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        <title>Frontiers in Applied Mathematics and Statistics | New and Recent Articles</title>
        <link>https://www.frontiersin.org/journals/applied-mathematics-and-statistics</link>
        <description>RSS Feed for Frontiers in Applied Mathematics and Statistics | New and Recent Articles</description>
        <language>en-us</language>
        <generator>Frontiers Feed Generator,version:1</generator>
        <pubDate>2026-05-14T16:37:53.611+00:00</pubDate>
        <ttl>60</ttl>
        <item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fams.2026.1765200</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fams.2026.1765200</link>
        <title><![CDATA[Development of novel quality control charts for individual observations using Haar and Dmey wavelet transforms]]></title>
        <pubdate>2026-05-14T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Azzah Mustafa Abdulqader</author><author>Jihan Fakhre Salih</author><author>Taha Hussein Ali</author>
        <description><![CDATA[IntroductionTraditional individual control charts primarily monitor process location and provide limited information regarding dispersion. This study proposes discrete wavelet-based quality control charts using Haar and discrete Meyer (Dmey) wavelets to separately monitor mean and variance through approximation and detail coefficients.MethodsDiscrete wavelet transform (DWT) is applied to Phase I data to obtain approximation and detail coefficients. Control charts are constructed for normalized coefficients and evaluated against the classical individuals chart. Performance is assessed via Monte Carlo simulation and a real healthcare application involving neonatal serum iron levels.ResultsThe proposed charts produce narrower control limits and improved sensitivity compared with the traditional individuals chart. In simulation, control-limit width decreases from D = 6.2974 (individuals chart) to D = 3.9867 (Dmey detail chart), a reduction of approximately 36.7%. In real data, the individuals chart yields D = 92.0192, while the Haar detail chart reduces it to D = 59.4071 (about 35.4% narrower). Detail-based charts demonstrate superior variance monitoring performance.DiscussionIntegrating wavelet decomposition within statistical process control enables separate monitoring of location and scale changes, enhancing detection sensitivity for individual-observation processes.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fams.2026.1826475</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fams.2026.1826475</link>
        <title><![CDATA[Generation of new batik floral patterns via chaotic modulation and Julia-Mandelbrot fractal transformations]]></title>
        <pubdate>2026-05-11T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Michael Kopp</author><author>Inna Samuilik</author>
        <description><![CDATA[This paper presents a novel hierarchical approach for generating complex floral patterns through sequential application of chaotic modulation and fractal transformations. Classical geometric floral patterns are first subjected to binary chaotic modulation using signals extracted from a four-dimensional memristor-based hyperchaotic system, introducing controlled irregularity into regular structures. Subsequently, Julia and Mandelbrot set-based fractal transformations embed hierarchical self-similarity within these chaotically modulated configurations. The choice of the hyperchaotic system is motivated by its minimal complexity enabling practical analog and digital implementation, hyperchaotic dynamics providing rich modulation signals, and capability to generate multi-scroll attractors. Analysis demonstrates that global geometric order coexists with local fractal complexity in the resulting patterns, with topological features and symmetries partially preserved throughout the transformation cascade. This synergistic synthesis of chaotic dynamics and fractal geometry opens new possibilities for generating patterns with tunable visual and topological characteristics applicable to digital art and textile design.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fams.2026.1843851</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fams.2026.1843851</link>
        <title><![CDATA[Editorial: Integrative mathematical models for disease, volume II]]></title>
        <pubdate>2026-04-28T00:00:00Z</pubdate>
        <category>Editorial</category>
        <author>Joseph Malinzi</author><author>Preeti Dubey</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fams.2026.1776255</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fams.2026.1776255</link>
        <title><![CDATA[Novel hierarchical support vector models with application to diabetes control]]></title>
        <pubdate>2026-04-22T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Nadia Al Habsi</author><author>Iman Al Hasani</author><author>Ronald Wesonga</author>
        <description><![CDATA[This study examines classification and prediction in non-separable, heterogeneous data settings, with emphasis on improving support vector–based methods. We consider support vector machines (SVM) and support vector regression (SVR) and demonstrate that classical kernel approaches can lose efficiency when population heterogeneity and imbalance are ignored. To address this limitation, we propose hierarchical stratified support vector models that incorporate subgroup structure through stratum-adaptive learning. Performance is evaluated using confusion matrix based indices together with novel weighted criteria for correct classification rate and mean squared error, ensuring alignment with stratified population targets. An application to diabetes control classification using glycated hemoglobin (HbA1c) data shows that the proposed hierarchical models consistently outperform classical SVM and SVR in accuracy and predictive error, while enabling scalable computation and meaningful stratum-specific inference.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fams.2026.1795340</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fams.2026.1795340</link>
