Our results allow for a more general dependence structure compared to previous works and are widely applicable to a variety of linear and non‐linear autoregressive processes. We demonstrate the validity of our proposed methodology by examining the finite‐sample performance in the simulation study. Finally, an application in time series, the forward premium regression, is presented, where we construct the SCR for the foreign exchange risk premium from the exchange rate and macroeconomic data. [ABSTRACT FROM AUTHOR]

Background: The impact of excluding intrahepatic segmental vessels from regions of interest (ROIs) on liver stiffness measurement (LSM) via magnetic resonance elastography (MRE) remains uncertain. Purpose: To determine the effect of excluding intrahepatic segmental vessels from ROIs on LSM obtained from MRE. Material and Methods: This retrospective analysis included 95 participants who underwent successful two-dimensional gradient recalled-echo MRE before hepatic tumor resection (n = 49) or living liver donation (n = 46). The conventional LSM was determined by manually drawing ROIs on the elastogram within the 95% confidence region, staying 1 cm within the liver capsule and excluding large hilar vessels, the gallbladder, hepatic lesions, and artifacts. In addition, the modified LSM was determined by excluding intrahepatic segmental vessels. LSMs obtained by the two methods were compared with paired sample signed-rank test. Diagnostic performance for advanced fibrosis was calculated and compared using McNemar's test and Delong's test. The stage of hepatic fibrosis was assessed using surgical specimens by the METAVIR system. Results: The modified LSM was larger than the conventional LSM (2.4 kPa vs. 2.2 kPa in reader 1; 2.7 kPa vs. 2.4 kPa in reader 2; P < 0.001). The modified LSM showed superior sensitivity (0.841 vs. 0.659 in reader 1; 0.864 vs. 0.705 in reader 2; P < 0.05) and area under the curve (0.901 vs. 0.820 in reader 1; 0.912 vs. 0.843 in reader 2; P < 0.05) for detecting advanced fibrosis (≥F3) than conventional LSM. Conclusion: The exclusion of intrahepatic segmental vessels from ROIs in MRE affected the LSM and enhanced diagnostic performance for advanced fibrosis. [ABSTRACT FROM AUTHOR]

Background: Parametric regression analysis is widely used in methods comparisons and more recently in checking the concordance of test results following receipt of new reagent lots. The greater frequency of reagent-lot evaluations increases pressure to detect bias with smallest possible sample sizes (i.e. smallest consumption of time and resources). This study revisits bias detection using the joint slope, intercept confidence region as an alternative to slope and intercept confidence intervals. Methods: Four cases were considered representing constant errors, proportional errors (constant CV) and two more complicated error patterns typical of immunoassays. Maximum:minimum range ratios varied from 2:1 to 2000:1. After setting a maximum tolerable difference a series of slope, intercept combinations, each of which predicted the critical difference, were systematically evaluated in simulations which determined the minimum sample size required to detect the difference, firstly using slope, intercept confidence intervals and secondly using the joint slope, intercept confidence region. Results: At small to moderate range ratios, bias detection by joint confidence region required greatly reduced sample sizes to the extent that it should encourage reagent-lot evaluations or, alternatively, transform those already routinely performed into considerably less costly exercises. Conclusions: While some software is available to calculate joint confidence regions in real-life analyses, shifting this testing method into the mainstream will require a greater number of software developers incorporating the necessary code into their regression programs. The computer program used to conduct this study is freely available and can be used to model any laboratory test. [ABSTRACT FROM AUTHOR]

This work is motivated by a gap in the functional data analysis literature, particularly in the context of neuroimaging, regarding the ability of functional models to robustly accommodate intra‐observation dependence. In response, we propose an M‐estimator based on generalized empirical likelihood for the varying‐coefficient model with a functional response. We develop statistical inference procedures, simultaneous confidence regions, and a global general linear hypothesis test for the model's functional coefficient. Our theoretical results establish the weak convergence of the log‐likelihood ratio process, a nonparametric version of Wilks' theorem for the log‐likelihood ratio, and asymptotic properties of the proposed estimator. Through a simulation study, we show that the proposed confidence sets have close‐to‐nominal coverage probabilities. In a real‐world application to a neuroimaging dataset, we show that mini‐mental state examination score and apolipoprotein E genotype have significant associations with fractional anisotropy, while associations with gender and age are only present at high quantile levels. [ABSTRACT FROM AUTHOR]

