Moments Of Variables Research Articles

A Quasi-Poisson Item Response Theory Model for Heterogeneous Dispersion in Count Data

Item-level count data frequently arise in cognitive, educational, and psychological assessments. Correctly handling different dispersion levels in count data is crucial for accurate statistical inference. This research proposes a Quasi-Poisson item response theory model that accommodates overdispersion, underdispersion, and equidispersion in count data, aiming to explicitly model the connection between the mean and variance parameters, providing a method that is both computationally efficient and statistically robust. This semiparametric model specifies the first two conditional moments for the count variables and derives marginal moments to estimate model parameters. Simulation studies demonstrate the Quasi-Poisson model’s efficacy in parameter recovery across different dispersion scenarios and its negligible computation time. Empirical data analysis further underscores the model’s superior fit and computational efficiency in a real-world setting.

Finite-Element-Based Time-Dependent Service Life Prediction for Carbonated Reinforced Concrete Aqueducts

This study proposes a time-dependent reliability analysis method for aqueduct structures based on concrete carbonation and finite element analysis. The primary goal of this study is to improve the reliability assessment of reinforced concrete aqueducts by incorporating environmental factors such as carbonation over time. First, a three-dimensional finite element model of a reinforced concrete aqueduct is established using the Midas 2022 Civil software, incorporating a time-varying function derived from a predictive model of concrete carbonation depth. Point estimation is then integrated with structural finite element analysis to calculate the first four moments of random variables as functions of concrete carbonation. Additionally, the original performance function is transformed into a normal distribution using dual power transformation and the Jarque–Bera test. The high-order unscented transformation (HUT) is subsequently employed to estimate the first four moments of the transformed performance function, facilitating the calculation of time-varying reliability indices for the carbonated concrete aqueduct. Based on the time-varying reliability index data, a reliability function corresponding to different time points is fitted and applied to service life prediction. The results demonstrate that the proposed method effectively reduces large errors associated with the fourth-moment method in calculating large reliability indices. Furthermore, the comparison with Monte Carlo simulation (MCS) results validates the high efficiency and accuracy of the proposed method, offering a valuable tool for addressing the reliability challenges of aqueducts exposed to carbonation and other environmental factors over time.

Статистики вищих порядків в задачах виявлення сигналів на фоні корельованих негаусових завад

Signal detection in noise is critical in telecommunications, navigation and radar systems, image processing and biomedical research, where noise often deviates from a normal distribution, and sample values exhibit statistical dependence. Traditional methods for analyzing and designing such systems face significant limitations, including algorithmic and computational complexity, severely restricting their practical application. An effective approach to developing signal detection systems involves using moment and cumulant descriptions of random variables, simplifying the design of detection systems for signals with various probability density functions. The authors propose a novel approach based on one-dimensional (1D) and two-dimensional (2D) moment-cumulant models to describe correlated non-Gaussian processes. This approach enables the modification of the moment-based criterion for statistical hypothesis testing and synthesising polynomial stochastic decision rules for detecting signals in correlated non-Gaussian noise. The study demonstrates that the nonlinear processing of sample values and the application of higher-order statistics to describe the investigated processes account for the structure of non-Gaussian noise and its statistical dependencies. This reduces the error probabilities of the synthesized decision rules compared to traditional Gaussian models. The study aims to enhance the efficiency of signal detection systems under additive interaction with correlated non-Gaussian noise by developing new moment-cumulant models of the investigated processes, modifying the moment-based criterion for hypothesis testing, and designing polynomial decision rules. The study's practical significance lies in creating simple-to-implement algorithms with high accuracy that achieve lower error probabilities in decision rules compared to existing methods.

Properties and integral inequalities of P-superquadratic functions via multiplicative calculus with applications

This manuscript explores the idea of a multiplicatively P-superquadratic function and its properties. Utilizing these properties, we derive the inequalities of Hermite–Hadamard type for such functions in the framework of multiplicative calculus. In addition to this, we derive integral inequalities of Hermite–Hadamard type for the product and quotient of multiplicatively P-superquadratic and multiplicatively P-subquadratic functions. Moreover, we develop the fractional version of Hermite–Hadamard type inequalities involving midpoints and end points for multiplicatively P-superquadratic functions with respect to multiplicatively Riemann–Liouville (R.L) fractional integrals. Graphical illustrations based on specific relevant examples validate the credibility of the findings. The study is further stimulating by being pushed with potential applications in terms of moment of random variables, special means, and modified Bessel functions of the first kind. Regarding superquadraticity, the results presented in this work are new, therefore they clearly provide extensions and improvements of the work available in the literature.

