Asymmetric Data Research Articles

Discrete Joint Random Variables in Fréchet-Weibull Distribution: A Comprehensive Mathematical Framework with Simulations, Goodness-of-Fit Analysis, and Informed Decision-Making

This paper introduces a novel four-parameter discrete bivariate distribution, termed the bivariate discretized Fréchet–Weibull distribution (BDFWD), with marginals derived from the discretized Fréchet–Weibull distribution. Several statistical and reliability properties are thoroughly examined, including the joint cumulative distribution function, joint probability mass function, joint survival function, bivariate hazard rate function, and bivariate reversed hazard rate function, all presented in straightforward forms. Additionally, properties such as moments and their related concepts, the stress–strength model, total positivity of order 2, positive quadrant dependence, and the median are examined. The BDFWD is capable of modeling asymmetric dispersion data across various forms of hazard rate shapes and kurtosis. Following the introduction of the mathematical and statistical frameworks of the BDFWD, the maximum likelihood estimation approach is employed to estimate the model parameters. A simulation study is also conducted to investigate the behavior of the generated estimators. To demonstrate the capability and flexibility of the BDFWD, three distinct datasets are analyzed from various fields, including football score records, recurrence times to infection for kidney dialysis patients, and student marks from two internal examination statistical papers. The study confirms that the BDFWD outperforms competitive distributions in terms of efficiency across various discrete data applications.

A New Multimodal Modification of the Skew Family of Distributions: Properties and Applications to Medical and Environmental Data

The skew distribution has the characteristic of appropriately modeling asymmetric unimodal data. However, in practice, there are several cases in which the data present more than one mode. In the literature, it is possible to find a large number of authors who have studied extensions based on the skew distribution to model this type of data. In this article, a new family is introduced, consisting of a multimodal modification to the family of skew distributions. Using the methodology of the weighted version of a function, we perform the product of the density function of a family of skew distributions with a polynomial of degree 4, thus obtaining a more flexible model that allows modeling data sets, whose distribution contains at most three modes. The density function, some properties, moments, skewness coefficients, and kurtosis of this new family are presented. This study focuses on the particular cases of skew-normal and Laplace distributions, although it can be applied to any other distribution. A simulation study was carried out, to study the behavior of the model parameter estimates. Illustrations with real data, referring to medicine and environmental data, show the practical performance of the proposed model in the two particular cases presented.

A Statistical Framework for a New two-parameter Unit Bilal Distribution with Application to Model Asymmetric Data

In the realm of statistical mechanics, unit models are commonly employed to elucidate tangible numerical values that fall within the range of 0 to 1, encompassing entities like proportions, percentages, and probabilities. Unit models are frequently created by adapting an existing model originally defined in a broader domain, while others are explicitly formulated independently. This study aims to develop a novel unit interval lifespan model with a small number of parameters that is capable of describing more complex failure patterns, such as growing and bathtub-shaped failure rates. The unit Bilal (UB) model, based on the inverse-exponential technique and the Bilal model, is explained in this research study. Different statistical features and aspects of reliability are investigated. The results have both theoretical and practical implications. We derive explicit expressions for primary functions from the UB distribution from a theoretical perspective. To evaluate parameter, estimate approaches, we demonstrate various numerical and graphical simulation findings. The applicability of the model to real-world data is also discussed.

Improving class probability estimates in asymmetric health data classification: An experimental comparison of novel calibration methods

In the context of health data classification, imbalanced and asymmetric class distributions can significantly impact the performance of machine learning models. One critical aspect affected by these issues is the reliability of class probability estimates, which are crucial for informed decision-making in healthcare applications. Instead of predicting class values directly for a classification problem, it can be more convenient to predict the probability of an observation belonging to each possible class. This research aims to address the challenges posed by imbalanced and asymmetric responses in health data classification by evaluating the effectiveness of recent calibration methods in improving class probability estimates. We propose Beta calibration techniques and the Stratified Brier score and Jaccard's Score as novel calibration methods and evaluation metrics respectively. The experimental comparison involves implementing and assessing various calibration techniques to determine their impact on model performance and calibration accuracy of simulated and healthcare datasets with varying imbalance ratios. Our results show that the Beta calibration method consistently improved the classifiers' predictive ability. The findings of this study provide valuable insights into selecting the most suitable calibration method for enhancing class probability estimates in healthcare-related machine learning tasks.