In this paper, we study the robust estimation and empirical likelihood for the regression parameter in generalized linear models with right censored data. A robust estimating equation is proposed to estimate the regression parameter, and the resulting estimator has consistent and asymptotic normality. A bias-corrected empirical log-likelihood ratio statistic of the regression parameter is constructed, and it is shown that the statistic converges weakly to a standard $ \chi ^2 $ χ 2 distribution. The result can be directly used to construct the confidence region of regression parameter. We use the bias correction method to directly calibrate the empirical log-likelihood ratio, which does not need to be multiplied by an adjustment factor. We also propose a method for selecting the tuning parameters in the loss function. Simulation studies show that the estimator of the regression parameter is robust and the bias-corrected empirical likelihood is better than the normal approximation method. An example of a real dataset from Alzheimer's disease studies shows that the proposed method can be applied in practical problems. [ABSTRACT FROM AUTHOR]

In many common situations, a Bayesian credible interval will be, given the same data, very similar to a frequentist confidence interval, and researchers will interpret these intervals in a similar fashion. However, no predictable similarity exists when credible intervals are based on model-averaged posteriors whenever one of the two nested models under consideration is a so called "point-null". Not only can this model-averaged credible interval be quite different than the frequentist confidence interval, in some cases it may be undefined. This is a lesser-known correlate of the Jeffreys-Lindley paradox and is of particular interest given the popularity of the Bayes factor for testing point-null hypotheses. [ABSTRACT FROM AUTHOR]

Local polynomial fitting exhibits numerous compelling statistical properties, particularly within the intricate realm of multivariate analysis. However, as functional data analysis gains prominence as a dynamic and pertinent field in data science, the exigency arises for the formulation of a specialized theory tailored to local polynomial fitting. We explored the intricate task of estimating the regression function operator and its partial derivatives for stationary mixing random processes, denoted as (Yi, Xi), using local higher-order polynomial fitting. Our key contributions include establishing the joint asymptotic normality of the estimates for both the regression function and its partial derivatives, specifically in the context of strongly mixing processes. Additionally, we provide explicit expressions for the bias and the variance-covariance matrix of the asymptotic distribution. Demonstrating uniform strong consistency over compact subsets, along with delineating the rates of convergence, we substantiated these results for both the regression function and its partial derivatives. Importantly, these findings rooted in reasonably broad conditions that underpinned the underlying models. To demonstrate practical applicability, we leveraged our results to compute pointwise confidence regions. Finally, we extended our ideas to the nonparametric conditional distribution, and obtained its limiting distribution. [ABSTRACT FROM AUTHOR]

The present work intends to discuss parameter estimation and statistical analysis in adsorption. The Langmuir and Tóth isotherm models are compared for a set of carbon dioxide adsorption data on 13X zeolite from literature at different temperatures: 303, 323, 373, and 423 K. Statistical analyses were performed under frequentist and Bayesian perspectives. Under the frequentist statistical view, parameters were estimated using Maximum Likelihood estimation (MLE). Statistical analyses of parameters were performed by confidence regions in terms of elliptical approximation and likelihood region, while the evaluation of models was performed by chi-square statistics. The results showed that, for these nonlinear models, the elliptical region offers a poor approximation of the parameter estimates' confidence region, especially for the most correlated parameter pairs. Additionally, the four-parameter Tóth's equation yields less correlated parameters than the three-parameter Langmuir model. From a Bayesian perspective, the Markov chain Monte Carlo (MCMC) technique facilitated the reconstruction of the probability density functions of parameters as well as enabled the propagation of parametric uncertainties in the model responses. Finally, the accurate assessment of experimental uncertainty significantly influences the evaluation of models and their respective parameters. [ABSTRACT FROM AUTHOR]

Summary: This article addresses the challenge of estimating the state of a discrete‐time stochastic multiplicative system with zonotopic set‐membership and stochastic uncertainties. The stochastic system model incorporates additive and multiplicative Gaussian and zonotopic set‐membership uncertainties, posing challenges in accurately managing these sources of uncertainty. In practical scenarios with limited communication resources, it is crucial to reduce communication overhead, save resources, and prevent unnecessary information transmission. To achieve these objectives, this article proposes an event‐triggered mechanism and a corresponding state estimator. The optimal gain matrix of the state estimator is derived to minimize the estimation uncertainty represented by the size of zonotope and Gaussian confidence ellipsoid regions. Conditions are established to guarantee the mean square stability of the estimation error, ensuring reliable estimation performance. The configuration of the triggering condition parameters, which play a vital role in determining the event‐triggered updates, is presented. is presented. The effectiveness of the proposed event‐triggered mechanism and the corresponding estimator is demonstrated through simulation results and analyses, using specific metrics and evaluation criteria. [ABSTRACT FROM AUTHOR]

