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In this paper, we present two new classes of fuzzy negations. They are an extension of a well-known class of fuzzy negations, the Sugeno Class. We use it as a base for our work for the first two construction methods. The first method generates rational fuzzy negations, where we use a second-degree polynomial with two parameters. We investigate which of these two conditions must be satisfied to be a fuzzy negation. In the second method, we use an increasing function instead of the parameter δ of the Sugeno class. In this method, using an arbitrary increasing function with specific conditions, fuzzy negations are produced, not just rational ones. Moreover, we compare the equilibrium points of the produced fuzzy negation of the first method and the Sugeno class. We use the equilibrium point to present a novel method which produces strong fuzzy negations by using two decreasing functions which satisfy specific conditions. We also investigate the convexity of the new fuzzy negation. We give some conditions that coefficients of fuzzy negation of the first method must satisfy in order to be convex. We present some examples of the new fuzzy negations, and we use them to generate new non-symmetric fuzzy implications by using well-known production methods of non-symmetric fuzzy implications. We use convex fuzzy negations as decreasing functions to construct an Archimedean copula. Finally, we investigate the quadratic form of the copula and the conditions that the coefficients of the first method and the increasing function of the second method must satisfy in order to generate new copulas of this form. [ABSTRACT FROM AUTHOR]

In this paper, we extend the research on the effect of corporate social responsibility (CSR) on firm risk by analyzing the CSR–idiosyncratic risk nexus and how CSR can be integrated as insurance in a global risk management strategy. First, the causality between CSR and risk was tested. Second, copulas were estimated to strengthen the existing results on the structure of the dependence between the different dimensions of CSR activities and idiosyncratic risk levels. The empirical analysis was conducted on a sample of 254 European-listed firms over the 2018–2020 period. The main results showed a directional causality effect between CSR and idiosyncratic risk, and the dependences were modeled between CSR and realized idiosyncratic risk. This allows a better understanding of the risk implications of CSR for investors, corporate managers, and policy makers. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Dil Araştırmaları is the property of Journal of Dil Arastirmalari and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The comparison of coherent systems in terms of stochastic orders is vital in reliability theory. While there is a considerable amount of literature devoted to comparing systems with homogeneous and independent components, real‐world systems often consist of heterogeneous components. Hence, this article aims to investigate systems with heterogeneous and independent components, as well as, those with heterogeneous and dependent components. For this purpose, we consider systems comprise of three components, which are of two different types of components, namely two components of type A and one component of type B. The system's lifetime distribution is represented using the failure signature when the components are independent, which is a function of the component's life distribution. However, when the components are dependent, the system's lifetime distribution is represented using copula and diagonal sections. Additionally, distorted distributions are utilized to enable distribution‐free stochastic comparisons. Using these representations, we compare systems with components having proportional reversed hazard rates, in three scenarios: (i) when components are heterogeneous and independent; (ii) when components are heterogeneous and dependent; and finally, (iii) comparing systems with homogeneous and independent components with those that have heterogeneous components. To illustrate the applicability of these results, we provide some examples and applications. [ABSTRACT FROM AUTHOR]

