Assumption Of Homogeneity Research Articles

CureAuxSP: An R package for estimating mixture cure models with auxiliary survival probabilities

Background and Objective:There is a rising interest in exploiting aggregate information from external medical studies to enhance the statistical analysis of a modestly sized internal dataset. Currently available software packages for analyzing survival data with a cure fraction ignore the potentially available auxiliary information. This paper aims at filling this gap by developing a new R package CureAuxSP that can include subgroup survival probabilities extracted outside into an interested internal survival dataset. Methods:The newly developed R package CureAuxSP provides an efficient approach for information synthesis under the mixture cure models, including Cox proportional hazards mixture cure model and the accelerated failure time mixture cure model as special cases. It focuses on synthesizing subgroup survival probabilities at multiple time points and the underlying method development lies in the control variate technique. Evaluation of homogeneity assumption based on a test statistic can be automatically carried out by our package and if heterogeneity does exist, the original outputs can be further refined adaptively. Results:The R package CureAuxSP provides a main function SMC.AxuSP() that helps us adaptively incorporate external subgroup survival probabilities into the analysis of an internal survival data. We also provide another function Print.SMC.AuxSP() for printing the results with a better presentation. Detailed usages are described, and implementations are illustrated with numerical examples, including a simulated dataset with a well-designed data generating process and a real breast cancer dataset. Substantial efficiency gain can be observed by our results. Conclusions:Our R package CureAuxSP can make the wide applications of utilizing auxiliary information possible. It is anticipated that the performance of mixture cure models can be improved for the survival data with a cure fraction, especially for those with small sample sizes.

Distributed statistical learning algorithm for nonlinear regression with autoregressive errors

The growing size of modern data brings challenges to statistical learning, and substantial distributed algorithms have been proposed. However, most of them need the homogeneity assumption that the distribution of the local data is the same as that of the global data. This is seldom in practice, and the learning performance deteriorates seriously if this assumption is not satisfied. Moreover, they are only for independent data, and cannot incorporate the serial correlations between data. To solve these issues, we propose a novel distributed statistical learning framework for the nonlinear regression with autoregressive errors, which realizes communication-efficient distributed optimization, and overcomes the homogeneity assumption. The theoretical results also guarantee that the new distributed framework is equivalent to the global one. Numerical experiments also illustrate the good performance of the new method.

Geographic disaggregation of household surveys. Modified estimators for correcting misclassification bias

ABSTRACT We present a two-stage method to estimate spatial conditional means at a higher spatial resolution than the data actually have. In the first stage, we increase the spatial resolution of the data using classification tools and ancillary data. In the second stage, we estimate spatial conditional means (conditioning on the new spatial resolution). The estimation procedure in the second stage is not straightforward because the new finer spatial areas are subject to misclassification (measurement error). We prove that the least square (LS) estimators are biased under this framework and propose a consistent and asymptotically normal estimator under non-differential measurement errors. Given that the proposed estimator depends on unobservable terms, we also present its feasible version. Unlike most of the spatial downscaling methods, our proposal is non-model-based, and does not require area homogeneity assumptions. We assess analytical results by some Monte Carlo simulations, showing that our proposals work properly and outperform the spatial microsimulation approach. Finally, we conduct an empirical application where we analyse poverty and unemployment in one of the main urban agglomerates of Argentina known as Gran Rosario, we spatially disaggregate the original database to make inferences at a finer geographic scale.

The influence of non-state-owned shareholder governance on the governance level of state-owned enterprises--Based on the perspective of board resolution behavior and party organization governance.