When modelling counts or rates using Poisson regression, it is common to find overdispersion in data. Overdispersed count data is prevalent in a variety of applied research areas such as ecology and finance when the variance of the response is higher than the Poisson distribution allows. While there are models that are capable of handling data of this nature, conducting inference when presented with overdispersed data poses some challenges. Classical parametric approaches to inference may fail to be reliable when computing bounds for confidence regions as the mean-variance assumption of the Poisson distribution may not be satisfied. Bootstrap approaches are a viable alternative and we explore the performance of the one-step residual and wild bootstrap as a means to perform inference for regression parameters. Furthermore, we adopt an analytic approach to bootstrapping that is able to accommodate overdispersion, while being preferable from an efficiency perspective. [ABSTRACT FROM AUTHOR]

Background: Oxford Nanopore provides high throughput sequencing platforms able to reconstruct complete bacterial genomes with 99.95% accuracy. However, even small levels of error can obscure the phylogenetic relationships between closely related isolates. Polishing tools have been developed to correct these errors, but it is uncertain if they obtain the accuracy needed for the high-resolution source tracking of foodborne illness outbreaks. Results: We tested 132 combinations of assembly and short- and long-read polishing tools to assess their accuracy for reconstructing the genome sequences of 15 highly similar Salmonella enterica serovar Newport isolates from a 2020 onion outbreak. While long-read polishing alone improved accuracy, near perfect accuracy (99.9999% accuracy or ~ 5 nucleotide errors across the 4.8 Mbp genome, excluding low confidence regions) was only obtained by pipelines that combined both long- and short-read polishing tools. Notably, medaka was a more accurate and efficient long-read polisher than Racon. Among short-read polishers, NextPolish showed the highest accuracy, but Pilon, Polypolish, and POLCA performed similarly. Among the 5 best performing pipelines, polishing with medaka followed by NextPolish was the most common combination. Importantly, the order of polishing tools mattered i.e., using less accurate tools after more accurate ones introduced errors. Indels in homopolymers and repetitive regions, where the short reads could not be uniquely mapped, remained the most challenging errors to correct. Conclusions: Short reads are still needed to correct errors in nanopore sequenced assemblies to obtain the accuracy required for source tracking investigations. Our granular assessment of the performance of the polishing pipelines allowed us to suggest best practices for tool users and areas for improvement for tool developers. [ABSTRACT FROM AUTHOR]

In the generalized linear model (GLM) setup, the information matrix depends on the unknown parameters of the model. In such a situation, an experimenter has to adopt the approach of finding local optimal designs i.e. first guess the best value for the parameters and then calculate the optimal designs [see Chernoff (1953)]. In this paper, locally R-optimal designs for the Poisson regression model with two parameters including intercept are discussed. The R-optimality criterion has been proposed in the literature as an alternative to the most frequently used D-optimality criterion when the experimenter wishes to minimize the volume of the confidence region for unknown parameters based on Bonferroni t-intervals [see Dette (1997)]. The necessary and sufficient conditions of this optimality criterion are confirmed through the equivalence theorem [ABSTRACT FROM AUTHOR]

Stainless steel is the primary material of the lowerside superheater (LSSH) convection pipe support for petroleum or natural gas. Stainless steel corrosion is a severe challenge in equipment maintenance and operation. Corrosion may cause structural defects, leaks, and even failures in superheater convection pipes that could disrupt production and lead to reduced product quality. The Sigma phase occurrence is an indicator of the existence of stainless-steel corrosion. This corrosion occurs due to the exposure of steel to extremely high temperatures over a long period. The experiment was conducted on ASTM A297 after 24 years of operation. The testing sample was acquired from an Indonesian petrochemical company as part of the LSSH convection pipe support. Specimens of the material were heated at a high temperature over 500 °C. The corrosion process was observed based on the corrosion location and color. To analyze and understand this corrosion problem, correspondence analysis was performed. One statistical technique for examining the relationship between two sets of categorical variables is correspondence analysis. In the context of stainless-steel corrosion, the first variable is corrosion location categorized into three locations: A, B, and C, while the second variable is corrosion color divided into light brown (LBr), dark brown(DBr), bluish blue(BBr), light blue(LBl) and dark blue(DBl). Furthermore, elliptic confidence regions were used to estimate the extent to which points in a two-dimensional space represent different corrosion levels in line with the observed variables. The location and corrosion color of stainless steel are found to be statistically significantly correlated in the results; location A tends to be blue, location B tends to be brown, and location C is uncertain because it depends on whether it is light blue or dark brown. It suggests distribution patterns and identifies areas that are more susceptible to corrosion. These findings assist engineers in making decisions regarding the preservation and maintenance of stainless steel to reduce the corrosion rate. [ABSTRACT FROM AUTHOR]

