Random Coefficients Research Articles

MST Initialization Based Intuitionistic Fuzzy c Means Clustering Using LINEX Hellinger Distance and Its Applications

Due to the uncertainty and fuzziness of information, the traditional clustering analysis method sometimes cannot meet the requirement in practice. The clustering method based on intuitionistic fuzzy set has attracted more and more scholars attention nowadays. This paper discusses the intuitionistic fuzzy C-means clustering algorithm. There are a number of clustering techniques developed in the past using different distance/similarity measure. In this paper, we proposed a improved edge density minimal spanning tree initilization method using LINEX hellinger distance based weighted LINEX intuitionistic fuzzy c means clustering. IFCM considered an uncertainty parameter called hesitation degree and incorporated a new objective function which is based upon intutionistic fuzzy entropy in the conventional Fuzzy C-means. The clustering algorithm has membership and non membership degrees as intervals. Information regarding membership and typicality degrees of samples to all clusters is given by algorithm. Furthermore, the algorithm is extended for calculating membership and updating prototypes by minimizing the new objective function of weighted LINEX intuitionistic fuzzy c-means. Finally, the developed algorithms are illustrated through conducting experiments on random dataset, partition coefficient and validation function are used to evaluate the validity of clustering also this paper compares the results of proposed method with the results of existing basic intuitionistic fuzzy c-means.

Genomic and phenotypic selection indices for decreased birth weight, shortening fattening period, and regulating total weight gain in beef cattle.

To develop genomic and phenotypic indices for beef cattle selection that afford progeny with reduced birth weight and fattening period. The indices should also allow regulating total weight gain during the fattening period and avoid marked increases of inbreeding. Whether addition of constraint on total weight gain to constraints on body weight gain at up to four specific time points during the fattening process was effective for the weight gain until the end of the fattening period was examined in two selection indices with the selection trait being a phenotypic or a genomic breeding value random regression coefficient. Both indices afforded cattle with desired weight gains at specific time points and desired total weight gains. One cycle of index-based selection made it possible to shorten the fattening period two weeks compared with that before selection while maintaining the same final fattening weight as before selection. The impact of constraining total weight gain was smallest when the number of weight gain constraints at specific time points was three or four. However, constraining total weight gain was necessary to avoid poor total weight gain when the number of weight gain constraints at specific time points was only one or two. The developed indices make it possible to regulate total weight gain during the fattening process and to achieve desired weight gains at specific time points. Importantly, the indices bring about genetic improvement without an excessive increase of inbreeding. Thus, we expect that these indices will contribute to sustainable genetic improvement in cattle while maintaining genetic diversity.

Decomposing Impact on Longitudinal Outcome of Time-Varying Covariate into Baseline Effect and Temporal Effect

Longitudinal processes are often associated with each other over time; therefore, it is important to investigate the associations among developmental processes and understand their joint development. The traditional latent growth curve model (LGCM) with a time-varying covariate (TVC) provides a method to estimate the TVC effect on a longitudinal outcome while modeling the outcome’s change. However, it does not allow the TVC to predict variations in the random growth coefficients. We propose decomposing the TVC into initial trait and temporal states using three methods to address this limitation. In each method, the baseline of the TVC is viewed as an initial trait, and the corresponding effects are obtained by regressing random intercepts and slopes on the baseline value. Temporal states are characterized as (a) interval-specific slopes, (b) interval-specific changes, or (c) changes from the baseline at each measurement occasion, depending on the method. We demonstrate our methods through simulations and real-world data analyses, assuming a linear–linear functional form for the longitudinal outcome. The results demonstrate that LGCMs with a decomposed TVC can provide unbiased and precise estimates with target confidence intervals. We also provide OpenMx and Mplus 8 code for these methods with commonly used linear and nonlinear functions.

Stability, Uniqueness and Existence of Solutions to McKean–Vlasov Stochastic Differential Equations in Arbitrary Moments

We deduce stability and pathwise uniqueness for a McKean–Vlasov equation with random coefficients and a multidimensional Brownian motion as driver. Our analysis focuses on a non-Lipschitz continuous drift and includes moment estimates for random Itô processes that are of independent interest. For deterministic coefficients, we provide unique strong solutions even if the drift fails to be of affine growth. The theory that we develop rests on Itô’s formula and leads to pth moment and pathwise exponential stability for p≥2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$p\\ge 2$$\\end{document} with explicit Lyapunov exponents.