With an increasing number of heterogeneous shareholders participating in corporate governance in reality, the assumption of shareholder homogeneity in agency theory is gradually relaxing in the modern field of corporate governance. The policy of mixed ownership reform in China provides empirical evidence for studying heterogeneous shareholder governance. To fully understand the governance effects of non-state shareholders, we employ the ownership proportion held by non-state shareholders among the top ten shareholders and the appointment of directors as measures for non-state shareholder governance. Using a panel fixed-effect model from the perspective of state-owned enterprises (SOEs) party organizations, we examine the impact of non-state shareholder governance on the governance level of SOEs. The study reveals that non-state shareholder governance positively affects the governance level of SOEs, with board resolutions playing a crucial role in this relationship. When party members serve as directors, the governance effect of non-state shareholders is more significant. Based on the aforementioned research findings, we recommend further refining corporate governance measures for SOEs within the context of SOE reforms. It is advisable to optimize the party organizational governance structure and leverage the synergistic effects of non-state shareholder governance and party organizational governance. Advancing reforms along the Pareto improvement path will contribute to establishing a distinctive corporate governance system for Chinese SOEs.

Modeling heterogeneous and adaptive homeowners’ decision-making for green retrofit

Green retrofitting (GR) of existing residential buildings is widely acknowledged as a potential solution for addressing climate and energy challenges. However, the realization of GR potential depends on homeowners’ decision-making, as they play a pivotal role in initiating GR and are the primary users of retrofitted homes. Previous studies have explored homeowners’ GR decisions from various perspectives. The majority of these studies may have encountered limitations, such as the use of static analysis methods, homogeneity assumptions, and oversimplified decision processes. These limitations can potentially hinder a comprehensive understanding of homeowners’ GR adoption, indicating the need for further research. To address this research gap, this study develops a decision-making model for homeowners that incorporates multiple dimensions: heterogeneity (e.g., individual attributes and GR motivations), a multi-stage decision process (needs-utility-willingness-adoption), and dynamic adaptability (both at individual and overall networks). The proposed model is empirically validated through Python simulations based on a case study in China. The findings of this study reveal the critical roles of GR needs, perceived (actual) utility, and perceived behavioral control in homeowners’ adoption of GR. Moreover, this study highlights the significance of coordinating homeowners’ overall perceived utility with the specific sub-utility associated with their motivations. Furthermore, empirical evidence underscores the influence of social factors on GR adoption and emphasizes the existence of gaps across different stages of the decision-making process. Drawing from these research findings, policy implications are proposed for promoting housing GR. This research contributes to the advancement of decision models for building GR and provides valuable insights for policymakers to promote housing GR.

Efficient computational modeling of electronic stopping power of organic polymers for proton therapy optimization

This comprehensive study delves into the intricate interplay between protons and organic polymers, offering insights into proton therapy in cancer treatment. Focusing on the influence of the spatial electron density distribution on stopping power estimates, we employed real-time time-dependent density functional theory coupled with the Penn method. Surprisingly, the assumption of electron density homogeneity in polymers is fundamentally flawed, resulting in an overestimation of stopping power values at energies below 2 MeV. Moreover, the Bragg rule application in specific compounds exhibited significant deviations from experimental data around the stopping maximum, challenging established norms.

On the boundary conditions for a thin circular plate conjugated to a massive body

The problem of deformation under the action of uniform pressure of a circular plate coupled with a massive base is considered, while the condition for the coupling of the plate with the base is modeled using boundary conditions of the generalized elastic embedding type, i.e. the relationship between the bending moment and forces at the edge of the plate with displacements and rotation angles through the compliance matrix. The main goal of the work is to study the influence of the elasticity of the embedding on the elastic response of the plate. The solution to the problem was obtained in the formulation of the linear theory of plates, the theory of membranes in the approximation of homogeneity of longitudinal forces, and the Foppl — von Karman theory, also in the approximation of the assumption of homogeneity of longitudinal forces. The values of the coefficients of the compliance matrix were obtained using the finite element method for the auxiliary problem and compared with the values of the coefficients obtained for related problems by analytical methods. Numerical results were obtained for an aluminum wafer on a silicon base. The obtained solution was compared with the solution obtained for the rigid embedment condition for all three models used. It is shown that in the case of large deflections (several plate thicknesses), taking into account the compliance of the embedment becomes essential.

