Extreme Dependence Research Articles

A Scoping Review of Bullying and Harassment in Nonprofit and Voluntary Organizations

ABSTRACTApproximately 15% of workers are exposed to bullying in their workplace. However, few studies appear to have been conducted in nonprofit and voluntary organizations. This scoping review explored prevalence and structural and contextual factors that influence bullying in nonprofit and voluntary organizations. From a capture of 671 papers, 54 were assessed for eligibility and 18 full‐text articles were reviewed. Bullying is prevalent in nonprofit and voluntary organizations at the same or higher levels as other sectors, with sexual harassment appearing to be a serious problem in the context of fundraising. A synthesis of papers found a suite of factors presented in four themes; complex and unique power relations, the exploitation of virtuosity, the exercise of hegemonic power, and asymmetrical power and extreme dependency, which together act to stabilize the presence of bullying in nonprofit and voluntary organizations. Such dynamics need to be disrupted for intervention to occur. Further research is needed on how volunteers “sense‐make” in the face of dissonance between organizational mission and behavior, intervention evaluation, and regulating philanthropic donation to reduce donor dependence.

Semiparametric Bayesian modelling of nonstationary joint extremes: How do big tech’s extreme losses behave?

Abstract Motivated by the hype surrounding Artificial Intelligence (AI) and big tech stocks, we develop a model for tracking the dynamics of their combined extreme losses over time. Specifically, we propose a novel Bayesian model for inferring about the intensity of observations in the joint tail over time, and for assessing if two stochastic processes are asymptotically dependent. To model the intensity of observations exceeding a high threshold, we develop a Bayesian nonparametric approach that defines a prior on the space of what we define as Extremal Dependence Intensity functions. In addition, a parametric prior is set on the coefficient of tail dependence. An extensive battery of experiments on simulated data show that the proposed method are able to recover the true targets in a variety of scenarios. An application of the proposed methodology to a set of big tech stocks—known as FAANG (Meta’s Facebook, Apple, Amazon, Netflix and Alphabet’s Google)—sheds light on some interesting features on the dynamics of their combined losses over time.

The Dehumanization of Contemporary Society Through Social Media in “Nosedive” and “Smithereens”

“Nosedive” and “Smithereens”, two different episodes from the British science fiction television series called Black Mirror, focus on contemporary world’s obsession with social media which takes advantage of humans’ insatiable greed for getting attention from others. In these episodes the borderline between the real and the virtual seems so blurred that people do not seem to differentiate one from another. Nosedive and Smithereens vividly explore and analyze the worst nightmares of contemporary world social media can ultimately bring into being. They examine the consequences of extreme dependence on new media technologies by illustrating with characters immersed into the depths of artificial world these new technologies ensure. Drawing a parallel to what French sociologist Jean Baudrillard asserts in his criticism of postmodern era, “boundaries between science fiction and science fact are fast collapsing” , this study aims to explore and deconstruct the crucial threat posed by social media culture aggravated by latest media technologies that play a fundamental role in shaping and constructing contemporary society, which has been remarkably proved by Black Mirror episodes.

Hidden tail chains and recurrence equations for dependence parameters associated with extremes of stationary higher-order Markov chains

Abstract We derive some key extremal features for stationary kth-order Markov chains that can be used to understand how the process moves between an extreme state and the body of the process. The chains are studied given that there is an exceedance of a threshold, as the threshold tends to the upper endpoint of the distribution. Unlike previous studies with $k>1$ , we consider processes where standard limit theory describes each extreme event as a single observation without any information about the transition to and from the body of the distribution. Our work uses different asymptotic theory which results in non-degenerate limit laws for such processes. We study the extremal properties of the initial distribution and the transition probability kernel of the Markov chain under weak assumptions for broad classes of extremal dependence structures that cover both asymptotically dependent and asymptotically independent Markov chains. For chains with $k>1$ , the transition of the chain away from the exceedance involves novel functions of the k previous states, in comparison to just the single value, when $k=1$ . This leads to an increase in the complexity of determining the form of this class of functions, their properties, and the method of their derivation in applications. We find that it is possible to derive an affine normalization, dependent on the threshold excess, such that non-degenerate limiting behaviour of the process, in the neighbourhood of the threshold excess, is assured for all lags. We find that these normalization functions have an attractive structure that has parallels to the Yule–Walker equations. Furthermore, the limiting process is always linear in the innovations. We illustrate the results with the study of kth-order stationary Markov chains with exponential margins based on widely studied families of copula dependence structures.