        <title><![CDATA[Qualitative dynamics and optimal control of a delayed algae–fish bioeconomic system with nitrate recycling]]></title>
        <pubdate>2026-04-20T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Saida Khiyar</author><author>Mohamed Hafdane</author><author>Hamza Boutayeb</author><author>Imane Elberrai</author>
        <description><![CDATA[A bioeconomic delay model is developed for an algae–fish ecosystem in which the time delay τ represents the lag required for nutrients released by fish decomposition to become bioavailable. The model incorporates logistic growth for both populations together with a Holling type-II functional response to describe trophic interactions. The well-posedness of the system is established by proving the existence, uniqueness, positivity, and boundedness of solutions. The local stability of biologically relevant equilibria is then analyzed, including the boundary equilibrium associated with algal extinction and the interior equilibrium corresponding to species coexistence. The influence of the delay parameter on system stability is investigated, and explicit conditions for the occurrence of Hopf bifurcations are derived. Numerical simulations illustrate the destabilizing impact of increasing delay and support the analytical results. Finally, an optimal control framework is introduced to address long-term resource management, and Pontryagin's maximum principle is applied to characterize harvesting strategies that balance economic performance with ecological sustainability.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fams.2026.1790416</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fams.2026.1790416</link>
        <title><![CDATA[Modeling cross-market capital flows using fractional competition dynamics]]></title>
        <pubdate>2026-04-16T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Sherif Abdul Ganiyu</author><author>Mercy Ngungu</author><author>Rebecca Bottey</author><author>Susana Danso Mensah</author>
        <description><![CDATA[IntroductionInteractions between the stock market and the rapidly expanding cryptocurrency market have become an important feature of modern financial systems. Compared with traditional equity markets, cryptocurrency markets are generally more volatile and more sensitive to investor sentiment, speculative behavior, and technological change. Classical integer-order models may not adequately capture the nonlinear interactions, delayed responses, and memory effects that characterize such cross-market capital dynamics.MethodsThis study develops a fractional-order Lotka-Volterra competition model to describe the dynamic competition between stock and cryptocurrency markets. The model employs Caputo fractional derivatives to incorporate memory effects and persistent shocks, and includes migration terms to represent capital reallocation between the two markets. The resulting system is investigated numerically using the fractional Adams-Bashforth-Moulton predictor-corrector scheme.ResultsNumerical simulations show that the fractional-order parameter has a substantial effect on market behavior. Lower fractional orders produce stronger memory effects, slower convergence to equilibrium, and smoother long-term transitions, whereas higher orders recover behavior closer to the classical integer-order case. The simulations further indicate that changes in competition coefficients and capital migration rates strongly affect long-term market dominance, coexistence, and equilibrium levels.DiscussionThe proposed fractional competition framework provides a more flexible and realistic description of stock-cryptocurrency interactions than classical models. By accounting for memory-driven dynamics, it offers useful insight into long-term capital allocation, market coexistence, and the possible influence of regulatory conditions and investor sentiment. These findings suggest that fractional-order modeling can serve as a valuable tool for studying evolving financial markets and cross-market capital flows.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fams.2026.1807939</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fams.2026.1807939</link>
        <title><![CDATA[LaSIPDE: Latent-Space Identification of Partial Differential Equations from indirect, high-dimensional measurements]]></title>
        <pubdate>2026-04-14T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Imane Koulali</author><author>Erhan Turan</author><author>M. Taner Eskil</author>