Parameter and initial conditions (factors) estimation is a challenging task in non-linear models. Even if researchers successfully estimate those model factors, they still should estimate their confidence intervals, which could require a high computational cost to turn them into informative values. Some methods in the literature attempt to estimate regions within the search space where factors may jointly exist and fit the experimental data, i.e., confidence contours. However, the estimation of such confidence contours comes up with several issues as the number of factors increases. In this work we propose a method to compute a region within the confidence contour of an inverse problem, we denote this region as the confidence sub-contour box (CSB). To estimate a CSB of an inverse problem, we propose two main algorithms alongside their interpretation and validation, testing their performance with two epidemiological models for different diseases and different kinds of data. Moreover, we exposed some desirable properties of the method through numerical experiments, such as user-defined uncertainty level, asymmetry of the resultant intervals regarding the nominal value, sensitivity assessment related to the interval length for each factor, and identification of true-influential factors. We set available all the algorithms we used in this paper at Mathworks in the GSUA-CSB toolbox. [ABSTRACT FROM AUTHOR]

Quantify the uncertainty to decide and explore better In statistical inference, large-sample behavior and confidence interval construction are fundamental in assessing the error and reliability of estimated quantities with respect to the data noises. In the paper "Uncertainty Quantification and Exploration for Reinforcement Learning", Dong, Lam, and Zhu study the large sample behavior in the classic setting of reinforcement learning. They derive appropriate large-sample asymptotic distributions for the state-action value function (Q-value) and optimal value function estimations when data are collected from the underlying Markov chain. This allows one to evaluate the assertiveness of performances among different decisions. The tight uncertainty quantification also facilitates the development of a pure exploration policy by maximizing the worst-case relative discrepancy among the estimated Q-values (ratio of the mean squared difference to the variance). This exploration policy aims to collect informative training data to maximize the probability of learning the optimal reward collecting policy, and it achieves good empirical performance. We investigate statistical uncertainty quantification for reinforcement learning (RL) and its implications in exploration policy. Despite ever-growing literature on RL applications, fundamental questions about inference and error quantification, such as large-sample behaviors, appear to remain quite open. In this paper, we fill in the literature gap by studying the central limit theorem behaviors of estimated Q-values and value functions under various RL settings. In particular, we explicitly identify closed-form expressions of the asymptotic variances, which allow us to efficiently construct asymptotically valid confidence regions for key RL quantities. Furthermore, we utilize these asymptotic expressions to design an effective exploration strategy, which we call Q-value-based Optimal Computing Budget Allocation (Q-OCBA). The policy relies on maximizing the relative discrepancies among the Q-value estimates. Numerical experiments show superior performances of our exploration strategy than other benchmark policies. Funding: This work was supported by the National Science Foundation (1720433). [ABSTRACT FROM AUTHOR]

We develop and analyse the HulC, an intuitive and general method for constructing confidence sets using the convex hull of estimates constructed from subsets of the data. Unlike classical methods which are based on estimating the (limiting) distribution of an estimator, the HulC is often simpler to use and effectively bypasses this step. In comparison to the bootstrap, the HulC requires fewer regularity conditions and succeeds in many examples where the bootstrap provably fails. Unlike sub-sampling, the HulC does not require knowledge of the rate of convergence of the estimators on which it is based. The validity of the HulC requires knowledge of the (asymptotic) median bias of the estimators. We further analyse a variant of our basic method, called the Adaptive HulC, which is fully data-driven and estimates the median bias using sub-sampling. We discuss these methods in the context of several challenging inferential problems which arise in parametric, semi-parametric, and non-parametric inference. Although our focus is on validity under weak regularity conditions, we also provide some general results on the width of the HulC confidence sets, showing that in many cases the HulC confidence sets have near-optimal width. [ABSTRACT FROM AUTHOR]

This paper explored the effective empirical likelihood inferences for partially nonlinear models. By combining the modal regression method with orthogonal projection technology, a modal empirical likelihood-based estimation procedure was proposed. The proposed empirical likelihood approach retained Wilk’s theorem under mild conditions, and the confidence regions of model coefficients were constructed. Nonparametric and parametric components of the estimators were independent. Simulation results demonstrated that it is more robust and effective than the existing methods. [ABSTRACT FROM AUTHOR]

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The article discusses the features and prevalence of the SARS-CoV-2 KP.3 variant. The KP.3 variant is a descendant of the JN.1 lineage and is categorized as a variant under monitoring (VUM). It has been observed that the KP.3 variant is spreading globally, but with relatively low and variable concentrations. The article highlights the specific mutations present in the KP.3 variant, such as F456L, V1104L, and Q493E, and discusses their potential impact on the virus's ability to evade the immune system. However, the article emphasizes that there is currently no evidence to suggest that the KP.3 variant is a particularly concerning new variant. The authors stress the importance of continuous monitoring of SARS-CoV-2 variants to prevent unexpected outbreaks. [Extracted from the article]