Novel extensions to the Fisher copula to model flood spatial dependence over North America

Abstract. Taking into account the spatial dependence of floods is essential for an accurate assessment of fluvial flood risk. We propose novel extensions to the Fisher copula to statistically model the spatial structure of observed historical flood record data across North America. These include the machine-learning-based XGBoost model, exploiting the information contained in 130 catchment-specific covariates to predict discharge Kendall's τ coefficients between pairs of gauged–ungauged catchments. A novel conditional simulation strategy is utilized to simulate coherent flooding at all catchments efficiently. After subdividing North America into 14 hydrological regions and 1.8 million catchments, applying our methodology allows us to obtain synthetic flood event sets with spatial dependence, magnitudes, and frequency resembling those of the historical events. The different components of the model are validated using several measures of dependence and extremal dependence to compare the observed and simulated events. The obtained event set is further analyzed and supports the conclusions from a reference paper in flood spatial modeling. We find a nontrivial relationship between the spatial extent of a flood event and its peak magnitude.

Efficient Modeling of Spatial Extremes over Large Geographical Domains

Various natural phenomena exhibit spatial extremal dependence at short spatial distances. However, existing models proposed in the spatial extremes literature often assume that extremal dependence persists across the entire domain. This is a strong limitation when modeling extremes over large geographical domains, and yet it has been mostly overlooked in the literature. We here develop a more realistic Bayesian framework based on a novel Gaussian scale mixture model, with the Gaussian process component defined though a stochastic partial differential equation yielding a sparse precision matrix, and the random scale component modeled as a low-rank Pareto-tailed or Weibull-tailed spatial process determined by compactly-supported basis functions. We show that our proposed model is approximately tail-stationary and that it can capture a wide range of extremal dependence structures. Its inherently sparse probabilistic structure allows fast Bayesian computations in high spatial dimensions based on a customized Markov chain Monte Carlo algorithm prioritizing calibration in the tail. We fit our model to analyze heavy monsoon rainfall data in Bangladesh. Our study shows that our model outperforms natural competitors and that it fits precipitation extremes well. We finally use the fitted model to draw inference on long-term return levels for marginal precipitation and spatial aggregates. Supplementary materials for this article are available online.

Unveiling the impact of oil price shocks on global sukuk markets: a focus on quantile coherence and time-frequency connectedness

ABSTRACT This study examines the relationship between oil price shocks and Islamic bonds in 15 global markets. We focus on how they are connected in terms of time and frequency, specifically in quantiles. Through a quantile coherency approach, we discover that oil price shocks affect sukuk indices differently depending on the type of shock. When we increase the frequency of analysis from once a year to once a month, we observe a significant increase in coherence values of oil risk shocks across the majority of sukuk indices of oil exporting nations in both the lower and middle quantiles. Furthermore, based on quantile cross-spectral coherence analysis, we notice that the extreme quantile dependence between oil price shocks and the majority of the oil exporting nations’ sukuk indices remains stable over a short-time horizon. Our research indicates that demand shocks have a significant impact on the correlation between sukuk markets during uncertain periods, such as COVID-19 and the Russian-Ukraine war. Our findings provide a credible understanding of the behaviour of sukuk indices in response to fluctuations in oil prices, which is important for investment portfolio managers and policy decision-makers.

Spatial Extremal Modelling: A Case Study on the Interplay Between Margins and Dependence

ABSTRACTIt is no secret that statistical modelling often involves making simplifying assumptions when attempting to study complex stochastic phenomena. Spatial modelling of extreme values is no exception, with one of the most common such assumptions being stationarity in the marginal and/or dependence features. If non‐stationarity has been detected in the marginal distributions, it is tempting to try to model this while assuming stationarity in the dependence, without necessarily putting this latter assumption through thorough testing. However, margins and dependence are often intricately connected and the detection of non‐stationarity in one feature might affect the detection of non‐stationarity in the other. This work is an in‐depth case study of this interrelationship, with a particular focus on a spatio‐temporal environmental application exhibiting well‐documented marginal non‐stationarity. Specifically, we compare and contrast four different marginal detrending approaches in terms of our post‐detrending ability to detect temporal non‐stationarity in the spatial extremal dependence structure of a sea surface temperature dataset from the Red Sea.