        <description><![CDATA[Discovering governing equations from data is a central challenge in scientific machine learning, particularly when observations are high-dimensional and the underlying state variables are not directly accessible. In this work, we introduce a framework for data-driven discovery of partial differential equations (PDEs) from indirect high-dimensional observations. The proposed approach combines nonlinear representation learning through an autoencoder with sparse identification of governing equations in the latent space, enabling simultaneous model reduction and PDE discovery while preserving spatial structure. Unlike existing methods that either operate on observable variables or discover latent ordinary differential equations, our framework identifies PDEs directly in the learned latent coordinates. We validate the approach on high-dimensional observations generated from Burgers and Korteweg–de Vries (KdV) systems, where the true state variables are intentionally hidden. In both cases, the method successfully recovers the correct dynamical operators, including diffusion, nonlinear advection, and dispersive terms. Although the recovered coefficients differ due to latent coordinate transformations, we show both theoretically and empirically that the discovered equations are dynamically equivalent to the ground-truth systems up to an affine transformation. These results demonstrate that governing PDEs can be recovered from indirect, high-dimensional data without access to the physical state variables, providing a foundation for interpretable model discovery in realistic measurement settings.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fams.2026.1799489</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fams.2026.1799489</link>
        <title><![CDATA[A multi-host deterministic-stochastic framework for giardiasis transmission: branching-process extinction analysis and continuous-time Markov chain dynamics]]></title>
        <pubdate>2026-04-13T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Christopher Chukwuma Asogwa</author><author>Ifeanyi Sunday Onah</author>
        <description><![CDATA[Giardiasis remains a widespread waterborne disease with substantial public health importance, particularly in settings with inadequate sanitation and high environmental contamination. In this study, we formulate and analyze a deterministic compartmental model that captures the transmission dynamics of Giardia duodenalis among immunocompetent and immunocompromised human populations, a lamb reservoir, and the environmental cyst pool. We derive the disease-free equilibrium and establish its local and global stability in terms of the basic reproduction number R0, obtained via the next-generation matrix method. To complement the deterministic analysis, we construct a multi-type Galton-Watson branching process approximation near the DFE and compute type-specific extinction probabilities under different introduction scenarios. Sensitivity analysis is performed by varying key shedding parameters to quantify their influence on extinction likelihood. Furthermore, a continuous-time Markov chain (CTMC) model is developed to estimate the distribution of extinction times, providing additional insight into stochastic fadeout dynamics. Numerical experiments reveal that infections introduced through lambs or the environmental cyst reservoir exhibit markedly lower extinction probabilities and longer mean extinction times compared with human-initiated introductions. Overall, the combined deterministic-stochastic framework highlights the importance of reducing environmental contamination and targeting high-shedding hosts to enhance the probability of disease elimination. The results underscore the significance of early detection, timely treatment, and interventions that curtail environmental cyst persistence as effective strategies for mitigating giardiasis transmission.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fams.2026.1742828</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fams.2026.1742828</link>
        <title><![CDATA[Fuzzy hyperheuristic optimization of a facilitated hub-and-spoke drone-enabled logistics network: a case study of Australia Post]]></title>
        <pubdate>2026-04-10T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Kassem Danach</author><author>Samir Haddad</author><author>Wissam Khalil</author><author>Ziad El Balaa</author><author>Jinane Sayah</author>
        <description><![CDATA[IntroductionThe rapid growth of e-commerce has increased pressure on postal logistics networks, especially in remote regions.MethodsThis study proposes a fuzzy hyperheuristic genetic algorithm for optimizing a facilitated hub-and-spoke network with drone integration under uncertainty.ResultsThe proposed approach improves service robustness by 25–35% and expands drone coverage to 93.4% of remote demand, with only a modest cost increase (8–12%).DiscussionResults highlight the effectiveness of fuzzy optimization and adaptive hyperheuristics in designing resilient and cost-efficient postal logistics systems.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fams.2026.1784640</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fams.2026.1784640</link>
        <title><![CDATA[Teaching-oriented modeling and numerical demonstration of Newton's law of gravitation]]></title>
        <pubdate>2026-04-07T00:00:00Z</pubdate>
        <category>Brief Research Report</category>
        <author>Baolu Fan</author><author>Yang Liu</author>
        <description><![CDATA[Based on Newton's laws of motion and Kepler's empirical laws, this study presents a pedagogically oriented derivation of the inverse-square law of gravitation and develops a numerical model to simulate two-dimensional planetary motion under central gravitational interaction. The governing equations are solved using a finite-difference time-stepping scheme, enabling visualization of orbital trajectories and conservation properties. Through combined analytical derivation and numerical simulation, the study highlights the dynamical roles of energy and angular momentum conservation in shaping orbital behavior. In addition, an instructional analogy with LC oscillatory circuits is introduced to clarify the underlying dynamical structure responsible for orbital stability. By integrating theoretical reasoning, computational modeling, and cross-domain analogy, this work aims to enhance students' conceptual understanding and model-based reasoning in classical mechanics.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fams.2026.1798581</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fams.2026.1798581</link>