Smoothness and Lévy concentration function inequalities for distributions of random diagonal sums

We present new explicit upper bounds for the smoothness of the distribution of the random diagonal sum S n = ∑ j = 1 n X j , π ( j ) S_n=\sum _{j=1}^nX_{j,\pi (j)} of a random n × n n\times n matrix X = ( X j , r ) X=(X_{j,r}) , where X j , r X_{j,r} are independent integer valued random variables, and π \pi denotes a uniformly distributed random permutation on { 1 , … , n } \{1,\dots ,n\} independent of X X . As a measure of smoothness, we consider the total variation distance between the distributions of S n S_n and 1 + S n 1+S_n . Our approach uses new auxiliary inequalities for a generalized normalized matrix hafnian and for inverse moments of non-negative random variables, which could be of independent interest. This approach is also used to prove upper bounds of the Lévy concentration function of S n S_n in the case of independent real valued random variables X j , r X_{j,r} .

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In the present work we establish for the first time a class of (P,m)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$(P,\\mathrm{m})$\\end{document}-superquadratic functions and look into its features. Using them, we come up with the Jensen and Hermite–Hadamard inequalities, as well as the fractional versions of Hermite–Hadamard inequalities with respect to Riemann–Liouville fractional integral operators. The findings are confirmed by certain numerical calculations and graphical depictions that take a few appropriate examples into account. The study is enhanced by the addition of applications of special means, moments of random variables, and modified Bessel functions of the first kind. This is achieved by considering new functions pertaining to the uniform probability density function and taking the modified Bessel functions of the first kind into consideration. The new results clearly provide extensions and improvements of the work given in the literature.

Chance-constrained stochastic optimal control of epidemic models: A fourth moment method-based reformulation

This work proposes a methodology for the reformulation of chance-constrained stochastic optimal control problems that ensures reliable uncertainty management of epidemic outbreaks. Specifically, the chance constraints are reformulated in terms of the first four moments of the stochastic state variables through the so-called fourth moment method for reliability. Moreover, a spectral technique is employed to obtain surrogate models of the stochastic state variables, which enables the efficient computation of the required statistics. The practical implementation of the proposed approach is demonstrated via the optimal control of two different stochastic mathematical models of the COVID-19 transmission. The numerical experiments confirm that, unlike those reformulations based on the Chebyshev–Cantelli’s inequality, the proposed method does not exhibit the undesired outcomes that are typically observed when a higher precision is required for the risk level associated to the given chance constraints.

Integral inequalities of h‐superquadratic functions and their fractional perspective with applications

The purpose of this article is to provide a number of Hermite–Hadamard and Fejér type integral inequalities for a class of ‐superquadratic functions. We then develop the fractional perspective of inequalities of Hermite–Hadamard and Fejér types by use of the Riemann–Liouville fractional integral operators and bring up with few particular cases. Numerical estimations based on specific relevant cases and graphical representations validate the results. Another motivating component of the study is that it is enriched with applications of modified Bessel function of first type, special means, and moment of random variables by defining some new functions in terms of modified Bessel function and considering uniform probability density function. The results in this paper have not been initiated before in the frame of ‐superquadraticity. We are optimistic that this effort will greatly stimulate and encourage additional research.

A direct and analytical method for inverse problems under uncertainty in energy system design: combining inverse simulation and Polynomial Chaos theory

This article introduces and formalizes a novel stochastic method that combines inverse simulation with the theory of generalized Polynomial Chaos (gPC) to solve and study inverse problems under uncertainty in energy system design applications. The method is particularly relevant to design tasks where only a deterministic forward model of a physical system is available, in which a target design quantity is an input to the model that cannot be obtained directly, but can be quantified reversely via the outputs of the model. In this scenario, the proposed method offers an analytical and direct approach to invert such system models. The method puts emphasis on user-friendliness, as it enables its users to conduct the inverse simulation under uncertainty directly in the gPC domain by redefining basic algebra operations for computations. Moreover, the method incorporates an optimization-based approach to integrate supplementary constraints on stochastic quantities. This feature enables the solution of inverse problems bounding the statistical moments of stochastic system variables. The authors exemplify the application of the proposed method with proof-of-concept tests in energy system design, specifically performing uncertainty quantification and sensitivity analysis for a Multi-Energy System (MES). The findings demonstrate the high accuracy of the method as well as clear advantages over conventional sampling-based methods when dealing with a small number of stochastic variables in a system or model. However, the case studies also highlight the current limitations of the proposed method such as slow execution speed due to the optimization-based approach and the challenges associated with, for example, the curse of dimensionality in gPC.