Hydrological droughts may be referred to as sustained and regionally extensive water shortages as reflected in streamflows that are noticeable and gauged worldwide. Hydrological droughts are largely analyzed using the truncation level approach to represent the desired flow condition such as the median, mean, or any other flow quantile of an annual, monthly, or weekly flow sequence. The quantification of hydrologic droughts is accomplished through indices, such as the standardized streamflow index (SSI) in tandem with the standardized precipitation index (SPI) commonly used in meteorological droughts. The runs of deficits in the SSI sequence below the truncation level are treated as drought episodes, and thus, the theory of runs forms an essential tool for analysis. The parameters of significance from the modeling perspective of hydrological droughts (or tantamount to streamflow droughts in this paper) are the longest duration and the largest magnitude over a desired return period of T-year (or month or week) of the streamflow sequences. It is to be stressed that the magnitude component of the hydrological drought is of paramount importance for the design and operation of water resource storage systems such as reservoirs. The time scales chosen for the hydrologic drought analysis range from daily to annual, but for most applications, a monthly scale is deemed appropriate. For modeling the aforesaid parameters, several methodologies are in vogue, i.e., the empirical fitting of the historical drought sequences through a known probability density function (pdf), extreme number theorem, Markov chain analysis, log-linear, copulas, entropy-based analyses, and machine learning (ML)-based methods such as artificial neural networks (ANN), wavelet transform (WT), support vector machines (SVM), adaptive neuro-fuzzy inference systems (ANFIS), and hybrid methods involving entropy, copulas, and machine learning-based methods. The forecasting of the hydrologic drought is rigorously conducted through machine learning-based methodologies. However, the traditional stochastic methods such as autoregressive integrated moving average (ARIMA), seasonal autoregressive integrated moving average (SARIMA), copulas, and entropy-based methods are still popular. New techniques for flow simulation are based on copula and entropy-based concepts and machine learning methodologies such as ANN, WT, SVM, etc. The simulated flows could be used for deriving drought parameters in consonance with traditional Monte Carlo methods of data generation. Efforts are underway to use hydrologic drought models for reservoir sizing across rivers. The ML methods whilst combined in the hybrid form hold promise in drought forecasting for better management of existing water resources during the drought periods. Data mining and pre-processing techniques are expected to play a significant role in hydrologic drought modeling and forecasting in future. [ABSTRACT FROM AUTHOR]

We develop statistical methodology for the quantification of risk of source-destination pairs in an internet network. The methodology is developed within the framework of functional data analysis and copula modeling. It is summarized in the form of computational algorithms that use bidirectional source-destination packet counts as input. The usefulness of our approach is evaluated by an application to real internet traffic flows and via a simulation study. [ABSTRACT FROM AUTHOR]

To solve the problems that the correlation of multi-scale coefficients is ignored, as well as inaccurate identification of complementarity and redundancy of source images in traditional infrared and visible image fusion methods, an infrared and visible image fusion method based on quaternion wavelet transform (QWT) and feature-level copula model is proposed in this paper. The proposed method extracts the luminance, contrast and structure features of QWT magnitude and phase subbands. Then, the feature-level copula model capturing the inter-scale and phase-magnitude correlation is constructed to describe the intrinsic structure of images. The redundant and complementary feature type of QWT coefficient is further determined through the similarity of proposed models. According to the feature types, different fusion rules for high-frequency subbands are designed to accurately transfer the salient features of source images into fused image. For the low frequency subbands, a fusion rule is proposed using multiple features to avoid the degradation of image visual quality caused by false information. Finally, the fused low frequency subbands and high frequency subbands are transformed by inverse QWT to get the fused image. Experimental results show that the proposed method can effectively retain the rich details and structure information in infrared and visible images. [ABSTRACT FROM AUTHOR]

In the rapidly evolving business landscape of Thailand, the design and facilities of office buildings play a crucial role in enhancing employee satisfaction and productivity. This study seeks to answer the question: "How can office building facilities be optimized to meet the diverse preferences of occupants in Thailand, thereby improving their satisfaction and productivity"? This study employs a copula-based ordinal regression model combined with machine learning techniques to investigate the determinants of facility preferences in office buildings in Thailand. By analyzing data from 372 office workers in Bangkok, we identify the factors influencing facility needs and preferences, and measure the correlation between these preferences. Our findings reveal that safety and security are the highest-rated amenities, indicating their importance in the workplace. The findings reveal distinct preferences across demographic groups: age negatively influences the demand for certain amenities like lounges, while higher education levels increase the preference for cafeteria services. Employees in smaller firms show a higher preference for lounges and fitness centers but lower for restaurants and cafeterias. Interestingly, the size of the enterprise does not significantly affect preferences for fundamental facilities like security and cleaning. The study also uncovers the significant role of gender and income in shaping preferences for certain facilities. These results suggest that while basic amenities are universally valued, luxury or leisure-oriented facilities are more appreciated in smaller, possibly more community-focused work environments. This study highlights the need for tailored facility management in office buildings, considering the diverse needs of different employee groups, which has significant implications for enhancing workplace satisfaction and productivity. [ABSTRACT FROM AUTHOR]