A comparison of deflation methods for carbon footprint calculations using Japanese data

An embodied CO2 emissions intensity database estimated from input–output data at current prices is now available in Japan. This study compares two non-survey methods: The double-deflation (DD) method and an elaborated approach combining the DD method with the generalized RAS method to deflate an input–output table (IOT). We used these methods to estimate new datasets of embodied CO2 emission intensity data in Japan, utilizing Japan's IOTs for 2005, 2011, and 2015 at constant prices in 2015. Furthermore, we compare the estimated data of 367 commodity sectors for a specific year (2005 or 2011). The results reveal that the intensity for certain aggregated sector groups, such as non-ferrous metals and finance and insurance, exhibits higher uncertainty owing to the extreme price homogeneity assumption associated with the DD method. Consequently, we recommend that life-cycle-assessment practitioners use an open database when analyzing changes in the carbon footprint of products over time.

Three-dimensional simulation of an orifice flow with cavitation-induced air release

A model for the approximation of cavitation-induced air release in three-dimensional flow simulations is proposed. A cavitating orifice flow is investigated. It is assumed that vapor vanishes in the proximity of the orifice, and bubbles further downstream consist essentially of air. The model is based on a homogeneous mixture assumption and comprises one main parameter, which needs to be adjusted to the experimentally measured degassing fraction. Experimental validation is based on transmission light images downstream of the orifice. In the proximity of the orifice, the inclusion of air release in the CFD simulation yields a better agreement to experimentally measured cavitation intensity than the consideration of pure vapor only. It is concluded that a considerably larger amount of air is released than is dissolved in the evaporated amount of liquid. The simulation results suggest that the released air mass corresponds to about 1% of the evaporated liquid mass. These observations may be a good basis for purposeful future experiments, which are indispensable for the development of a more predictive approach of cavitation-induced air release in 3D CFD methods.

Small-Scale Cosmology Independent of the Standard Model

‘Small-scale cosmology’ is a theory designed to incorporate the linear redshift versus distance relation, which is inferred from observations, into the theoretical framework independent of the global Robertson–Walker–Friedman (RWF)-type models. The motivation behind this is that the RWF cosmological models, based on the assumptions of homogeneity and a constant matter density, as well as the concept of expanding space inherent to them are not applicable on the scales of observations from which the linear Hubble law is inferred. Therefore, explaining the Hubble law as the small redshift limit of the RWF model or as an effect of expanding space is inconsistent. Thus, the Hubble linear relation between the redshift of an extragalactic object and its distance should be considered an independent law of nature valid in the range of the distances where the RWF cosmology is not valid. In general, the theory, based on that concept, can be developed in different ways. In the present paper, ‘small-scale cosmology’ is formulated as a theory operating in the (redshift–object coordinates) space, which allows developing a conceptual and computational basis of the theory along the lines of that of special relativity. In such a theory, the condition of invariance of the Hubble law with respect to a change in the observer acceleration plays a central role. In pursuing this approach, the effectiveness of group theoretical methods is exploited. Applying the Lie group method yields transformations of the variables (the redshift and space coordinates of a cosmological object) between the reference frames of the accelerated observers. In this paper, the transformations are applied to studying the effects of the solar system observer acceleration on the observed shape, distribution and rotation curves of galaxy clusters.

Shock waves at final stages of cavity collapse in non-homogeneous liquid with divergenceless flow

We show the emergence of shock waves at the final stages of the complete collapse of a spherical cavity in a liquid with a smoothly decreasing density. The well-known Rayleigh assumption of fluid homogeneity is altered, while maintaining that of divergenceless flow. The fundamental difference between both infinite liquids is that his has an infinite mass, while ours, a finite one. Given the ease of deformation of non-homogeneous media in relation to homogeneous ones, as observed in several materials, all Rayleigh results are modified, including the cavity wall speed and acceleration, total time of complete collapse, and distribution of pressure in the infinite liquid. Rather than the homogeneous Rayleigh fluid, our non-homogeneous liquid can support a finite local sound speed. As a result, we succeed to show the emergence of shock patterns at the final stages of the cavity collapse. The analytical formulation is compared with underwater implosion and explosion experiments and simulations. Possible applications as a benchmark test for hydrocodes are briefly discussed.