Novel Weakness Multivariate Quadratic Structures Detected within Macaulay Matrix

The security of a Multivariate Public-Key Cryptosystem (MPKC) is based on the hard mathematical problem of solving Multivariate Quadratic (MQ) equations over finite fields, also known as the MQ problem. An MPKC has the potential to be a post-quantum cryptosystem. In this paper, we identify new weaknesses in the Macaulay matrix identified via Wang's technique, which was initially designed for solving multivariate quadratic equation systems. This new weakness occurs in the case of random coefficients in any column vector for different variables of monomials and random coefficients are assigned to other monomials. The weakness is exposed through the use of Gaussian elimination to obtain a univariate equation. We illustrate our findings using a random example.

An efficient ADAM-type algorithm with finite elements discretization technique for random elliptic optimal control problems

We consider an optimal control problem governed by an elliptic partial differential equation (PDE) with random coefficient, and introduce an efficient numerical method for the problem under concern. Based on a finite element discretization of the constraint PDE and objective functional, an ADAM-type iteration scheme is proposed to compute the approximated optimal control, which equips the stochastic gradient iteration with modified momentum acceleration and adaptive step size. We provide theoretical bounds on both the discretization and iteration error of the proposed numerical method. Our method is tested on two examples, and the numerical results show the efficiency and performance of the method.

Computational Methods for Stationary and Nonstationary Fokker–Planck–Kolmogorov Equations with Random Coefficients

In this paper, methodological approaches for distribution functions of nonlinear stochastic differential equations (SDEs) with uncertain parameters are elaborated. The stationary and nonstationary Fokker–Planck–Kolmogorov (FPK) equations associated with the considered random equations are investigated. A semi-analytical approach based on the extended exponential closure handling the parameters uncertainty effects is presented. A new numerical method coupling the polynomial chaos with the differential quadrature method (DQM) is elaborated for the stationary case. A compact matrix formulation is established allowing considering a large number of generalized polynomial chaos. For nonstationary random FPK equations, the polynomial chaos is coupled with the quadrature discretization. A symmetric discretization of the main Fokker–Planck operator using a tensor product of the Hilbert state space and the Hilbert probabilistic space has resulted. The obtained eigenvalues and random eigenfunctions are used for a spectral decomposition. The time solution is obtained by the spectral expansion as well as by the random Euler stochastic method. The accuracy, effectiveness and advantages of the developed procedure in analyzing the stationary and nonstationary probabilistic solutions of nonlinear stochastic dynamical systems with uncertain parameters excited by a Gaussian white noise are demonstrated.

Tandem gait step-width increases more rapidly in more severely affected people with Parkinson's disease

IntroductionTandem gait performance reportedly predicts fall risk in people with Parkinson's disease (PwPD) and help distinguish PwPD from atypical parkinsonism. In a cross-sectional study, we previously showed that tandem gait step-width widens with increasing Hoehn and Yahr (H&Y) staging scores. In this longitudinal study, we aimed to determine if progression in tandem gait deficits is dependent on disease severity in PwPD. MethodsParticipants underwent an instrumented tandem gait measurement every 6 months for at least 2 years. The mean and variability of 4 tandem gait parameters were calculated at each visit: step-width, step-length, step-time, and step-velocity. The change in these parameters over time for 3 H&Y groups (stage 1, 2 and 2.5+) compared to aging controls was determined using a random coefficients regression model. The annual percent change in tandem gait parameters was correlated with initial disease features using Kendall's τB. Results66 participants were analyzed (46 PD, 20 controls). Mean step-width increased over time in an H&Y stage-dependent manner, with H&Y 2 and H&Y 2.5+ experiencing increases of 6% and 10% per year (p = 0.001 and 0.024 respectively). Annual percent-change in step-width was correlated with initial motor Unified Parkinson's Disease Rating Scale (UPDRS) scores (Kendall's τB = 0.229), total UPDRS scores (τB = 0.249), H&Y scores (τB = 0.266) and inversely correlated with Montreal Cognitive Assessment (MoCA) scores (τB = −0.209; ps ≤ 0.019). ConclusionTandem gait step-width widens over time more rapidly in more severely affected PD patients. These results suggest that tandem gait should be routinely clinically evaluated and considered in the management of imbalance in PwPD.