        <title><![CDATA[Formulating likelihood functions for infectious disease dynamics for neglected tropical diseases]]></title>
        <pubdate>2026-04-02T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Renata Retkute</author><author>T. Déirdre Hollingsworth</author><author>Amanda Minter</author>
        <description><![CDATA[Reliable inference in infectious disease modeling requires careful treatment of both model structure and the relationship between latent infection dynamics and observed data. Likelihood functions, which link model parameters to empirical observations, can be formulated either to explicitly represent underlying disease transmission and reporting processes (process-based) or to summarize statistical patterns in aggregated outcomes (observation-based). Stochastic models capture inherent variability in transmission and detection, whereas deterministic models describe average system behavior and often rely on statistical assumptions to account for residual uncertainty. Using two neglected tropical disease (NTD) models, we compare parameter estimation based on complete individual-level events with that based on aggregated counts. By generating synthetic outbreak data from stochastic simulations and analyzing it under alternative modeling frameworks, we show how different combinations of model formulation and likelihood structure influence both point estimates and uncertainty quantification. Our findings indicate that, even when detailed process information is unavailable, observation-based likelihoods can produce robust parameter estimates and credible uncertainty intervals, highlighting their usefulness for practical decision-making in contexts with limited or aggregated surveillance data.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fams.2026.1762084</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fams.2026.1762084</link>
        <title><![CDATA[Fitting reinforcement learning model to behavioral data under bandits]]></title>
        <pubdate>2026-03-26T00:00:00Z</pubdate>
        <category>Technology and Code</category>
        <author>Hao Zhu</author><author>Jasper Hoffmann</author><author>Baohe Zhang</author><author>Joschka Boedecker</author>
        <description><![CDATA[We consider the problem of fitting a reinforcement learning (RL) model to some given behavioral data under a multi-armed bandit environment. These models have received much attention in recent years for characterizing human and animal decision-making behavior. We provide a generic mathematical optimization problem formulation for the fitting problem of a wide range of RL models that appear frequently in scientific research applications. We then provide a detailed theoretical analysis of its convexity properties. Based on the theoretical results, we introduce a novel solution method for the fitting problem of RL models based on convex relaxation and optimization. Our method is then evaluated in several simulated and real-world bandit environments to compare with some benchmark methods that appear in the literature. Numerical results indicate that our method achieves comparable performance to the state-of-the-art, while significantly reducing computation time. We also provide an open-source Python package for our proposed method to empower researchers to apply it in the analysis of their datasets directly, without prior knowledge of convex optimization.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fams.2026.1809903</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fams.2026.1809903</link>
        <title><![CDATA[Low-rank tensor completion with fractal-inspired multi-scale energy regularization]]></title>
        <pubdate>2026-03-23T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Chuling Wen</author><author>Weijie Liang</author><author>Chen Xu</author><author>Yuru Zou</author>
        <description><![CDATA[Low-rank tensor completion has become a fundamental tool for recovering high-dimensional data from incomplete observations. However, conventional methods rely primarily on algebraic low-rank priors and do not explicitly regulate how signal energy is distributed across scales. This study introduces a fractal-inspired multi-scale energy regularization that enforces approximate power-law scaling of tensor energy across resolution levels. The proposed formulation integrates scale-consistency constraints with tensor nuclear norm regularization in a unified framework. An inexact ADMM algorithm is developed to solve the resulting non-convex problem. Experimental results demonstrate consistent improvements in reconstruction accuracy across tensor sizes and observation ratios.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fams.2026.1791613</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fams.2026.1791613</link>
        <title><![CDATA[Effective transport and minimal invasion speed in a vector-host model of cassava mosaic disease with partial cross-protection]]></title>
        <pubdate>2026-03-18T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Myunghyun Oh</author>