Influence of platinum content on semiconductor and electrochemical properties of materials based on titanium suboxides

The effect of platinum content on the surface morphology, phase composition and electrochemical properties of materials based on titanium suboxides was investigated. Titanium suboxides are formed at the stage of coating reduction under cathodic polarization, which allows the formation of a porous developed surface for electrodeposition of catalytic platinum layers. The main allotropic modification of TiOx in the studied composite materials is anatase, which contains particles of elemental titanium and platinum. The investigated materials are highly doped semiconductors with a high concentration of charge carriers. With an increase in the platinum content, an extreme dependence of the potential of flat zones is observed, which is associated with the formation of a composite material. The electrocatalytic activity was studied in the reactions of oxygen and hydrogen evolution. The overvoltage of oxygen production on platinum-containing materials is much lower than on the TiOx electrode. The slope of the linear dependence of the potential on the current density decreases with an increase in platinum content. The Tafel slope for the studied materials in the hydrogen evolution reaction was 160 mVdec–1 for coatings containing 0.25 mgcm–2 Pt; whereas it was equal to 42 and 43 mVdec–1 for electrodes with 0.5 and 2 mgcm–2 Pt, respectively.

Convolution Bounds on Quantile Aggregation

Advancing Risk Assessment: New Ways To Compute Quantile Aggregation This issue features a pivotal study on quantile aggregation amid dependence uncertainty, an area critical to finance, risk management, and statistics. The authors introduce “convolution bounds,” derived from a recent inf-convolution formula of quantiles and related risk measures. The obtained analytical tools unify existing results and enhance the understanding of quantile methods by providing general, sharp, and computationally efficient solutions. The results offer insights into the extremal dependence structures, with several implications in risk management and economic analysis applications. For more detailed insights, read the full paper, “Convolution Bounds on Quantile Aggregation” (reference: [insert reference]).

Impact of caring for patients with polyhandicap on institutional health care workers' quality of life: a cross-sectional and longitudinal evaluation.

Profound intellectual multiple disabilities or polyhandicap (PLH) is defined as a combination of profound mental retardation and serious motor deficits resulting in extreme dependence. Support for these patients is multidisciplinary, complex, and time-consuming. Thus, institutional health care workers (HCWs) face specific working conditions: frequent physical tasks, distressed families, and restricted feedback. We aimed to identify determinants of quality of life (QoL) of HCWs and to study longitudinal evolution. The study used data from the French cohort EVAL-PLH. The participants were institutional HCWs of persons with PLH (age ≥ 3 years at the time of inclusion; age at onset of cerebral lesion <3 years old). Two populations were used: (1) cross sectional study: the sample 1 includes the HCWs assessed at T2 (2020-2021); (2) longitudinal study: the sample 2 includes the HCWs assessed at both T1 (2015-2016) and T2 (2020-2021). The data collected included: sociodemographics, health status, professional variables, and psycho-comportemental aspects. QoL was assessed using WHOQOL-BREF which provides 4 scores. In comparison with French norms, the physical and social scores of QoL were significantly lower while the psychological score was significantly higher for (i) the 223 HCWs (participation rate 62%) assessed at T2 and (ii) the 61 HCWs assessed at T1 and T2. The main factors modulating QoL were age, marital status, self-perceived financial difficulties, personal chronic disease, anxiety-mood disorders, nature of coping strategies, and burnout. This study confirms the mixed (negative and positive) impact of caring persons with PLH on the institutional HCWs' QOL. Main determinants of the HCW's QOL were: older age, single status, perceived financial difficulties, altered health status, burn out and coping strategies.Clinical trial registration number: NCT02400528.

Unveiling Extreme Dependencies between Oil Price Shocks and Inflation in Tunisia: Insights from a Copula Approach

We follow a non-linear dynamic correlation approach using a combination of a DCC-GARCH model and a copula model to capture the dependence between oil price changes and inflation in Tunisia. The case of Tunisia is particularly instructive since after having been an exporter and a major producer, it became a net oil importer in the 2000s. The study, based on monthly data spanning decades, selects a Gumbel copula and shows that beyond weak average dependencies, there is a strong correlation between extreme values, suggesting that inflation in Tunisia is more sensitive to extreme (positive) variations in oil prices than to average variations. The implications of these empirical results for economic policy are crucial for the Tunisian economy.Copyright© 2024 The Author(s). This article is distributed under the terms of the license CC-BY 4.0., which permits any further distribution in any medium, provided the original work is properly cited.