Domain-Linked and Domain-Specific Competence: a Validation Study of a Two-Dimensional Model of Economic Vocational Competence in Germany

Modeling vocational competence is increasingly crucial for monitoring and enhancing the quality of Vocational Educational Training (VET), particularly in the context of ongoing international comparative studies known as "large-scale assessments" of vocational education and training. This study endeavors to provide well-structured and guideline-compliant empirical evidence for the validation of the two-dimensional construct of economic vocational competence, advancing beyond the current state of research. A sample of 1438 first-year apprentices from two federal states in Germany participated as test-takers. The authentic assessment framework comprised 24 items, assessing two dimensions of vocational competence: domain-linked competence and domain-specific competence in the business/commercial domain. Measurement invariance was assessed across (1) federal states and (2) versions of test booklets, and the Multidimensional Random Coefficient Multinomial Logit model was employed to examine the quality of the two-dimensional vocational competence construct. The results supported the validity of the structure, highlighting the differentiation between domain-linked competence and domain-specific competence. This provides a more substantively accurate representation of trainees' vocational competence compared to a unidimensional model.

Does Mainstreamed Aid Advance Gender Parity? Insights from Empirical Evidence

This study investigates the effectiveness of gender-mainstreamed aid in mitigating gender inequality. We develop a robust theoretical model that accounts for the potential positive and perceived negative effects of shifts toward gender parity, capturing diverse societal perspectives. Utilizing a comprehensive dataset on aid activities focused on gender (in)equality and women’s empowerment across 118 countries from 2009 to 2022, primarily low-income nations, we employ panel fixed-effects and mixed-effects random coefficient models to examine the impact of gender-related aid on gender inequality. Our findings reveal that significant gender-related aid (SGRA), which integrates gender considerations into broader development projects, reduces gender inequality in 115 out of 118 countries. In contrast, principal gender-related aid (PGRA), which explicitly targets gender equality, shows significant effects in only 85 countries. When analyzing the effects of both components of gender-related aid, we find that SGRA consistently impacts gender inequality. However, the effectiveness of PGRA becomes less clear-cut. This observation, coupled with the variation in the effectiveness of the components across countries, underscores the importance of developing strategies tailored to country-specific needs and conditions in promoting gender parity effectively.

Exponential Dispersion accelerated degradation modelling and reliability assessment considering initial value and processes heterogeneity

In the production and operation, inherent variability and uncertainty necessitate addressing unit-to-unit heterogeneity in initial performance values and degradation processes. This article presents a bi-stochastic exponential dispersion process (BS-ED) designed to account for heterogeneity in both initial performance values and degradation processes. First, based on the ED process, the time and acceleration covariates are introduced to form a nonlinear accelerated ED process, and a random effect coefficient associated with the accelerated stress is incorporated to consider the heterogeneity of the process. Meanwhile, through the modelling of degradation time-shift, a degradation model considering the stochastic initial value of the product performance is developed. To effectively conduct the statistic inference of the BS-ED process, an improved stochastic EM algorithm is proposed, and the information matrix and Ito calculus are combined to estimate the confidence intervals. Finally, the stability of the method is verified by simulation and analyzed by two real cases.

Research on stochastic error suppression method of FOG based on quasi-proportional information

Abstract In order to improve the precision of fiber optic gyro (FOG), it is necessary to suppress the stochastic error effectively. Therefore, the stochastic error suppression of FOG is studied. Firstly, the data of FOG at room temperature and variable temperature are analyzed. Then, a general form of FOG random error model is derived, and an adaptive filtering method based on quasi-proportional information is proposed to estimate the process noise covariance effectively. At last, the room temperature experiment, the variable temperature experiment and the dynamic experiment are carried out. Compared with the recently proposed adaptive filter algorithm, the variance reduction of the proposed algorithm at the average time of 33 s, 54 s and 55 s is more than 70%, and the suppression effect of the algorithm on the random walk coefficient is improved by more than 2%, showing strong robustness.

Predicting the Deformation of a Slope Using a Random Coefficient Panel Data Model

Engineering constructions in coastal areas not only affect existing landslides, but also induce new landslides. Variation of the water level makes the coastal area a geological hazard-prone. Prediction of the slope displacement based on monitoring data plays an important role in early warning of potential landslide and slope failure, and supports the risk management of hazards. Given the complex characteristic of the slope deformation, we proposed a prediction model using random coefficient model under the frame of panel data analysis, so as to take the correlation among monitoring points into consideration. In addition, we classified the monitoring data using Gaussian mixture model, to take the temporal-spatial characteristics into consideration. Monitoring data of Guobu slope was used to validate the model. Results indicated that the proposed model have a better performance in prediction accuracy. We also compared the proposed model with the BP neural network model and temporal – temperature model, and found that the prediction accuracy of the proposed model is better than those of the two control models.