        <description><![CDATA[Cassava mosaic disease (CMD) can spread across agricultural landscapes through whitefly-mediated transmission, creating invasion fronts whose speed is governed by vector movement and survival. Motivated by mild-strain cross-protection as a management tool, we formulated a spatially explicit vector–host model that couples plant infection dynamics with vector advection–diffusion and includes a protected plant class representing partial cross-protection. We derived an explicit invasion threshold R0 and showed that cross-protection enters through an effective susceptible fraction. For linearly determined (pulled) fronts, we characterized the minimal invasion speed and identified effective drift and diffusion weights that aggregate transport across the coupled host–vector system; near threshold, the speed scales with R0 − 1 through the leading-edge growth rate. We computed monotone traveling waves numerically and found close agreement between observed wave speeds and the pulled-speed prediction in the parameter regime considered. The analysis highlights how increasing vector mortality, improving protection efficacy, and strengthening roguing can either prevent invasion (R0 < 1) or slow spatial spread. Here, we also discuss limitations relevant to field deployment, including human-mediated movement of infected cuttings, temporally varying wind conditions, and episodic planting and harvest.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fams.2026.1763637</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fams.2026.1763637</link>
        <title><![CDATA[Advancing bearing fault detection through a modified metaheuristic optimization approach]]></title>
        <pubdate>2026-03-17T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Lana A. Abullah</author><author>Chnoor M Rahman</author>
        <description><![CDATA[IntroductionDetecting bearing faults plays a vital role in industrial maintenance since discovering problems early can help avoid unexpected breakdowns and expensive production losses. Yet, spotting these faults in their initial stages is still difficult because vibration signals are often complex and change over time.MethodsIn this study, optimized Mel Frequency Cepstral Coefficients (MFCC) feature extraction approach enhanced through a modified FOX optimization algorithm. The enhancement focuses on fine-tuning MFCC hyperparameters to maximize the discriminative power of extracted features for fault detection tasks. The proposed Enhanced FOX (EFOX) algorithm integrates different random distribution method and improved exploration–exploitation balance, enabling more effective parameter optimization compared to conventional methods.ResultsExperimental evaluations were conducted using benchmark datasets, and the optimized MFCC features were compared against those obtained via standard MFCC settings and other metaheuristic optimization techniques. Results demonstrate that our approach consistently outperforms competing methods in terms of classification accuracy and the robustness of the proposed model was assessed by testing it on two distinct bearing’s datasets with different noise ratios including −3 dB and −6 dB.DiscussionThe analysis highlights the impact of each of hyperparameter’s of MFCC to bearing fault detection.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fams.2026.1777018</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fams.2026.1777018</link>
        <title><![CDATA[Cause-distinct incidence for resolving confusion in competing risk analysis: a critical review]]></title>
        <pubdate>2026-03-16T00:00:00Z</pubdate>
        <category>Mini Review</category>
        <author>Tsuyoshi Nakamura</author><author>Tomomi Yamada</author><author>Yoshiaki Nose</author>
        <description><![CDATA[An event that hinders or changes the possibility of observing the event of interest is called a competing risk. For instance, clinical studies involving patients with multimorbidity or critically severe illnesses often require the evaluation of competing risks, as the occurrence of other events may preclude the primary event of interest. Cause-specific incidence and the Fine–Gray hazard have been widely used and have become the default methodological approaches in competing risk analysis. Nevertheless, some clinicians are unable to correctly interpret the results obtained from the competing risk analysis. Recently, the cause-distinct incidence has been introduced to resolve drawbacks in competing risk analysis, but because this confusion is widespread among biostatisticians, it may take considerable time to resolve. During this period, clinical researchers may continue to publish articles with incorrect interpretations. This study aimed to address these misinterpretations and accelerate clarification of the prevailing drawbacks.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fams.2026.1687194</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fams.2026.1687194</link>
        <title><![CDATA[Monotone delta: an order-theoretic tournament graph approach for internal consistency assessment]]></title>
        <pubdate>2026-03-11T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Muhammad Umair Danish</author><author>Umair Rehman</author><author>Katarina Grolinger</author>
        <description><![CDATA[This paper introduces Monotone Delta (δ), an order-theoretic measure for assessing the internal consistency of survey-based instruments. Classical coefficients such as Cronbach's Alpha and McDonald's Omega can yield misleading estimates under practical violations, including redundancy, multidimensional constructs, and correlated errors. Monotone Delta avoids parametric and factor-model assumptions by quantifying internal consistency through contradiction minimization with a weighted tournament formulation, aligning responses to an optimal unidimensional latent order. In controlled synthetic studies across four scenarios (tau-equivalence, redundancy, multidimensionality, and non-normal/correlated errors), Monotone Delta stays closest to the theoretical reliability, with absolute error ≤ 0.02 in the challenging scenarios where Alpha and Omega deviate by as much as 0.22 and 0.14, respectively. On a 350-participant human study for AI-generated image assessment, Monotone Delta agrees with Alpha/Omega under near-ideal conditions (overall δ = 0.91 vs. α = 0.92, ω = 0.94) while remaining stable under redundancy and non-normal perturbations (overall δ = 0.84 and δ = 0.81, respectively, where Alpha drops to 0.95 and 0.35). These results position Monotone Delta as a practical alternative for reliability assessment in socio-technical systems, human factors, healthcare, and interactive system design.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fams.2026.1778863</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fams.2026.1778863</link>