Private policing is generally used to curb crime in certain neighbourhoods. The impact of an increase in these services on the city as a whole is unclear, as crime could shift from protected to unprotected targets, causing greater social harm. In practice, estimating this effect at the city level encounters at least two problems. First, the simultaneity between the demand for private policing and criminal activity requires appropriate instrumental variables to mitigate endogeneity biases. Second, the dependent and endogenous variables can have an excessive number of zeros at the same time, requiring the use of more specialized econometric models. In this paper, I combine Clotfelter's theoretical model for private policing and crime with Amemiya's econometric model for simultaneously censored outcomes, linking structural and reduced forms and discussing identification issues. Then, a regular Bitobit is modified to estimate reduced forms with many zeros outcomes in a set of copulas to improve censoring specifications with endogeneity. Finally, I conduct an empirical study examining an exclusive database from Brazil to discuss the copula approach, and the hypothesis that more private policing reduces crime at the city level is not rejected. [ABSTRACT FROM AUTHOR]

Recently, the relevation transformation has received further attention from researchers, and some interesting results have been developed. It is well known that the active redundancy at component level results in a more reliable coherent system than that at system level. However, the lack of study of this problem with relevation redundancy prevents us from fully understanding such a generalization of the active redundancy. In this note we deal with relevation redundancy to coherent systems of homogeneous components. Typically, for a series system of independent components, we have proved that the lifetime of a system with relevation redundancy at component level is larger than that with relevation redundancy at system level in the sense of the usual stochastic order and the likelihood ratio order, respectively. For a coherent system with dependent components, we have developed a sufficient condition in terms of the domination function to the usual stochastic order between the system lifetime with redundancy at component level and that at system level. [ABSTRACT FROM AUTHOR]

In the present study, a new methodology has been introduced for predicting the simultaneous removal optimal conditions of Acid red 33 (AR33), Reactive orange 7 (RO7), Acid yellow 3 (AY3), and Malachite green (MG) dyes. Since industrial wastewater contains a variety of dyes, simultaneous prediction of their removal efficiency is a practical issue. The goal of this research was to address challenges associated with modeling this type of removal process. In the first part of this methodology, the c-vine copula function was used to generate synthetic data since the use of most modeling methods requires a large number of data, and studying different experimental conditions is time- and cost-consuming. Besides, Artificial neural network, Adaptive neuro-fuzzy inference system, Least-square support vector machine, and Long-short term memory algorithms were applied for modeling the dyes' removal efficiency using experimental and synthetic data. Utilizing synthetic data significantly improved the accuracy of modeling. For the purpose of finding the optimal amount of dyes' removal efficiency simultaneously, the removal efficiency of each dye is defined as an objective function. This leads to a problem requiring multi-objective optimization. Hence, the Multi-Objective Adaptive Guided Differential Evolution was employed for multi-objective optimization of dyes' removal efficiency. The optimization process was carried out based on the four objective functions including AR33, RO7, AY3, and MG dyes' removal efficiency. The results indicated a direct relationship between dyes' removal efficiency on the Pareto front as well as a highly oscillating and nonlinear relationship with decision variables (pH, NaCl concentration, current, and Time) under optimal conditions. Finally, the average results of Technique for Order of Preference by Similarity to Ideal Solution and VIekriterijumsko KOmpromisno Rangiranje were utilized to select the best optimal conditions from the Pareto front. With pH, NaCl, Time, and current inputs at 6.60, 0.67 (g/L), 96.93 (min), 0.01 (A) (optimal conditions), the best removal efficiencies of 98.88, 37.02, 91.19, and 98.33 (%) were obtained for AR33, RO7, AY3, and MG dyes, respectively. The introduced method demonstrated excellent promise for predicting optimal conditions for dyes' removal efficiency as well as for making wastewater treatment systems easier to design. [Display omitted] • A new methodology for optimization of AR33, RO7, AY3, and MG dyes removal efficiency. • Utilization of copula function in the generation of a large number of synthetic data. • The ANN, ANFIS, LSSVM-GS, and LSTM modeled the removal efficiency of dyes. • MOAGDE has been used for multi-objective optimization. • The use of TOPSIS and VIKOR to select the best solution of optimal Pareto front. [ABSTRACT FROM AUTHOR]