Exploring newcomer voice and silence dynamics: The role of organizational socialization

AbstractThis paper examines the temporal dynamics of newcomer voice and silence in organizations, focusing on the role of organizational socialization. While prior research has explored the antecedents and effects of newcomer voice behaviour, little attention has been given to how voice and silence evolve over time as newcomers become more familiar with their roles and the organizational context. Drawing on multiple goal pursuit theory, this study investigates how organizational socialization influences the trajectories of newcomer promotive voice, prohibitive voice and employee silence. We test our hypotheses on a sample of 198 organizational newcomers over four waves of data, employing random coefficient growth models. The findings suggest that employee voice and silence follow distinct trajectories and provide partial support for the influence of organizational socialization on these changes in newcomer behaviour. Implications for theory and practice are discussed.

Vibration-Based Railway Suspension Monitoring Under Varying Operating Conditions via Robust Statistical Time Series Type Methods

The on-board condition monitoring of railway suspension components is important for ensuring passenger comfort, maintaining proper operation, enhancing safety, and reducing operational costs. In this context statistical time series type random-vibration-based methods constitute a potentially efficient and cost-effective framework. Yet a significant challenge emerges from the competing requirements for high sensitivity to suspension component degradation and robustness to varying Operating Conditions (OCs) and uncertainty. Varying Operating Conditions and uncertainty are related to factors such as varying payload, travelling speed, and rail profile. The problem is further aggravated by the complexity of the vehicle dynamics, including the wheelbase filtering effect. Despite the significant progress made so far and the development of various methods, the challenges related to varying OCs and uncertainty largely remain as a technical barrier. The aim of the present study is the introduction of a data-based methodology aspiring to overcome them. The methodology is based on Multiple-Input Single-Output Transmittance Function (MISO-TF) time series models of a special form allowing for eliminating the effects of varying rail profile while properly treating the wheelbase filtering effect. Monitoring is based on a MISO-TF feature space, using either a Random Coefficient Gaussian Mixture or a Multiple Model based scheme. The former, although more elaborate, allows for the introduction of a complete probabilistic framework. An interesting advantage of both versions is that operation can be based on a very limited number of sensors and only a partial data-driven model of the dynamics. The methodology is comprehensively assessed via hundreds of SIMPACK based Monte Carlo runs under variable payload and rail profiles. The achievable performance is shown to be very good for both versions and for 10%, 15%, and 20% of condition degradation in either primary or secondary suspension components.

Multilevel Logistic Regression Analysis of Correlates of Graduate Employment by Fields of Specialization

A number of research settings involve data having a multilevel (hierarchical) structure. Failing to take into account such hierarchical structures may lead to wrong statistical inference. The objective of this study was to explore the correlates of employment status among graduates of Ethiopian Higher Education Institutions nested within their fields of specialization using hierarchical two‐level logistic regression model. The study sample consisted of 2569 graduates nested within 20 fields of specialization. The impact of level-1 covariates and fields of specialization on employment outcome was explored using average marginal effects. The estimated variance of the random (field of specialization) effects was found to be significant – an indication that a multilevel model is appropriate. Likelihood ratio test confirmed that the multilevel random coefficients model was a better fit to the data. Besides demographic and socio-economic characteristics of graduates (and their households), the dimensions related to the skills and competences of graduates, namely perceived level of training of graduates on technical/practical skills and soft skills at their universities as well as cumulative grade point average, had significant influence on employment outcomes. The results also revealed that the effect of practical skills on graduate employment varied across fields of specialization. Average marginal effects analysis indicated that the probability of employment was lower for graduates who were unmarried, aged below 25, with disability, from a low income family and those graduates who perceived that their preparation for practical as well as soft skills required in the job market was less than expected/very poor. Moreover, practical and soft skills differentials in the probability of graduate employment were found to diminish with an increase in cumulative grade point average. To improve graduate employment, provision of adequate training in practical/technical and soft skills is recommended.