A single-index varying coefficients regression model is considered, from which robust confidence regions/intervals of the regression parameters are derived. Considering the rank-based estimating equations, empirical likelihood objective functions of the index and functional coefficients objective functions are defined and their asymptotic properties are established under mild regularity conditions. The performance of the proposed approach is demonstrated via extensive Monte Carlo simulation experiments. The simulation results are compared with those obtained from a normal approximation alternative. Also the proposed method is compared with the least squares and least absolute deviations alternatives. Finally, a real data example is given to illustrate the method. [ABSTRACT FROM AUTHOR]

Background: Computed tomography (CT) is routinely used to guide cryoablation procedures. Notably, CT‐guidance provides 3D localization of cryoprobes and can be used to delineate frozen tissue during ablation. However, metal‐induced artifacts from ablation probes can make accurate probe placement challenging and degrade the ice ball conspicuity, which in combination could lead to undertreatment of potentially curable lesions. Purpose: In this work, we propose an image‐based neural network (CNN) model for metal artifact reduction for CT‐guided interventional procedures. Methods: An image domain metal artifact simulation framework was developed and validated for deep‐learning‐based metal artifact reduction for interventional oncology (MARIO). CT scans were acquired for 19 different cryoablation probe configurations. The probe configurations varied in the number of probes and the relative orientations. A combination of intensity thresholding and masking based on maximum intensity projections (MIPs) was used to segment both the probes only and probes + artifact in each phantom image. Each of the probe and probe + artifact images were then inserted into 19 unique patient exams, in the image domain, to simulate metal artifact appearance for CT‐guided interventional oncology procedures. The resulting 361 pairs of simulated image volumes were partitioned into disjoint training and test datasets of 304 and 57 volumes, respectively. From the training partition, 116 600 image patches with a shape of 128 × 128 × 5 pixels were randomly extracted to be used for training data. The input images consisted of a superposition of the patient and probe + artifact images. The target images consisted of a superposition of the patient and probe only images. This dataset was used to optimize a U‐Net type model. The trained model was then applied to 50 independent, previously unseen CT images obtained during renal cryoablations. Three board‐certified radiologists with experience in CT‐guided ablations performed a blinded review of the MARIO images. A total of 100 images (50 original, 50 MARIO processed) were assessed across different aspects of image quality on a 4‐point likert‐type item. Statistical analyses were performed using Wilcoxon signed‐rank test for paired samples. Results: Reader scores were significantly higher for MARIO processed images compared to the original images across all metrics (all p < 0.001). The average scores of the overall image quality, iceball conspicuity, overall metal artifact, needle tip visualization, target region confidence, and worst metal artifact, needle tip visualization, iceball conspicuity, and target region confidence improved by 34.91%, 36.29%, 39.94%, 34.17%, 35.13%, and 45.70%, respectively. Conclusions: The proposed method of image‐based metal artifact simulation can be used to train a MARIO algorithm to effectively reduce probe‐related metal artifacts in CT‐guided cryoablation procedures. [ABSTRACT FROM AUTHOR]

Measurement error is common in many statistical problems and has received considerable attention in various regression contexts. In this study, we consider the random coefficient autoregressive model with measurement error possibly present in covariates. The least squares and weighted least squares methods are used to estimate the model parameters, and the consistency and asymptotic normality of the two kinds of estimators are proved. Furthermore, we propose an empirical likelihood method based on weighted score equations to construct confidence regions for the parameters. The simulation results show that the weighted least squares estimators are superior to the least squares estimators and that the confidence regions have good finite-sample behavior. At last, the model is applied to a real data example. [ABSTRACT FROM AUTHOR]