Metaverse (MV) technology introduces new tools for users each day. MV companies have a significant share in the total stock markets today, and their size is increasing. However, MV technologies are questioned as to whether they contribute to environmental pollution with their increasing energy consumption (EC). This study explores complex nonlinear contagion with tail dependence and causality between MV stocks, EC, and environmental pollution proxied with carbon dioxide emissions (CO2) with a decade-long daily dataset covering 18 May 2012–16 March 2023. The Mandelbrot–Wallis and Lo's rescaled range (R/S) tests confirm long-term dependence and fractionality, and the largest Lyapunov exponents, Shannon and Havrda, Charvât, and Tsallis (HCT) entropy tests followed by the Kolmogorov–Sinai (KS) complexity measure confirm chaos, entropy, and complexity. The Brock, Dechert, and Scheinkman (BDS) test of independence test confirms nonlinearity, and White's test of heteroskedasticity of nonlinear forms and Engle's autoregressive conditional heteroskedasticity test confirm heteroskedasticity, in addition to fractionality and chaos. In modeling, the marginal distributions are modeled with Markov-Switching Generalized Autoregressive Conditional Heteroskedasticity Copula (MS-GARCH–Copula) processes with two regimes for low and high volatility and asymmetric tail dependence between MV, EC, and CO2 in all regimes. The findings indicate relatively higher contagion with larger copula parameters in high-volatility regimes. Nonlinear causality is modeled under regime-switching heteroskedasticity, and the results indicate unidirectional causality from MV to EC, from MV to CO2, and from EC to CO2, in addition to bidirectional causality among MV and EC, which amplifies the effects on air pollution. The findings of this paper offer vital insights into the MV, EC, and CO2 nexus under chaos, fractionality, and nonlinearity. Important policy recommendations are generated. [ABSTRACT FROM AUTHOR]

In this paper, we define a multivariate order based on the concept of orthant directional dependence and study some of its properties. The relationships with other dependence orders given in the literature are also studied. We analyze the order between two random vectors in terms of their associated copulas and illustrate our results with several examples. [ABSTRACT FROM AUTHOR]

Under the background of global warming and human activities, drought occurs frequently in the Shaying River Basin (SYRB). It is particularly important for agriculture and water resources management to comprehensively evaluate the evolution of hydrological drought in the river basin. In this study, we used the nonparametric standardized runoff index (NSRI) to investigate the temporal characteristics of hydrological drought in the SYRB from 1956 to 2013. The duration and severity of hydrological drought events were identified based on run theory, and the copula functions with the highest goodness of fit were used to investigate the drought return period. In addition, the double cumulative curve method was used to analyze the main causes of hydrological drought in the SYRB. The results indicated that: (1) From 1957 to 2013, the drought showed an increasing trend in the upper, middle and lower reaches of the SYRB, with frequent alternations of droughts, and the trend characteristic of drought was different in each subzone; (2) the frequency of drought decreased from upstream to downstream, but the duration and severity of drought increased; (3) Frank-copula was considered to be the best fitting two-dimensional copula function in the SYRB and the most severe drought lasted for 25 months, with drought severity of 11.485, and drought return period of 42.14 years; (4) human activities were the main reason for the decrease of runoff in the SYRB and the dominant factor for the intensification of hydrological drought in the basin. [ABSTRACT FROM AUTHOR]

Sea ice conditions in Liaodong Bay of China are often described by sea ice grades, which classify annual sea ice conditions based on the annual maximum sea ice thickness (AM-SIT) and annual maximum floating ice extent (AM-FIE). The joint probability distribution of AM-SIT and AM-FIE was established on the basis of their data pairs from 1949/1950 to 2019/2020 in Liaodong Bay. The joint intensity index of the sea ice condition in the current year is calculated, and the joint classification criteria of the sea ice grades in past years are established on the basis of the joint intensity index series. A comparison of the joint criteria with the 1973 and 2022 criteria revealed that the joint criteria of the sea ice grade match well, and the joint intensity index can be used to quantify the sea ice condition over the years. A time series analysis of the sea ice grades and the joint intensity index sequences based on the joint criteria are then performed. Results show a decreasing trend of the sea ice condition from 1949/1950 to 2019/2020, a mutation in 1990/1991, and a period of approximately 91 years of the sea ice condition. In addition, the Gray-Markov model (GMM) is applied to predict the joint sea ice grade and the joint intensity index of the sea ice condition series in future years, and the error between the results and the actual sea ice condition in 2020/2021 is small. [ABSTRACT FROM AUTHOR]