Normal approximation for call function by refined Lindeberg principle

. The call function plays a crucial role in pricing the collateralized dept obligation (CDO) and we will generalize the refined Lindeberg principle developed in Chen, Shao, and Xu (2023) to study the normal approximation for the call function. In this article, we will give the uniform and non uniform bounds on normal approximation for the call function, under the assumptions that the third and (3+δ)-th moments of random variables exist, respectively, here δ ∈ ( 0 , 1 ] .

Third moment method for reliability analysis with uncertain moments characterized as interval variables

Traditional reliability analysis aims to compute the failure probability based on probability distribution functions, which are constructed using the moments of random parameters. In practice, however, appropriate samples may be insufficient to obtain deterministic values of the moments of all random variables and the exact value of failure probability cannot be obtained. To be consistent with the reality, the uncertainties in moments can be measured as interval variables, and then the bounds of failure probability should be evaluated. In this study, an idealized case is considered, where there is at most one imprecise moment associated with any given input random variable. A third moment method is proposed with uncertain moments measured as interval variables, and is named as TMI method. The proposed TMI method is straightforward including only four steps. Firstly, the derivative of performance function to random variables having uncertain moments is calculated, with the random variables set to be their mean values. Secondly, the values of uncertain moments for computing the bounds of failure probability are determined. Then, with inverse normal transformation defined based on the moments, the performance function at the bounds in Gaussian space is directly constructed. Finally, bounds of failure probability can be evaluated by two times of classical reliability analysis corresponding to the constructed performance functions. The application of TMI method is validated by numerical examples, including high-dimensional and strong nonlinear problems.

Are exchange rates absorbers of global oil shocks? A generalized structural analysis

This paper studies the impact of global oil market shocks on the level, volatility, and correlation dynamics of real effective exchange rates over time. We find that the USD and the EURO act as shock absorbers, as shocks to the global oil market explain substantial proportions of the volatility and correlation dynamics of each exchange rate – compared to the small or even negligible contributions of exogenous interest rate and effective exchange rate shocks. Further we find an interesting “puzzle” that the Euro Area unexpectedly behaves as if it were an oil exporting country - the EURO appreciates in response to a flow demand shock that increases oil price. Our findings are garnered from a generalized time-varying SVAR model with stochastic volatility that allows for correlated disturbances between observation and transition equations and among transition equations themselves. This approach of enriching dynamics between the first and second moments of endogenous variables is particularly suitable for capturing the transmission of structural level oil shocks to the volatility of exchange rates and their correlation with the global oil market.

On Integral Representations Involving the Probability Generating Function for Inverse Moments of Positive Discrete Random Variables

I simplify and note extensions of results of Shibu et al. Sankhya¯\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\overline{a}$$\\end{document}, Ser. A85 (2023a,b) concerning integral representations involving the probability generating function for inverse moments of positive discrete random variables, both univariate and multivariate.

A fast algorithm for computing product moments of multivariate normal random variables

We provide a simple identity that decomposes a product moment of multivariate normal random variables as a sum of various products of univariate moments of one of the random variables and multivariate moments of the other random variables. The new identity allows for much faster computation of the product moments of multivariate normal random variables than existing methods.

Geometrically exact 3D beam theory with embedded strong discontinuities for modeling of localized failure in bending

In this work, we propose the elastoplastic model of a three-dimensional (3D) geometrically exact beam, which can capture localized bending deformation leading to a plastic hinge. This is a follow-up work to Tojaga et al. (2023), where we only studied the fracture of fiber-like beam structures that break in mode I or mode II, here extended to handle beam-like behavior with bending failure and the main difficulty of the non-vectorial character of large 3D rotations. The Reissner model is chosen for representing the large elastic and large plastic deformations of such a beam model, which leads to the multiplicative decomposition of the rotation tensor. We shown how to replace this with the corresponding additive decomposition of the rotation vector derivatives, to be performed in the material description in the tangent space of SO(3) manifold in the initial configuration. The plasticity model for which we give a more detailed implementation employs a Rankin-like multi-surface plasticity criteria, where each moment vector component (i.e. torsion or bending) is considered separately. The non-local variational formulation is proposed to deal with the softening phenomena characteristic of localized bending failure, where the fracture energy is introduced as the main parameter for softening. The discrete approximation is built in terms of the embedded-discontinuity finite element method (ED-FEM), which introduces a jump in rotation vector that can be handled at the level of a particular beam element. This kind of approach builds upon the best approximation property of the FEM discrete approximation in the energy norm and provides the best-approximation property for the dissipated energy computations, and thus optimal computational accuracy if the quantity of interest is inelastic dissipation. The computations are carried out by the operator split method, which separates the computations of global state variables (displacements and moments) from local (plastic curvature) variables. Several illustrative examples are provided to confirm an excellent performance of the proposed methodology.