A fractal Langmuir adsorption equation on coal: Principle, methodology and implication

The conventional Langmuir equation based on the homogeneous surface assumption is failed to characterize the gas adsorption in coal due to the obvious heterogeneity of the coal pore structure. Therefore, a novel fractal Langmuir adsorption equation on coal is proposed based on the fractal characteristics of pore heterogeneity in this paper. The pore heterogeneity in coal displays obvious fractal characteristic, which can be characterized by the fractal dimension for the pore tortuosity (DT) and the fractal dimension for the pore size (Df). The principles and methodology of the proposed equation are deduced and summarized in detail. The results indicate that the proposed fractal Langmuir adsorption equation displays the higher accuracy than the conventional Langmuir equation. In addition, the proposed equation is the function of the fractal dimension for the pore tortuosity (DT), the fractal dimension for the pore size (Df), and the pore structural parameters (λmin, λmax, and L), which more comprehensively reflects the influence of pore structure on adsorption. Therefore, the proposed equation achieves a breakthrough in the calculation of the methane adsorption capacity in pores with different pore diameter ranges, which can more accurately reflect the methane adsorption in coal pores compared with the conventional Langmuir equation.

Deep Neural Operator Enabled Concurrent Multitask Design for Multifunctional Metamaterials Under Heterogeneous Fields

AbstractMultifunctional metamaterials (MMM) bear promise as next‐generation material platforms supporting miniaturization and customization. Despite many proof‐of‐concept demonstrations and the proliferation of deep learning assisted design, grand challenges of inverse design for MMM, especially those involving heterogeneous fields possibly subject to either mutual meta‐atom coupling or long‐range interactions, remain largely under‐explored. To this end, a data‐driven design framework is presented, which streamlines the inverse design of MMMs involving heterogeneous fields. A core enabler is implicit Fourier neural operator (IFNO), which predicts heterogeneous fields distributed across a metamaterial array, thus in general at odds with homogenization assumptions. Additionally, a standard formulation of inverse problem covering a broad class of MMMs is presented, together with gradient‐based multitask concurrent optimization identifying a set of Pareto‐optimal architecture‐stimulus (A‐S) pairs. Fourier multiclass blending is proposed to synthesize inter‐class meta‐atoms anchored on a set of geometric motifs, while enjoying training‐free dimension reduction and built‐it reconstruction. Interlocking the three pillars, the framework is validated for light‐by‐light programmable nanoantenna, whose design involves vast space jointly spanned by quasi‐freeform supercells, maneuverable incident phase distributions, and conflicting figure‐of‐merits (FoM) involving on‐demand localization patterns. Accommodating all the challenges, the framework can propel future advancements of MMM.

Meta-Inverse Reinforcement Learning for Mean Field Games via Probabilistic Context Variables

Designing suitable reward functions for numerous interacting intelligent agents is challenging in real-world applications. Inverse reinforcement learning (IRL) in mean field games (MFGs) offers a practical framework to infer reward functions from expert demonstrations. While promising, the assumption of agent homogeneity limits the capability of existing methods to handle demonstrations with heterogeneous and unknown objectives, which are common in practice. To this end, we propose a deep latent variable MFG model and an associated IRL method. Critically, our method can infer rewards from different yet structurally similar tasks without prior knowledge about underlying contexts or modifying the MFG model itself. Our experiments, conducted on simulated scenarios and a real-world spatial taxi-ride pricing problem, demonstrate the superiority of our approach over state-of-the-art IRL methods in MFGs.