Asymmetry index for data and its verification in dimensionality reduction and data visualization

We propose an asymmetry index as a measure of degree of asymmetry of a given dataset. It provides an additional information on a dataset allowing to guide and improve any further analysis. The index reflects the intensity of the asymmetric relationships among data resulting from hierarchical data structure. Using the information retrieved by our asymmetry index, one obtains a justification and explanation of the effectiveness of the subsequent asymmetric data analysis methods, as well as helpful preparation to asymmetrizing the tools for the further analysis. The asymmetry index is based on the k-nearest neighbors graph representing the considered data. Therefore, it uses the intrinsic geometry-based information on the data, in this way, providing an insight into the data structure. Our experiments on real data are designed to verify the usefulness of the asymmetry index and the correctness of its theoretical fundamentals. In our empirical validation, we employ the symmetric and asymmetric dimensionality reduction algorithms and evaluate their results on the basis of clustering in the 2-dimensional visualization space. We test, whether our index indeed predicts the level of superiority of the asymmetric methods over their symmetric counterparts.

Spatio-temporal analysis and prediction of land use landcover (LULC) change in Wular Lake, Jammu and Kashmir, India.

Landsat land use/land cover (LULC) data analysis to establish freshwater lakes' temporal and spatial distribution can provide a solid foundation for future ecological and environmental policy development to manage ecosystems better. Analysis of changes in LULC is a method that can be used to learn more about direct and indirect human interactions with the environment for sustainability. Neural network technology significantly facilitates mapping between asymmetric and high-dimensional data. This paper presents a methodological advancement that integrates the CA-ANN (cellular automata-artificial neural network) technique with the dynamic characteristics of the water body to forecast forthcoming water levels and their spatial distribution in "Wular Lake." We used remote sensing data from 2001 to 2021 with a 10-year interval to predict spatio-temporal change and LULC simulation. The validation of the calibration of predicted and accurate LULC maps for 2021 yielded a maximum kappa value of 0.86. Over the past three decades, the study region has seen an increase in a net change % in the impervious surface of 22.41% and in agricultural land by 52.02%, while water decreased by 14.12%, trees/forests decreased by 40.77%, shrubs decreased by 11.53%, and aquatic vegetation decreased by 4.14%. Multiple environmental challenges have arisen in the environmentally sustainable Wular Lake in the Kashmir Valley due to the vast land transformation, primarily due to human activities, and have been predominantly negative. The research acknowledges the importance of (LULC) analysis, recognizing it as a fundamental cornerstone for developing future ecological and environmental policy frameworks.

Variance Estimation under Some Transformation for Both Symmetric and Asymmetric Data

This article suggests an improved class of efficient estimators that use various transformations to estimate the finite population variance of the study variable. These estimators are particularly helpful in situations where we know about the minimum and maximum values of the auxiliary variable, and the ranks of the auxiliary variable are associated with the study variable. Consequently, these rankings can be applied as an effective tool to improve the accuracy of the estimator. A first-order approximation is used to investigate the properties of the proposed class of estimators, such as bias and mean squared error (MSE) under simple random sampling. A simulation study carried out in order to measure the performance and verify the theoretical results. The suggested class of estimators has a greater percent relative efficiency (PRE) than the other existing estimators in all of the simulated situations, according to the results. Three symmetric and asymmetric datasets are examined in the application section in order to show the superior performance of the proposed class of estimators over the existing estimators.