Since spring discharge, especially in arid and semiarid regions, varies considerably in different months of the year, a time series of spring discharge observations is needed to determine the firm yield of the spring and the amount of water allocated to different needs. Because most springs are in mountainous and inaccessible areas, long-term observational data are often unavailable. This study proposes a probabilistic method based on bivariate analysis to estimate the discharge of the Absefid spring in Iran. This method constructed the bivariate distribution of the outflows of Absefid (AS) and Gerdebisheh (GS) springs using Copula functions. For this purpose, the fit of 11 different univariate distributions to the discharge data of each spring was tested. The results revealed that the GEV and log-normal distributions best fit the discharge data of GS and AS springs, respectively. In addition, among eight different copula functions, the Joe copula function was selected to construct the bivariate distribution of the discharge data of AS and GS springs. With the help of the created bivariate distribution and assuming a certain probability level, it is possible to estimate the discharge of Absefid spring based on the discharge of Gerdebisheh spring in a particular month. The estimated values of the discharge of the Absefid spring in the period from March 1993 to August 2022 show that with a probability of 90%, the lowest discharge of this spring is 600 L per second and occurred in June 2001. Therefore, to allocate the water from this spring for drinking purposes, this discharge value can be considered as the firm yield of this source. However, the amount of allocated water from this source should be determined by considering the ecological needs of the river downstream of this spring. [ABSTRACT FROM AUTHOR]

This study proposes a non-parametric ICA method, called ECOPICA, which describes the joint distribution of data by empirical copulas and measures the dependence between recovery signals by an independent test statistic. We employ the grasshopper algorithm to optimize the proposed objective function. Several acceleration tricks are further designed to enhance the computational efficiency of the proposed algorithm under the parallel computing framework. Our simulation and empirical analysis show that ECOPICA produces better and more robust recovery performances than other well-known ICA approaches for various source distribution shapes, especially when the source distribution is skewed or near-Gaussian. [ABSTRACT FROM AUTHOR]

We use a semiparametric GARCH-in-Mean copula model to examine the volatility dynamics and tail dependence between cryptocurrency markets and financial markets. We do not find any statistically significant tail dependence between the financial and cryptocurrency markets, but we find lower tail dependence between Bitcoin and stock returns. There is lower tail dependence among Bitcoin, Ethereum, and Litecoin, and the lower tail dependence between Ethereum and Litecoin returns is the strongest. The GARCH-in-Mean model shows that the uncertainty effect on cryptocurrency returns is not statistically significant, while uncertainty has a negative and statistically significant effect on Bitcoin returns. The fact that there is no tail dependence between cryptocurrency and the interest rate or the effective exchange rate of U.S. dollar suggests that cryptocurrency could offer safe haven, defined as an asset that is uncorrelated with stocks and bonds. [ABSTRACT FROM AUTHOR]

In low-lying coastal areas, the interplay of various factors including precipitation, river flow, and storm surge can lead to greater influence on floods when they occur simultaneously. The copula method was used in this study to investigate the bivariate flood risk of compounding storm surge and river discharge events in the Pearl River Delta (PRD). Our results indicate that while the correlation between storm surge and flood peak (S-Q) was weak, there was a strong dependence between the pairs of storm surge–flood volume (S-V) and storm surge–flood duration (S-D). For these three pairs, the Clayton copula was the optimal function for S-Q, while the Frank copula was the optimal function for S-V and S-D, respectively. When the flood volume exceeds 2.0 × 104 m3/s and the flood duration is more than 10 days, the bivariate hydrologic risk for S-V and S-D is observed to decrease rapidly. Furthermore, the failure probability (FP) would be underestimated when the combined impact of river flow and storm surge is ignored in coastal flood risk assessment. Such bivariate hydrologic risk analysis implies that when determining design values in coastal flood risk assessment, the combined impact of river flow and storm surge should be taken into account. [ABSTRACT FROM AUTHOR]