A spatio-temporal model for temporal evolution of spatial extremal dependence

Few spatio-temporal models allow temporal non-stationarity. When modeling environmental data recorded over the last decades of the 20th century until now, it seems not reasonable to assume temporal stationarity, since it would not capture climate change effects. In this paper, we propose a space–time max-stable model for modeling some temporal non-stationarity of the spatial extremal dependence. Our model consists of a mixture of max-stable spatial processes, with a rate of mixing depending on time. We use maximum composite likelihood for estimation, model selection, and a non-stationarity test. The assessment of its performance is done through wide simulation experiments. The proposed model is used to investigate how the rainfall in the south of France evolves with time. The results demonstrate that the spatial extremal dependence is significantly non-stationary over time, with a decrease in the strength of dependence.

TAIL DEPENDENCE OF COMMODITY FUTURES RETURNS IN THE AGRICULTURAL AND ENERGY SECTORS

The goal of this research was to examine tail dependence structures between selected commodity futures returns. Tail dependence, called also extremal dependence, was evaluated for the pairs of commodities coming from the same sector (energy or agricultural). The study covers the years 2018-2023, embracing the COVID-19 pandemic and the outbreak of the Russia-Ukraine war. To achieve the goal, bivariate dynamic models were applied to percentage log returns of commodity futures. Marginal distributions were described using the ARMA-GARCH models. Joint distributions were constructed using the symmetrized Joe-Clayton copula, which allowed to model asymmetric dependence in the tails of a distribution. Time variation of the copula parameters, here equal to tail dependence coefficients, was described using the evolution equations [Patton 2006]. In the energy sector, the dependence in both tails of analyzed distributions was relatively strong, dynamic and higher in the lower tail than in the upper tail. On the contrary, the agricultural sector lacks common patterns of tail dependency. This feature of the agricultural sector creates an opportunity for investors or risk managers to build well-diversified portfolios.

Flexible Modeling of Nonstationary Extremal Dependence using Spatially Fused LASSO and Ridge Penalties

Statistical modeling of a nonstationary spatial extremal dependence structure is challenging. Parametric max-stable processes (MSPs) are common choices for modeling spatially indexed block maxima, where an assumption of stationarity is usual to make inference feasible. However, this assumption is unrealistic for data observed over a large or complex domain. We develop a computationally efficient method for estimating extremal dependence using a globally nonstationary but locally stationary MSP construction, with the spatial domain divided into a fine grid of subregions, each with its own dependence parameters. We use LASSO ( L 1 ) or ridge ( L 2 ) penalties to obtain spatially smooth parameter estimates. We then develop a novel data-driven algorithm to merge homogeneous neighboring subregions. The algorithm facilitates model parsimony and interpretability. To make our model suitable for high-dimensional data, we exploit a pairwise likelihood to perform inference and discuss its computational and statistical efficiency. We apply our proposed method to model monthly maximum temperature data at over 1400 sites in Nepal and the surrounding Himalayan and sub-Himalayan regions; we show significant improvements in model fit compared to a stationary model. Furthermore, we demonstrate that the estimated merged partition is interpretable from a geographic perspective and leads to better model diagnostics by adequately reducing the number of parameters.

Extremality of factorizable copulas and implicit dependence copulas

Copulas of continuous random variables satisfying f(X)=g(Y) a.s. for some Borel functions f and g, where f(X) has continuous distribution, are called implicit dependence copulas, of which a special subclass is the class of factorizable copulas, i.e., copulas that can be written as the Markov product of a left invertible copula and a right invertible copula in that order. Implicit dependence copulas were recently shown to coincide with generalized Markov products of left and right invertible copulas, where the joining copulas are arbitrary. In this paper, we obtain characterizations of extreme factorizable copulas and prove that implicit dependence copulas whose joining copula is one of the Fréchet-Hoeffding bounds are extreme. This condition, however, is not necessary, which will be shown via some examples of extreme implicit dependence copulas.