In this paper, the empirical likelihood and estimations problems in varying coefficient models with right censored data are investigated by using a bias correction method. Naive and residual-adjusted empirical likelihood ratios and estimators of the coefficient functions are constructed, their asymptotic distributions are obtained, and a consistent estimator of the asymptotic variance is given. The obtained results can be directly used to construct the confidence regions of the coefficient functions. Furthermore, a new method for constructing pointwise confidence intervals and simultaneous confidence bands for each coefficient function is also proposed. The proposed method is to directly calibrate the empirical log-likelihood ratio so that the obtained ratio is asymptotically chi-squared. Undersmoothing of each coefficient function is avoided, and existing data-driven methods can also effectively select the optimal bandwidth. Simulation study is performed to compare the empirical likelihood with the normal approximation-based method. An example of real data is used to illustrate our approach. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new algorithm for solving convex-concave saddle-point problems using regret minimization in the repeated game framework. To do so, we introduce the Conic Blackwell Algorithm+ (CBA+), a new parameter- and scale-free regret minimizer for general convex compact sets. CBA+ is based on Blackwell approachability and attains O(T) regret. We show how to efficiently instantiate CBA+ for many decision sets of interest, including the simplex, ℓp norm balls, and ellipsoidal confidence regions in the simplex. Based on CBA+ , we introduce SP-CBA+ , a new parameter-free algorithm for solving convex-concave saddle-point problems achieving a O(1/T) ergodic convergence rate. In our simulations, we demonstrate the wide applicability of SP-CBA+ on several standard saddle-point problems from the optimization and operations research literature, including matrix games, extensive-form games, distributionally robust logistic regression, and Markov decision processes. In each setting, SP-CBA+ achieves state-of-the-art numerical performance and outperforms classical methods, without the need for any choice of step sizes or other algorithmic parameters. Funding: J. Grand-Clément is supported by the Agence Nationale de la Recherche [Grant 11-LABX-0047] and by Hi! Paris. C. Kroer is supported by the Office of Naval Research [Grant N00014-22-1-2530] and by the National Science Foundation [Grant IIS-2147361]. [ABSTRACT FROM AUTHOR]

The mixture designs are represented in terms of variables (xi, i = 1 , · , q ) called by components. The components display an experimental region described in a simplex space, where each variable has values between 0 and 1, portraying the specification of proportions. With these characteristics, the practice of the designs both in several industrial and in pharmaceutical sectors becomes justifiable. The issue is that defining a structure with N experimental points becomes complex since we can generate infinite configurations. Therefore, we used D-optimal mixture designs, which, in summary, guarantee more accurate confidence regions for parameter estimates as we develop these designs conditioned to a statistical criterion. For this reason, we have undertaken this approach to fulfill the aim of this study. We propose a modification in the simulated annealing algorithm combined with the Monte Carlo procedure in the generation of D-optimal mixture designs classified both by the presence and by the absence of bias in the proportion specifications. We conclude that the proposed modification in the simulated annealing algorithm provides the search with generation of D-optimal designs, supporting the control of the distribution of experimental points either near or far from the edges of the observed region limited by the simplex space. The optimal strategies generated in a biased way are less sensitive in relation to the number of components. [ABSTRACT FROM AUTHOR]

The R-optimality criterion is proposed in the literature as an alternative to the most frequently used D-optimality criterion. This criterion can be used in the experimental design when the main objective is to construct a rectangular confidence region. The present work investigates the R-optimal designs of the linear log contrast model supported on the general q-mixture components. The necessary and sufficient conditions of R-optimality have been examined by the equivalence theorem. [ABSTRACT FROM AUTHOR]

This work introduces an alternative approach for developing a customized Metaheuristic (MH) tailored for tuning a Fractional-Order Proportional-Integral-Derivative (FOPID) controller within an Automatic Voltage Regulator (AVR) system. Leveraging an Automated Algorithm Design (AAD) methodology, our strategy generates MHs by utilizing a population-based Search Operator (SO) domain, thus minimizing human-induced bias. This approach eliminates the need for manual coding or the daunting task of selecting an optimal algorithm from a vast collection of the current literature. The devised MH consists of two distinct SOs: a dynamic swarm perturbator succeeded by a Metropolis-type selector and a genetic crossover perturbator, followed by another Metropolis-type selector. This MH fine-tunes the FOPID controller's parameters, aiming to enhance control performance by reducing overshoot, rise time, and settling time. Our research includes a comparative analysis with similar studies, revealing that our tailored MH significantly improves the FOPID controller's speed by 1.69 times while virtually eliminating overshoot. Plus, we assess the tuned FOPID controller's resilience against internal disturbances within AVR subsystems. The study also explores two facets of control performance: the impact of fractional orders on conventional PID controller efficiency and the delineating of a confidence region for stable and satisfactory AVR operation. This work's main contributions are introducing an innovative method for deriving efficient MHs in electrical engineering and control systems and demonstrating the substantial benefits of precise controller tuning, as evidenced by the superior performance of our customized MH compared to existing solutions. [ABSTRACT FROM AUTHOR]