Amplification technology for spatial division multiplexing signals transmitted using multicore fibres

AbstractAmplification technologies needed for spatial division multiplexing (SDM) signal transmission have been reported, especially focusing on submarine systems, which have stricter requirements than terrestrial ones. As the first step of a large‐scale commercialisation, 2‐core un‐coupled (UC) fibre production has already been announced. However, optical amplifiers for SDM signals are still under investigation because SDM amplifiers have various architectural possibilities, depending on the requirements to be considered in designing systems. The multicore erbium‐doped fibre amplifier (MC‐EDFA) architecture is classified from the viewpoint of the number of erbium‐doped fibre (EDF) cores and its pumping method. Core pumped MC‐EDFA is more compatible with the conventional single‐core fibre (SCF)‐ based system and more mature than cladding pumped MC‐EDFA. However, cladding pumped MC‐EDFA has a unique feature of collective amplification of all cores in a single multicore fibre (MCF) and the potential of large‐scale integration of amplification cores in a single package. For a feasibility study, a C‐band 19‐core cladding pumped MC‐EDFA prototype is fabricated using a 19‐core isolator. A half volume of the equivalent conventional single‐core (SC‐) EDFA is successfully demonstrated. One unique feature of UC‐MCF is bidirectional (BD‐) transmission using a single UC‐MCF. Signal transmission direction can differ from core to core. This feature is useful for efficiently accommodating traffic of asymmetric communication data and expanding transmission capacity at a constant transmission distance. A C‐band 7‐core cladding pumped BD‐MC‐EDFA prototype is fabricated for the bidirectional MCF transmission line. Experimental results show transmission capacity was expanded by 17% using the prototype when bidirectional signal assignment was adapted. Towards practical use, optical components suitable for MC‐EDFA need to be developed to draw out potential advantages of SDM technologies.

Inference and diagnostics for censored linear regression model with skewed generalized t distribution

In recent years, some interested data can be recorded only if the values fall within an interval range, and the responses are often subject to censoring. Attempting to perform effective statistical analysis with censored, especially heavy-tailed and asymmetric data, can be difficult. In this paper, we develop a novel linear regression model based on the proposed skewed generalized t distribution for censored data. The likelihood-based inference and diagnostic analysis are established using the Expectation/Conditional Maximization Either algorithm in conjunction with smoothing approximate functions. We derive relevant measures to perform global influence for this novel model and develop local influence analysis based on the conditional expectation of the complete-data log-likelihood function. Some useful perturbation schemes are discussed. We illustrate the finite sample performance and the robustness of the proposed method by simulation studies. The proposed model is compared with other procedures based on a real dataset, and a sensitivity analysis is also conducted.

Effect of dynamic exclusion and the use of FAIMS, DIA and MALDI-mass spectrometry imaging with ion mobility on amyloid protein identification

Amyloidosis is a disease characterized by local and systemic extracellular deposition of amyloid protein fibrils where its excessive accumulation in tissues and resistance to degradation can lead to organ failure. Diagnosis is challenging because of approximately 36 different amyloid protein subtypes. Imaging methods like immunohistochemistry and the use of Congo red staining of amyloid proteins for laser capture microdissection combined with liquid chromatography tandem mass spectrometry (LMD/LC–MS/MS) are two diagnostic methods currently used depending on the expertise of the pathology laboratory. Here, we demonstrate a streamlined in situ amyloid peptide spatial mapping by Matrix Assisted Laser Desorption Ionization–Mass Spectrometry Imaging (MALDI-MSI) combined with Trapped Ion Mobility Spectrometry for potential transthyretin (ATTR) amyloidosis subtyping. While we utilized the standard LMD/LC–MS/MS workflow for amyloid subtyping of 31 specimens from different organs, we also evaluated the potential introduction in the MS workflow variations in data acquisition parameters like dynamic exclusion, or testing Data Dependent Acquisition combined with High-Field Asymmetric Waveform Ion Mobility Spectrometry (DDA FAIMS) versus Data Independent Acquisition (DIA) for enhanced amyloid protein identification at shorter acquisition times. We also demonstrate the use of Mascot’s Error Tolerant Search and PEAKS de novo sequencing for the sequence variant analysis of amyloidosis specimens.