Innovations in Public Health Services: Leveraging Data Science for Kidney Health Assessment

Improvements in Public Health Services use advanced analytical methods to address kidney-related illnesses early through extensive data research and to enhance public health outcomes. The difficulty would be an extreme dependence on data, which could distort physical condition assessment by ignore kidney patient aspect like existence and socioeconomic conditions. To overcome this problem, use the machine learning (ML) approach in the proposed method. Differential Evolution optimization with Upgraded logistic regression (DEO-ULR) utilizedfor the kidney health evaluation. A diagnosis of CKD has comprehensive health information dataset available in open source Kaggle website. The gathered data is preprocessed using Min-Max normalization and employed feature selectedutilizing Recursive Feature Elimination (RFE). The suggested method is also compared the other traditional algorithms, and this study is experiment with in the Python platform. The findings show the suggested technique achieve enhanced performance in accuracy, precision, F1-Score and recall. The study demonstrates the probable of data science in improvingpublic health for kidney health assessment through advanced analytics, resulting in more precise diagnoses, efficient treatment strategies, and earlier detection for patients.

Trades-offs between pollinator attraction and florivore defense maximize reproductive success in the self-incompatible Rivea ornata (Convolvulaceae)

BackgroundRivea ornata, a rare species from the morning glory family, exhibits uncommon characteristics compared to other typical morning glories, including nocturnal flowers that fit the classic moth pollination syndrome. However, the accuracy of its predicted pollination syndrome and its mating system have never been assessed. Additionally, R. ornata flowers attract not only pollinators but also florivores, potentially reducing plant reproductive success. Therefore, this study examined two populations of R. ornata in Thailand and assessed traits related to pollinator attraction and reward, determined its mating system, identified floral visitors and effective pollinators, and investigated the effect of florivory on reproductive success.ResultsRivea ornata is highly fertile but self-incompatible and an obligate outcrosser, rendering it highly dependent on pollinators. Lepidopterans, particularly nocturnal hawk moths, were found to account for a significant proportion of all visits and were the sole effective pollinators of this plant species, in correspondence with its predicted pollination syndrome. Surprisingly, florivory did not significantly reduce reproductive success. This phenomenon may be explained by the strategies employed by R. ornata, which align with the optimal defense hypothesis and functional trade-offs. Specifically, R. ornata appears to invest resources in defending key floral structures while, simultaneously, guard ants are conspicuously absent from flowers, resulting in some florivore damage to non-vital floral organs but ensuring that pollinators are not deterred by ants and thus maintaining high pollinator visitation rates.ConclusionsOur findings indicate that reproduction-related traits in R. ornata, including those involved in pollinator attraction and reward and florivore defense, are highly effective and work in concert to maximize plant reproductive success. Therefore, a main risk that R. ornata faces is the decline or disappearance of hawk moths and other lepidopterans given its extreme specialization and high dependence on pollinators, and conservation efforts should include habitat protection for both R. ornata and its pollinators.

Statistical Inference for Hüsler–Reiss Graphical Models Through Matrix Completions

The severity of multivariate extreme events is driven by the dependence between the largest marginal observations. The Hüsler–Reiss distribution is a versatile model for this extremal dependence, and it is usually parameterized by a variogram matrix. In order to represent conditional independence relations and obtain sparse parameterizations, we introduce the novel Hüsler–Reiss precision matrix. Similarly to the Gaussian case, this matrix appears naturally in density representations of the Hüsler–Reiss Pareto distribution and encodes the extremal graphical structure through its zero pattern. For a given, arbitrary graph we prove the existence and uniqueness of the completion of a partially specified Hüsler–Reiss variogram matrix so that its precision matrix has zeros on non-edges in the graph. Using suitable estimators for the parameters on the edges, our theory provides the first consistent estimator of graph structured Hüsler–Reiss distributions. If the graph is unknown, our method can be combined with recent structure learning algorithms to jointly infer the graph and the corresponding parameter matrix. Based on our methodology, we propose new tools for statistical inference of sparse Hüsler–Reiss models and illustrate them on large flight delay data in the United States, as well as Danube river flow data. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

Improving estimation for asymptotically independent bivariate extremes via global estimators for the angular dependence function

Modelling the extremal dependence of bivariate variables is important in a wide variety of practical applications, including environmental planning, catastrophe modelling and hydrology. The majority of these approaches are based on the framework of bivariate regular variation, and a wide range of literature is available for estimating the dependence structure in this setting. However, such procedures are only applicable to variables exhibiting asymptotic dependence, even though asymptotic independence is often observed in practice. In this paper, we consider the so-called ‘angular dependence function’; this quantity summarises the extremal dependence structure for asymptotically independent variables. Until recently, only pointwise estimators of the angular dependence function have been available. We introduce a range of global estimators and compare them to another recently introduced technique for global estimation through a systematic simulation study, and a case study on river flow data from the north of England, UK.