ANALYSIS FOR MULTIPLE RESPONSES IN A COMPLETELY RANDOMIZED EXPERIMENTAL DESIGN

Multiple responses are often generated in agricultural and forestry research. For example, the moisture content, fatty acids, carbohydrates, size, diameter, length, shape and hardness, among other characteristics are measured to cottonseed. Of course, multiple responses are observed in many other areas of research. Multivariate analysis of variance (MANOVA) can be useful for multiple response analysis when differences in treatment effects are to be determined. However, the performance of current post hoc tests in this context is not satisfactory due to the limitations of the available methods, or because they are difficult to use for non-statistician researchers. Furthermore, this methodology requires the assumptions of multivariate normality and homogeneity of variance and covariance matrices, assumptions that are difficult to verify if the sample size is small. This research proposes an alternative analysis to test the hypothesis of equality of effects between treatments and post hoc tests in the case of multiple responses. An asymptotic result is demonstrated for the random variable generated in the proposal for the case of uncorrelated normal variables and the case for correlated normal random variables is left open. A simulation study shows that the performance of the proposal with small samples is satisfactory in terms of power and that it has advantages compared to MANOVA. Furthermore, the methodological approach allows for post hoc testing in the case of multiple responses in the completely randomized experimental design.

Influence of Grazing on the Gestation Phase of Sows and the Birth of the Piglet

The present investigation was carried out in the “Rancho Rubén” pig farm, located in the Panecillo site of the Casacay Parish of the Pasaje Canton, Province of El Oro, Coastal Region of Ecuador. The aim of this work was to determine the possible benefits of grazing in pregnant breeders and newborn piglets. For the field experiment, the regulations of the Guide to Good Swine Practices (Agrocalidad-Ecuador) were taken into account. The variables evaluated were: initial weight of the sow, prepartum sow’s weight, postpartum sow’s weight, weight of the sow at weaning, number of piglets at birth, weight of the piglet at birth, mortality on the first day and feed consumption during gestation. A completely randomized design was used, with 2 treatments, each with a replica of 5 females, for a total of 20 animals; treatment 1 (balanced feeding and management in individual pens); and treatment 2 (feeding with balanced, and grazing). The statistical analysis was based on the book by Blasco [1], applying an ANOVA, prior to assumptions of normality and homogeneity, establishing the differences through the Bonferroni multiple comparison procedure (95% confidence). The statistical program used was Statgraphics Centurión XVI. The results show that there is no difference between the treatments with respect to the variables evaluated, except for the consumption of concentrate feed, which is observed to be reduced in grazing animals, assuming that the grazing system is beneficial by facilitating a more natural behavior and greater animal welfare.

Simulating Credit Loss Distributions: Empirical Versus the Vasicek Model

Because credit losses can be substantial, managing credit risk is a focus area of risk measurement and management. It is important for financial institutions to select credit risk models that accurately forecast losses. The Basel Committee on Banking Supervision (BCBS) chose the closed-form single risk factor Vasicek model for regulatory capital calculations. In this article, its forecast accuracy is compared with empirical loss distributions using simulated probabilities of default and losses given default. The effect of altering probabilities of default on asset correlations was analysed and how this affects credit portfolio loss distributions. The robustness of the Vasicek model against five different portfolios with unique compositions was explored: results highlight two key findings. Firstly, the Vasicek model is a good approximation of credit losses for a portfolio that does not contain dominating loans (it is, after all, based on the assumption of large-scale homogeneity). Secondly, the Vasicek model is a good approximation for expected loss (ELs) but lacks accuracy when determining extreme unexpected losses (ULs). Finally, credit capital requirements as a function of two variables are presented which reveals novel ways of viewing these values.