New local fractional Hermite-Hadamard-type and Ostrowski-type inequalities with generalized Mittag-Leffler kernel for generalized h-preinvex functions

Abstract In this study, based on two new local fractional integral operators involving generalized Mittag-Leffler kernel, Hermite-Hadamard inequality about these two integral operators for generalized h h -preinvex functions is obtained. Subsequently, an integral identity related to these two local fractional integral operators is constructed to obtain some new Ostrowski-type local fractional integral inequalities for generalized h h -preinvex functions. Finally, we propose three examples to illustrate the partial results and applications. Meanwhile, we also propose two midpoint-type inequalities involving generalized moments of continuous random variables to show the application of the results.

New fractal–fractional Simpson estimates for twice differentiable functions with applications

In this article, we establish a new auxiliary identity on fractal sets for twice local differentiable function involving extended fractal integral operators. Testing this identity together with generalized fractal Hölder’s and Power-mean integral inequalities, we develop some new fractal–fractional Simpson’s type inequalities. Furthermore, we use modified Yang Hölder’s and Power-mean inequality to create new fractal estimates. We also give comparison analysis of bounds and show how the modified form of Yang Hölder’s and Power-mean integral inequalities can result in improved lower upper bounds. We also provide concrete examples to examine the validity of obtain results numerically and also justify them by 2D and 3D graphical analysis. As implementations, we operate our findings to get new applications in form of ζ-type special means, moment of random variables and wave equations.

Correlation Structure of Steady Well‐Type Flows Through Heterogeneous Porous Media: Results and Application

AbstractSteady flow toward a fully penetrating well takes place in a natural porous formation, where the erratic spatial variations, and the raising uncertainty, of the hydraulic conductivity K are modeled within a stochastic framework which regards the log‐conductivity, ln K, as a Gaussian, stationary, random field. The study provides second order moments of the flow variables by regarding the variance of the log‐conductivity as a perturbation parameter. Unlike similar studies on the topic, moments are expressed in a quite general (valid for any autocorrelation function of ln K) and very simple (from the computational stand point) form. It is shown that the (cross)variances, unlike the case of mean uniform flows, are not anymore stationary due to the dependence of the mean velocity upon the distance from the well. In particular, they vanish at the well because of the condition of given head along the well’s axis, whereas away from it they behave like those pertaining to a uniform flow. Then, theoretical results are applied to a couple (one serving for calibration and the other used for validation purposes) of pumping tests to illustrate how they can be used to determine the hydraulic properties of the aquifers. In particular, the concept of head‐factor is shown to be the key‐parameter to identify the statistical moments of the random field K.

On the exact and population bi-dimensional reproduction numbers in a stochastic SVIR model with imperfect vaccine

We aim to quantify the spread of a direct contact infectious disease that confers permanent immunity after recovery, within a non-isolated finite and homogeneous population. Prior to the onset of the infection and to prevent the spread of this disease, a proportion of individuals was vaccinated. But the administered vaccine is imperfect and can fail, which implies that some vaccinated individuals get the infection when being in contact with infectious individuals. We study the evolution of the epidemic process over time in terms of a continuous-time Markov chain, which represents a general SIR model with an additional compartment for vaccinated individuals. In our stochastic framework, we study two bi-dimensional variables recording infection events, produced by a single infectious individual or by the whole infected group, taking into account if the newly infected individual was previously vaccinated or not. Theoretical schemes and recursive algorithms are derived in order to compute joint probability mass functions and factorial moments for these random variables. We illustrate the applicability of our techniques by means of a set of numerical experiments.

Moment-based approximations for stochastic control model of type (s, S)

In this study, we propose an approximation for a renewal reward process that describes a stochastic control model of type (s, S) based on the first three moments of demand random variables. Various asymptotic expansions for this model exist in the literature. All these studies rely on the condition of knowing the distribution function of demand random variables and require obtaining the asymptotic expansion of the renewal function produced by them. However, obtaining a renewal function can be challenging for certain distribution families, and in some cases, the mathematical structure of the renewal function is difficult to apply. Therefore, in this study, simple and compact approximations are presented for the stochastic control model of type (s, S). The findings of this study rely on Kambo’s method, through which we obtain approximations for the ergodic distribution, and the n t h order ergodic moments of this process. To conclude the study, the accuracy of the proposed approximate formulas are examined through a specialized illustrative example. Moreover, it has been noted that the proposed approximation is more accurate than the approximations existing in the literature.