Extreme random events will interfere with the inversion analysis of energy and environment systems (EES) and make the planning schemes unreliable. A Copula-based interval cost–benefit stochastic programming (CICS) is proposed to deal with extreme random uncertainties. Taking Yulin city as an example, there are nine constraint-violation scenarios and six coal-reduction scenarios are designed. The results disclose that (i) both system cost and pollutant emission would decrease as the industrial energy supply constraint-violation level increases; (ii) when the primary and secondary energy output increases by 9% and 13%, respectively, and industrial coal supply decreases by 40%, the coal-dependent index of the system would be the lowest, and the corresponding system profitability could reach [29.3, 53.0] %; (iii) compared with the traditional chance-constrained programming, Copula-based stochastic programming can reflect more uncertain information and achieve a higher marginal net present value rate. Overall, the CICS-EES model offers a novel approach to gain insight into the tradeoff between system reliability and profitability. [ABSTRACT FROM AUTHOR]

Bivariate failure data are common in reliability and survival studies, where estimation of dependency is often an important step in data analysis. In the literature, it known that the correlation coefficients measure the linear relationship between two variables, but strong non-linear relationship can also exist between them. Kendall's T concordance coefficient has become a useful tool for bivariate data analysis, which is used in nonparametric tests of independence and as a complementary measures of association. In the analysis of reliability data, there is a phenomenon that occurs when the value of the lifetime is partially known, which is known as censoring. In this paper, two estimation methods of Kendall's t are compared via simulation, one of them assuming normality in marginal distributions and adjusting them individually and the other based on copulas (Gaussian and Clayton), where the bivariate data are interval censored. The comparison is made using the mean squared error and the median absolute deviation. The results show that the method based on the copula approximation generally produces more precise estimates than the method of individual adjustment of the marginals. [ABSTRACT FROM AUTHOR]

In this paper, we study the estimation of a scale parameter from a sample of lifetimes of coherent systems with a fixed structure. We assume that the components are independent and identically distributed having a common distribution which belongs to a scale parameter family. Some results are obtained as well for dependent (exchangeable) components. To this end, we will use the representations for the distribution function of a coherent system based on signatures. We prove that the efficiency of the estimators depends on the structure of the system and on the scale parameter family. In the dependence case, it also depends on the baseline copula function. [ABSTRACT FROM AUTHOR]

Factorial analyses offer a powerful nonparametric means to detect main or interaction effects among multiple treatments. For survival outcomes, for example, from clinical trials, such techniques can be adopted for comparing reasonable quantifications of treatment effects. The key difficulty to solve in survival analysis concerns the proper handling of censoring. So far, all existing factorial analyses for survival data have been developed under the independent censoring assumption, which is too strong for many applications. As a solution, the central aim of this article is to develop new methods for factorial survival analyses under quite general dependent censoring regimes. This will be accomplished by combining existing nonparametric methods for factorial survival analyses with techniques developed for survival copula models. As a result, we will present an appealing F -test that exhibits sound performance in our simulation study. The new methods are illustrated in a real data analysis. We implement the proposed method in an R function surv.factorial (.) in the R package compound.Cox. [ABSTRACT FROM AUTHOR]

Species coexistence attracts wide interest in ecology. Modern coexistence theory (MCT) identifies coexistence mechanisms, one of which, storage effects, hinges on relationships between fluctuations in environmental and competitive pressures. However, such relationships are typically measured using covariance, which does not account for the possibility that environment and competition may be more related to each other when they are strong than when weak, or vice versa. Recent work showed that such 'asymmetric tail associations' (ATAs) are common between ecological variables, and are important for extinction risk, ecosystem stability, and other phenomena. We extend MCT, decomposing storage effects to show the influence of ATAs. Analysis of a simple model and an empirical example using diatoms illustrate that ATA influences can be comparable in magnitude to other mechanisms of coexistence and that ATAs can make the difference between species coexistence and competitive exclusion. ATA influences may be an important new mechanism of coexistence. [ABSTRACT FROM AUTHOR]

In this research, we introduce a truncation of the half-logistic distribution function as a multiplicative Archimedean generator. The corresponding Archimedean copula is obtained, namely the half-logistic family. The dependency structure of this copula is distinct from other well-known ones. Kendall's tau correlation coefficient is obtained in exact form and found to cover the entire positive dependence range (i.e., [0, 1]). We have proven that this copula is positively ordered and has no tail dependencies. The density of this copula is shown to be totally positive of order two. An extension of this copula is also introduced by adding a second parameter. This extension allows for a negative correlation and connects the famous Frank copula to the half-logistic copula. Two datasets were used to compare the half-logistic copula with some other known copula models. [ABSTRACT FROM AUTHOR]