The compassion connection: Experience sampling insights into romantic attraction

Our study examines the relationship between self-compassion, other-compassion, and romantic attraction in couples, and questions the psychological homogeneity assumption—the idea that psychological responses are uniform across individuals and couples. We analyzed data from 161 participants in 84 couples, with an average age 32 (SD = 12.02), using smartphones for event sampling six times daily over a week to measure self-compassion, other-compassion, and attraction. Through within-person and network analysis, we discovered significant variability in how self and other-compassion influence attraction, identifying two distinct couple types: “synergistic,” where compassion significantly affects attraction, and “independent,” where it does not. Further analysis revealed that, when other-compassion is accounted for, males with high self-compassion were less attracted to their female partners. The significant diversity in how individuals and couples experience compassion and attraction challenges the assumption that conclusions drawn from group averages can be universally applied to individual couples. Clinically this means that efforts to enhance compassion in couples therapy should be tailored to the couple's unique dynamics. Indeed, for some men, emphasizing self-compassion without considering other-compassion could even be detrimental to the relationship. Our findings highlight the need for nuanced case formulation and personalized treatment planning in couples therapy, underscoring the complexity of relationship dynamics and the importance of rejecting “one size fits all” assumptions.

A Critical Examination of the Standard Cosmological Model: Toward a Modified Framework for Explaining Cosmic Structure Formation and Evolution

This paper explores the fundamental cosmological principle, with a specific focus on the homogeneity and isotropy assumptions inherent in the Friedmann model that underpins the standard model. We propose a modified redshift model that is based on the spatial distribution of luminous matter, examining three key astronomical quantities: light intensity, number density, and the redshift of galaxies. Our analysis suggests that the model can account for cosmic accelerated expansion without the need for dark energy in the equations. Both simulations and analytical solutions reveal a unique pattern in the formation and evolution of cosmic structures, particularly in galaxy formation. This pattern shows a significant burst of activity between redshifts 0 < z < 0.4, which then progresses rapidly until approximately z ≈ 0.9, indicating that the majority of cosmic structures were formed during this period. Subsequently, the process slows down considerably, reaching a nearly constant rate until around z ≈ 1.6, after which a gradual decline begins. We also observe a distinctive redshift transition around z ≈ 0.9 before the onset of dark-matter-induced accelerated expansion. This transition is directly related to the matter density and is dependent on the geometry of the universe. The model’s ability to explain cosmic acceleration without requiring fine tuning of the cosmological constant highlights its novelty, providing a fresh perspective on the dynamic evolution of the universe.

Pemodelan ARIMA-GARCH dalam Peramalan Kurs Rupiah Terhadap Yen dengan Masalah Keheterogenan Ragam

The currency exchange rate is the price of a country's currency expressed into another country's currency. At the beginning of 2020, the COVID-19 pandemic affected the weakening and changes in the Rupiah exchange rate against hard currencies, one of which was the Japanese Yen. This affects the expectations of LCS cooperation between Indonesia and Japan in terms of increasing the value of trade to investment between the two countries. Therefore, forecasting the upcoming currency exchange rate is indispensable to determine the upcoming macroeconomic policy. ARIMA is a commonly used quantitative method to forecast future data using past data patterns. The weakness of this method arises when the data violates the assumption of homogeneity of variety that often occurs in financial data, one of which is currency exchange rate data. The ARCH/GARCH model is an effective model for data with uncertain diversity characteristics. However, there is potential to combine ARIMA and ARCH/GARCH into an ARIMA-ARCH/GARCH hybrid model to obtain forecasting results with greater accuracy. In this study, the minimum return data on the Indonesian Rupiah (IDR) exchange rate against the Japanese Yen (JPY) shows the results that the ARIMA(0,0,1) model provides RMSE accuracy of 0.008. While the best forecasting model that can be used to forecast the maximum return data of the IDR exchange rate against JPY is ARIMA(1,0,0)-GARCH(1,1) with a small RMSE accuracy of 0.014. The forecasting results for the minimum return data for buying and selling are expected to strengthen the exchange rate. Meanwhile, the forecasting results for the maximum return data for buying and selling are expected to experience exchange rate weakening.