In this paper, we apply conformal prediction to time series data. Conformal prediction is a method that produces predictive regions given a confidence level. The regions outputs are always valid under the exchangeability assumption. However, this assumption does not hold for the time series data because there is a link among past, current, and future observations. Consequently, the challenge of applying conformal predictors to the problem of time series data lies in the fact that observations of a time series are dependent and therefore do not meet the exchangeability assumption. This paper aims to present a way of constructing reliable prediction intervals by using conformal predictors in the context of time series. We use the nearest neighbors method based on the fast parameters tuning technique in the weighted nearest neighbors (FPTO–WNN) approach as the underlying algorithm. Data analysis demonstrates the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Polydactyly is a genetic abnormality that affects both pig welfare and industry profits. Despite efforts to explore the genetic basis of pig polydactyly, progress remains limited. In this study, we analyzed a group of Large White pigs with postaxial polydactyly, including 29 cases and 79 controls from 24 families. High‐depth sequencing was performed on 20 pigs, while low‐depth sequencing was improved through imputation for the remaining pigs. A genome‐wide association study (GWAS) and genetic differentiation were conducted using the resequencing dataset, resulting in the identification of 48 significantly associated SNPs and 27 candidate regions. The genetic differentiation regions on chromosomes 5 and 18, which harbored GWAS‐identified SNPs, were delineated as confidence regions. The confidence region at Chr18: 1.850–1.925 Mb covers the fifth intron of LMBR1, a gene that contains an important regulatory element for SHH, known as ZRS. Mutations in this ZRS have been found to cause polydactyly in animals and humans. Therefore, we propose LMBR1 as a prospective candidate gene for postaxial polydactyly. These findings emphasize the importance of exploring the role of ZRS within LMBR1 in the pathogenesis of polydactyly in pigs. [ABSTRACT FROM AUTHOR]

Centor and McIsaac scores are clinical prediction rules for diagnosing group A streptococcus (GAS) infection in patients with pharyngitis. Their recommended thresholds vary between guidelines. To estimate the sensitivity and specificity of the McIsaac and Centor scores to diagnose GAS pharyngitis and evaluate their impact on antibiotic prescribing at each threshold in patients presenting to secondary care. MEDLINE, Embase, and Web of Science were searched from inception to September 2022. Studies of patients presenting with acute pharyngitis to emergency or outpatient clinics that estimated the accuracy of McIsaac or Centor scores against throat cultures and/or rapid antigen detection tests (RADT) as reference standards. Centor or McIsaac score. Throat cultures and/or RADT. Quality Assessment of Diagnostic Accuracy Studies. The sensitivities and specificities of the McIsaac and Centor scores were pooled at each threshold using bivariate random effects meta-analysis. Fourteen studies were included (eight McIsaac and six Centor scores). Eight studies had unclear and six had a high risk of bias. The McIsaac score had higher estimated sensitivity and lower specificity relative to Centor scores at equivalent thresholds but with wide and overlapping confidence regions. Using either score as a triage to RADT to decide antibiotic treatment would reduce antibiotic prescription to patients with non-GAS pharyngitis relative to RADT test for everyone, but also reduce antibiotic prescription to patients with GAS. Centor and McIsaac scores are equally ineffective at triaging patients who need antibiotics presenting with pharyngitis at hospitals. At high thresholds, too many true positive cases are missed, whereas at low thresholds, too many false positives are treated, leading to the over prescription of antibiotics. The former may be compensated by adequate safety netting by clinicians, ensuring that patients can seek help if symptoms worsen. [ABSTRACT FROM AUTHOR]

In this study, we investigate statistical features of polar cap north (PCN) and south (PCS) indices in response to various interplanetary conditions (interplanetary magnetic field [IMF] orientation in three-dimensions) and terrestrial conditions (seasonal and magnetic local time [MLT] locations of the index stations). The concurrent PCN-PCS pairs for 1998-2002 and 2004-2018 are divided based on their sign type (positive-positive, negative-negative, negative-positive, and positive-negative PCN-PCS pairs) and time coverage (the times when both index stations are in the dawn/dusk MLT sector during northern summer/winter). Analyzing the IMF orientation dependence on the occurrence probabilities of concurrent indices and on the differences between the indices in various sign types for each time coverage reveals that the statistical features in PCN-PCS pairs obtained in the dawn MLT sector can be largely explained by the effects of the three-component IMF (related to the polar cap convection patterns) combined with season (related to the hemispheric asymmetry in solar illumination-induced ionospheric conductance). However, those obtained in the dusk MLT sector are controlled dominantly by seasonal effects rather than IMF orientation effects. Our findings indicate that PCN-PCS pair data provide local views about the solar wind-magnetosphere-ionosphere (SW-M-I) coupling system with different control efficiencies of IMF orientation and season depending on the MLT location of the stations. Therefore, introducing polar cap indices recorded simultaneously at various locations in both hemispheres and analyzing them are strongly required to infer global views of the coupled SW-M-I system in the open field regions with higher confidence. [ABSTRACT FROM AUTHOR]