System identification based on the cross Gramian

In order to make mechanical structures more reliable, research is being carried out into methods of detecting and localizing damage to structures, known as Structural Health Monitoring (SHM). One method for continuously monitoring a structure is called State Projection Estimation Error (SP2E) and was developed at the University of Applied Sciences Leipzig. This method is based on state space systems, which are generated by output-only system identification. It is state-of-the-art to create a stabilization diagram to choose the best model order. Since the monitoring process is continuous, a state space model is created every 10 minutes, and therefore it is not possible to look at a stabilization diagram. Thus, the system identification process has to be automated. A novel approach developed in the SPP 100+ research program takes place via the theory of the cross Gramian. The cross Gramian is defined by the Hankel operator, which maps the past input to the future output. This Gramian indicates the interaction between the states and the input/output of a system and can be seen as the energy capacity of a system. By comparing the power of the measurement with the identified energy capacity, the model order selection can be automated. There are limits to the existence of the cross Gramian for multiple-input, multiple-output (MIMO) systems. The system has to be square, stable and symmetric. The covariance driven system identification never provides a symmetric system. A way to create a symmetric system out of the asymmetric measurement data is presented by the use of a symmetrizer, such that the cross Gramian can always be computed. With this symmetrizer a method to estimate the system matrix A by a stable Lyapunov equation, which is classically computed by a numerically unstable pseudo inverse, is presented.

A novel non-parametric statistical method in reliability theory: Mathematical characterization and analysis of asymmetric data in the fields of biological sciences and engineering

In recent decades, strategies devised to address challenges in life testing have noticeably veered away from those designed for related yet more intricate issues. The increasing complexity of data generated daily across practical fields has given rise to a novel realm in reliability that includes life classes and specific probability distributions. This study serves as an illustration of the effectiveness of integrating the goodness-of-fit technique into life testing quandaries, resulting in more efficient processes comparable to or surpassing traditional methods. Furthermore, these techniques exhibit potential for enhanced performance, particularly when dealing with smaller sample sizes. The comparison between the Exponential Better than Used in Increasing Convex in Laplace Transform Order (EBUCL) test statistic and a distinct goodness-of-fit test statistic upholds an approach leaning towards normalcy. Analysis encompassing powers, Pitman asymptotic effectiveness, and critical points, as well as the exploration of methodologies for handling suppressed data, has been undertaken. Additionally, a simulation study has been conducted to examine the study's motivations across various sample sizes. The results derived from our experiments hold practical implications for both bioscience and engineering sustainability data analyses.

The Universal Periodic Review and the Ban on Intersex Genital Mutilation in an African Context

The Universal Periodic Review (UPR) assesses the human rights records of all 193 UN Member States against the benchmark of the Universal Declaration of Human Rights and its core human rights treaties. To date, more than 100,000 recommendations have been provided to states under review (SUR) from peer Member States. Less than 1% address the rights of intersex persons. Western countries issue most of these cases, followed by the Latin American and Caribbean countries. African and Asian countries formulate a negligible number. This asymmetric data might mistakenly support the assumption that Western countries care more about the rights of intersex persons than non-Western countries. However, the recent groundbreaking Resolution on the Promotion and Protection of the Rights of Intersex Persons in Africa calls upon its states’ parties to stop nonconsensual genital normalisation practices on intersex persons and considers these practices as mutilation. Intersex genital mutilation (IGM) stands as a profound human rights infringement experienced by intersex individuals, who undergo medical interventions often performed on their healthy bodies. The primary objective of such interventions is to enforce conformity to prevailing medical and sociocultural norms pertaining to binary genders. I argue that Member States formulating recommendations advocating for the ban on IGM should consider contextualised factors, especially with regards to “informed consent”. This approach aims to enhance the persuasiveness of recommendations and increase the likelihood of their acceptance by SUR. Through the analysis of twenty-nine IGM-related UPR recommendations, this article addresses the effectiveness of the UPR in discussing intersex rights and the ban on IGM, with a focus on Africa.