        <title><![CDATA[Basically tau-equivalent confirmatory factor analysis considering item particularities]]></title>
        <pubdate>2026-03-09T00:00:00Z</pubdate>
        <category>Methods</category>
        <author>Karl Schweizer</author><author>Tengfei Wang</author>
        <description><![CDATA[We propose a basically tau-equivalent measurement model for confirmatory factor analysis that differs from the original tau-equivalent measurement model through the replacement of strict tau equivalence by basic tau equivalence, enabling the accounting of item particularities. Considered in the framework of linear regression analysis, basic tau equivalence means a slope of zero across the set of factor loadings, but without their restriction to exact correspondence. Allowing for deviations from correspondence enables the account of item particularities, whereas a zero slope keeps away systematic influences unrelated to the attribute of interest. This measurement model is illustrated using an example matrix and data from two simulation studies. The fit results of the two described methods for realizing basic tau equivalence are highly correlated.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fams.2026.1736648</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fams.2026.1736648</link>
        <title><![CDATA[Analysis of the duration measurement of Jordanian debt as an effective hedging risk tool]]></title>
        <pubdate>2026-03-02T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Amro Salem Alamaren</author><author>Ahmad Ali Eyadat</author><author>Ashraf Mohammad Alrjoub</author><author>Diana Alhajjeah</author>
        <description><![CDATA[This study evaluates the duration of Jordanian public debt as an instrument for hedging interest rate risk over the period 2008–2020. It provides an analytical examination of public debt concepts and emphasizes the relevance of debt duration as a measure of maturity structure and interest rate sensitivity. The study covers both domestic and external public debt and situates the analysis within the historical context of Jordan’s debt evolution, including the 1988 debt crisis and subsequent fiscal adjustments. Using a comprehensive dataset of Jordanian government treasury bonds denominated in Jordanian dinars, the study applies Macaulay Duration and Modified Duration models to assess the effective maturity and interest rate exposure of the public debt portfolio. The results indicate that the estimated Macaulay Duration of Jordanian public debt is approximately 2.514 years, reflecting the weighted average timing of debt cash flows and the horizon of interest rate sensitivity. The Modified Duration is estimated at approximately 2.293 years, indicating a moderate degree of price sensitivity to changes in interest rates. Overall, the findings suggest that Jordan’s public debt exhibits a relatively short- to medium-term maturity structure, allowing for refinancing flexibility while maintaining manageable exposure to interest rate risk. The study highlights the usefulness of duration-based indicators as complementary tools for public debt management and risk assessment in emerging economies.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fams.2026.1764133</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fams.2026.1764133</link>
        <title><![CDATA[Asymptotic properties for self-weighted M-estimation of MEM]]></title>
        <pubdate>2026-02-25T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Ting Li</author><author>Saiful Izzuan Hussain</author><author>Ke-Ang Fu</author>
        <description><![CDATA[IntroductionIn the context of non-negative high-frequency financial time series, the multiplicative error model (MEM) is a generalized model. Maximum Likelihood Estimation (MLE) serves as the standard approach for parameter estimation when applying MEM to real-world modeling. This method relies on the assumption that the error term follows a specific known distribution with finite variance. However, directly imposing the assumption of a known distribution with bounded variance entails notable limitations.MethodsIn this model, a self-weighted M-estimation approach is used to estimate the model parameters. This estimation is performed considering the infinite variance of the model errors.ResultsOn a theoretical level, this estimation proved to have strong consistency and asymptotic normality. The results of the numerical simulations show that self-weighted M-estimation is more robust than other estimation methods. Finally, the self-weighted M-estimation method is applied to the price range of polyethylene and polypropylene futures on the Dalian Commodity Exchange.DiscussionThe results demonstrate that the self-weighted M-estimation outperforms both maximum likelihood estimation and least absolute deviation estimation. This finding is particularly significant for financial applications, where extreme outliers and infinite-variance events are frequently observed.]]></description>
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    </rss>