The distributional implications of health taxes: A case study on the Italian sugar tax

As over-consumption of sugar-sweetened beverages (SSBs) is considered a major contributor to the rising prevalence of obesity and associated diseases, public authorities from different countries are considering the introduction of SSBs taxation. In this study, we evaluate the potential impact of the upcoming Italian sugar tax on SSBs and sugar consumption, also accounting for differences across socio-economic groups. We also analyze alternative SSBs tax designs (i.e., excise tax on sugar and two-tier tax based on sugar content) to compare their effectiveness and provide a more general analysis about the outcomes of SSBs taxation. In our empirical analysis, we first estimate consumers’ demand for SSBs using the random coefficient logit demand model (Berry, Levinsohn, and Pakes, 1995) and Nielsen Household Panel data of SSBs purchases for the period 2019–2020. Then, the estimated demand parameters and marginal costs for SSBs are employed to conduct counterfactual simulations to derive the new market equilibria under the simulated SSBs tax scheme scenarios. Our results show that the Italian sugar tax is the most effective in reducing SSBs and sugar consumption (on average, by 18% and 24% respectively) among all the simulated tax scenarios. This is also due to the strategic reactions of SSBs manufacturers who over-shift the change in marginal cost (i.e., tax rate) to final prices. Moreover, despite being financially regressive, taxes on SSBs may be progressive from a health perspective, as low-income groups experience the greatest fall in SSBs and sugar consumption. Reinvesting tax revenues in health-related programs targeting the most vulnerable socio-economic groups (i.e., low-income households with children) may minimize the regressivity of SSBs taxes.

A novel two-layer fuzzy neural network for solving inequality-constrained [formula omitted]-minimization problem with applications

In this paper, we propose a novel two-layer fuzzy neural network model (TLFNN) for solving the inequality-constrained ℓ1-minimization problem. The stability and global convergence of the proposed TLFNN model are detailedly analyzed using the Lyapunov theory. Compared with the existing three-layer neural network model (TLNN) recently designed by Yang et al., the proposed TLFNN model possesses less storage, stronger robustness, faster convergence rate and higher convergence accuracy. These advantages are illustrated by some numerical experiments, where it is shown that the TLFNN model can achieve a convergence accuracy of 10−13 within 5s while the TLNN model can only acquire 10−6 in 105s when some random coefficient matrices are applied. Since the linear equality-constrained conditions can be equivalently transformed into double inequality-constrained ones, some simulation experiments for sparse signal reconstruction show that the proposed TLFNN model also has less convergence time and stronger robustness than the existing state-of-the-art neural network models for the equality-constrained ℓ1-minimization problem.

Linear combinations of i.i.d. strictly stable variables with random coefficients and their application to anomalous diffusion processes

We show that linear combinations of independent and identically distributed strictly stable variables with positive random coefficients is equal in distribution to a function of these random coefficients times a random variable from the same stable distribution. Furthermore, this result is used to show that a random linear combination of independent standard Wiener processes has the distribution of a function of these random coefficients times one standard Wiener process. In opposition to the central limit theorem, this result does not require a large number of terms but it holds with two or more terms. This has implications to simplify stochastic differential equations with a finite number of noises with random coefficients that can be used in modeling anomalous diffusion.

The value of waiting spaces: Tourists’ willingness to pay for high-quality bus terminals

Transport quality has long been recognized as an important factor in influencing travelers’ behavior, and transport terminal quality undoubtedly plays no small part. Indeed, transit promotion policies explicitly based on qualitative factors and high-standard architecture are increasingly being adopted in designing new bus terminals and stops. High architectural standards can be found in several bus terminals worldwide. Nevertheless, the literature in transport sector has yet to explore the impact of bus terminal “hedonic quality” on users’ behavior or use their willingness to pay (WTP) in cost–benefit analyses or other transport policy applications. Hedonic value is here intended as the aggregate of all elements related to travelers’ pleasure in spending time in a terminal where architectural beauty and the passenger services offered are arguably the most visible and representative attributes. Within this context, we propose a quantitative analysis of the perceived hedonic value of bus nodes in terms of users’ WTP (pure preference) for a high-quality bus terminal. To this end a discrete choice experiment based on a visual immersive experience was performed, and data were collected from 324 residents of Milan (Italy) traveling for tourism. Different binomial Mixed Logit models with panel data and random coefficients were estimated for the purpose. Monetary valuation of pure preference for using a high-quality bus terminal was estimated with a mean of about 25 % of the average trip cost observed for the case study considered. This pure preference means that the Italian tourist is willing to spend up to €4.35/trip more for a high-quality bus waiting space or travel up to 28.2 min/trip more, instead of using a traditional bus terminal for the same trip. Category-specific analyses show that females > 30 years old have a greater pure preference for a high-quality bus terminal (up to + 220 %) against males and young users. The employed also have a higher (+42 %) pure preference for the beauty of a bus terminal against others. The results of this paper should be compared with those from other case studies as they have a potential impact for transportation planning applications, underling the importance of also incorporating “hedonic quality” as explicit design variables for new/revamped transportation hub. At the same time, new challenges are posed for modeling user behavior and determining quality-related indicators and measures.