Quantile-based dynamic modeling of asymmetric data: a novel Burr XII approach for positive continuous random variables

Quantile-based dynamic modeling of asymmetric data: a novel Burr XII approach for positive continuous random variables

Calibrated Empirical Neutrosophic Cumulative Distribution Function Estimation for Both Symmetric and Asymmetric Data

The traditional stratification weight is widely used in survey sampling for estimation under stratified random sampling (StRS). A neutrosophic calibration approach is proposed under neutrosophic statistics for the first time with the aim of improving conventional stratification weight. This addresses the challenge of estimating the empirical cumulative distribution function (CDF) of a finite population using the neutrosophic technique. The neutrosophic technique extends traditional statistics, dealing with indeterminate, vague, and uncertain values. Thus, using additional information, we are able to obtain an effective estimate of the neutrosophic CDF. The suggested estimator yields an interval range in which the population empirical CDF is likely to exist rather than a single numerical value. The proposed family of neutrosophic estimators will be defined under suitable calibration constraints. A simulation study is also computed in order to assess the effectiveness of the suggested and adapted neutrosophic estimators using real-life symmetric and asymmetric datasets.

Influence of fintech, natural resources, and energy transition on environmental degradation of BRICS countries: Moderating role of human capital

The rapid pace of economic development experienced by the BRICS countries is accompanied by increased carbon (CO2) emissions resulting from the excessive use of energy and natural resources. However, the emergence of recent technologies has led to the offering of efficient production methods with improved financing channels. This study analyzes the role of financial technologies, digitalization, natural resources, energy transition, and energy innovations on CO2 emissions. It also tests the moderating effect of human capital in BRICS countries from 1995 to 2021. Initially, Augmented Mean Group (AMG) estimation approach is applied to conduct empirical analysis. Due to asymmetric data distribution and non-linear relationships among variables, the Method of Moments Quantile Regression (MMQR) approach is also applied to estimate the effect of the variables across different quantiles of CO2 emission. The findings indicate that digitalization, financial technologies, energy transition, energy innovations, and natural resources reduce CO2 emission, whereas human capital increases it. However, the moderating effect of human capital on the association between digitalization, energy transition, and CO2 emission is significant and positive, indicating that human capital strengthens the negative impact of these factors on environmental degradation. Human capital does not significantly moderate the relationship of natural resources, financial technologies, and renewable energy innovations with CO2 emissions.

Dual-type quantile regression approach for mean estimation incorporating sampled and non-sampled data

Drawing inspiration from recent advancements in robust mean estimation within finite sampling theory, we introduce a novel dual-type class of mean estimators in a design-based framework. The dual-type class is based on quantile regression and is specifically designed to be effective in the presence of extreme observations. Significantly, it integrates the averages of both sampled observations and non-sampled observations of auxiliary variable. In the initial discussion of this class, it is presumed that the target variable is non-sensitive, signifying its relevance to subjects that respondents do not consider embarrassing when queried directly. In this standard setting, we present specific estimators within the class and determine their theoretical properties. The class's scope broadens to include scenarios where the target variable incorporates sensitive topics, giving rise to nonresponse rates and inaccurate reporting. To alleviate these errors, one can promote respondent cooperation by employing scrambled response methods that obscure the actual value of the sensitive variable. Accordingly, the article delves into discussions on additive methods. Subsequently, a numerical study is conducted using asymmetric data to evaluate the effectiveness of the dual-type class by comparing it with several existing estimators, both in the absence and presence of scrambled responses.