A Wald type test with the wrong dispersion matrix is used when the dispersion matrix is not a consistent estimator of the asymptotic covariance matrix of the test statistic. One class of such tests occurs when there are p groups and it is assumed that the population covariance matrices from the p groups are equal, but the common covariance matrix assumption does not hold. The pooled t test, one-way ANOVA F test, and one-way MANOVA F test are examples of this class. Another class of such tests is used for weighted least squares. Two bootstrap confidence regions are modified to obtain large sample Wald type tests with the wrong dispersion matrix. [ABSTRACT FROM AUTHOR]

In this paper, we empirically examine the interdependency of uncertainties among ASEAN +3 countries as well as between these countries and the G6 countries. We collect the uncertainty data from the new World Uncertainty Index (WUI) database. The WUI captures uncertainty related to economic and political events in a country. We show that ASEAN + 3 countries as a group are weakly interdependent, in terms of the long-run relationships of uncertainties within the group. We demonstrate that, in the absence of outside influence, i.e., uncertainty shocks emanating from the G6, the + 3 and ASEAN countries are two independent group of countries with the former having its own dynamics and the latter neither affecting nor being affected by the former. We further demonstrate that outside influence strongly affects China but not the other + 3 and ASEAN countries. These findings survive robustness checks and suggest that the ASEAN + 3, as a region, may provide long-term diversification benefits to global investors and that the strengthening of the ASEAN + 3 regional cooperation framework may further buttress confidence in this region. [ABSTRACT FROM AUTHOR]

The time series data in many applications, for example, wind power and vehicle trajectory, show significant uncertainty. Using a single prediction value of wind power as feedback information for wind turbine control or unit commitment is not enough since the uncertainty of the prediction is not described. This paper addresses the uncertainty issue in time series data forecasting by proposing the novel interval reservoir computing method. The proposed interval reservoir computing can capture the underlying evolution of the stochastic dynamical system for time series data using the recurrent neural network (RNN). On the other hand, by formulating a chance-constrained optimization problem, interval reservoir computing outputs a set of parameters in the RNN, which maps to an interval of prediction values. The capacity of the interval is the smallest one satisfying the condition that the probability of having a prediction inside the interval is lower than the required level. The scenario approach solves the formulated chance-constrained optimization problem. We implemented an experimental data-based validation to evaluate the proposed method. The validation results show that the proposed interval reservoir computing can give a tight interval of time series data forecasting values for wind power and traffic trajectory. In addition, the confidence probability over the feasibility goes to 1 very quickly as the sample number increases. [ABSTRACT FROM AUTHOR]

The mixture of a distribution of responses from untreated patients and a shift of that distribution is a useful model for the responses from a group of treated patients. The mixture model accounts for the fact that not all the patients in the treated group will respond to the treatment and consequently their responses follow the same distribution as the responses from untreated patients. The treatment effect in this context consists of both the fraction of the treated patients that are responders and the magnitude of the shift in the distribution for the responders. In this article, we introduce inference based on a pseudo-likelihood approach and compare it with an existing method of moment approach. An extensive simulation study is used to compare robust performance of the two approaches regarding point estimation, confidence regions, and confidence intervals. The methods are demonstrated on an illustrative blood pressure data set. [ABSTRACT FROM AUTHOR]

We investigate the linear trends in meridional atmospheric heat transport (AHT) since 1980 in atmospheric reanalysis datasets, coupled climate models, and atmosphere-only climate models forced with historical sea surface temperatures. Trends in AHT are decomposed into contributions from three components of circulation: (i) transient eddies, (ii) stationary eddies, and (iii) the mean meridional circulation. All reanalyses and models agree on the pattern of AHT trends in the Southern Ocean, providing confidence in the trends in this region. There are robust increases in transient-eddy AHT magnitude in the Southern Ocean in the reanalyses, which are well replicated by the atmosphere-only models, while coupled models show smaller magnitude trends. This suggests that the pattern of sea surface temperature trends contributes to the transient-eddy AHT trends in this region. In the tropics, we find large differences between mean-meridional circulation AHT trends in models and the reanalyses, which we connect to discrepancies in tropical precipitation trends. In the Northern Hemisphere, we find less evidence of large-scale trends and more uncertainty, but note several regions with mismatches between models and the reanalyses that have dynamical explanations. Throughout this work we find strong compensation between the different components of AHT, most notably in the Southern Ocean where transient-eddy AHT trends are well compensated by trends in the mean-meridional circulation AHT, resulting in relatively small total AHT trends. This highlights the importance of considering AHT changes holistically, rather than each AHT component individually. [ABSTRACT FROM AUTHOR]