MaP-SGAN: Multi-anchor point siamese GAN for Wi-Fi CSI-based cross-domain gait recognition

With the pervasive growth of IoT, Wi-Fi CSI-based human gait recognition faces the challenge of maintaining model robustness in new environments. Current cross-domain methods often rely on symmetric data, restricting their practical utility. To overcome this limitation, we propose MaP-SGAN, an innovative model specifically designed to enhance gait recognition’s robustness in cross-domain scenarios. MaP-SGAN integrates three crucial components: (1) the Consistency Maintenance Module, utilizing a Siamese network to ensure latent vector consistency within the asymmetric data domain; (2) the Diversity Enhancement Module, incorporating static environmental information to diversify the generator and improve the alignment of generated samples with the target domain’s characteristics; (3) the Multi-anchor Point Metrics in the discriminator, employing multi-anchor point metrics to constrain the similarity between generated data and target domain features. This metric approach comprehensively addresses the distributional differences between the source and target domains, thereby enhancing the model’s discriminative capability and generalization performance. Through extensive testing in diverse scenarios involving different clothing and environments, our proposed MaP-SGAN model achieves an average accuracy of 87.58% in cross-clothing recognition, which is an improvement of 4.03% compared to existing state-of-the-art models. The average accuracy in cross-environment recognition is 53.8%, which is an improvement of about 6.34% compared to existing state-of-the-art models.

Exploring the Nexus of Perceived Organizational CSR Engagement, Job Satisfaction, Organizational Pride, and Involvement in CSR Activities: Evidence from an Emerging Economy

Corporate Social Responsibility (CSR) is becoming omnipresent in contemporary business environments given the growing awareness of the social role of business entities and their contribution to sustainable development. The research was conducted in order to explore the relationships between the perceived organizational engagement in CSR, job satisfaction due to CSR, organizational pride, and employees’ involvement in the company’s CSR activities. The research was conducted on a sample of employees in the life insurance department of a leading insurance company in Serbia, using a structured web questionnaire. Data analysis was performed on a sample of 138 respondents. Data were processed using Partial Least Squares Structural Equation Modeling (PLS-SEM), given the relatively small size of the sample, asymmetric data distribution, and the complexity of the relations. The study results point to a more influential role of CSR engagement in fostering organizational pride, compared to job satisfaction, with the latter as the most influential determinant of organizational involvement.

An Improved Slack Based Measure Model for Evaluating Green Innovation Efficiency Based on Asymmetric Data

Nowadays, one of the main challenges facing green innovation management is how to enhance the performance of innovation processes by utilizing asymmetric input and output data. Therefore, this paper develops an improved SBM model analysis framework for evaluating the green innovation efficiency of asymmetric input and output data. The framework is applied to assess the technical (TE), managerial (PTE), and scale (SE) efficiencies of new energy companies under three input variables (R&D personnel input, R&D capital input, and comprehensive energy consumption input), two desirable output variables (green technology output and economic output), and one undesirable output variable (greenhouse gas emissions). Then, environmental factors and random factors are eliminated from the obtained input slack variables based on the SFA model, placing decision-making units in a homogeneous environment. The results demonstrate that TE, PTE, and SE are improved after eliminating environmental factors and random factors. Subsequently, based on the entropy method, this paper classifies companies’ green innovation patterns into four categories and provides targeted solutions. The purpose of this paper is to provide an evaluation method for new energy companies to understand green innovation efficiency and assist decision makers in identifying the most optimal resource allocation approach. The proposed improved SBM model contributes to the literature and to industry practice by (1) providing a reliable evaluation of green innovation efficiency under asymmetric input and output data; (2) determining effective improvement actions based on a slack analysis of environmental variables and random variables that lead to improved process performance; and (3) making fuzzy innovation performance efficient to facilitate understanding and managing innovation resource allocation quality.