The clustering of time series with geo-referenced data requires a suitable dissimilarity matrix interpreting the comovements of the time series and taking into account the spatial constraints. In this paper, we propose a new way to compute the dissimilarity matrix, merging both types of information, which leverages on the Wasserstein distance. We then make a quasi-Gaussian assumption that yields more convenient formulas in terms of the joint correlation matrix. The method is illustrated in a case study involving climatological data. [ABSTRACT FROM AUTHOR]

The marking on copular verbs in Tibetic languages is regarded as an exemplar of egophoricity, although the extent to which it has been grammaticalized varies between languages. Dzongkha, a southern Tibetic language, is somewhat atypical of the egophoric pattern in the sense that the basic opposition in copulas exhibits a mirative pattern, wherein the non-mirative (egophoric) copula occurs with all grammatical persons in declaratives and interrogatives, and the mirative (non-egophoric) occurs with the 3rd person and rarely with 1st and 2nd persons. The conversational data studied for this paper also show that the speaker need not take knowledge stances that bifurcate the world between objectively 'old' and 'new' knowledge and the attendant associations of knowledge with a particular grammatical person. Rather, the speaker's representation of events is subjective, and dependent, in part, on the knowledge stances between speaker and respondent. What is in view in conversational interaction are the social goals of the conversation — assertions, face-saving strategies, and arriving at mutually shared knowledge — and the Dzongkha copulas are a manipulable linguistic resource in achieving these. [ABSTRACT FROM AUTHOR]

Motivated by three health economics-related case studies, we propose a unifying and flexible regression modeling framework that involves regimeswitching. The proposal can handle the peculiar distributional shapes of the considered outcomes via a vast range of marginal distributions, allows for a wide variety of copula dependence structures and permits to specify all model parameters (including the dependence parameters) as flexible functions of covariate effects. The algorithm is based on a computationally efficient and stable penalized maximum likelihood estimation approach. The proposed modeling framework is employed in three applications in health economics, that use data fromtheMedical Expenditure Panel Survey, where novel patterns are uncovered. The framework has been incorporated in the R package GJRM, hence allowing users to fit the desiredmodel(s) and produce easy-to-interpret numerical and visual summaries. [ABSTRACT FROM AUTHOR]

Given the widespread use of technical analysis and the tight relationship between derivatives and the underlying assets, we employ the copula approach to investigate whether the technical indicators based on underlying assets convey extra information about the future movements of implied volatility (IV) indexes. The empirical results, based on long samples of five well‐known IV indexes, suggest that although the technical indicators are not informative for forecasting the future prices of IV indexes, they can provide extra information about the size of forecasting errors of the IV indexes. The findings are also robust to the impact of COVID‐19. The technical indicators are then used to extend Threshold ARCH and Exponencial GARCH models for improving the estimation of Value at Risks (VaRs). The out‐of‐sample forecast results show that the proposed model outperforms the benchmark in estimating the VaRs. These findings have implications for pricing options of IV indexes and managing the risks of IV‐related portfolios. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Economic Policy Researches / İktisat Politikası Araştırmaları Dergisi is the property of Journal of Economic Policy Researches / Iktisat Politikasi Arastirmalari Dergisi and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copulas are quite a popular, flexible, and useful tool for multivariate modeling in many fields of applications, including finance and insurance, actuarial sciences, biomedical studies, geostatistics, and hydrology. In this paper, we use the concept of diagonal function to build new bivariate copulas based on the well known and important Ali–Mikhail–Haq (AMH), Farlie–Gumbel–Morgenstern (FGM) and Plackett families. We also study some dependence properties of the newly constructed semilinear copulas, namely, the Spearman's ρ coefficient. In particular, we find that the range of this rank correlation coefficient varies between weak and moderate values for the transformed AMH and FGM families, although it varies between moderate and strong values for the transformed Plackett families. Moreover, in all cases, the Spearman's ρ coefficient reaches negative and positive values. Finally, we present a synthetic data generation algorithm, as well as a classical method of estimation of the copula association parameter for each of the six transformed bivariate families. [ABSTRACT FROM AUTHOR]

The tail probability of a function of a multivariate random variable is not easy to estimate by the crude Monte Carlo simulation. When the occurrence of the function value over a threshold is rare, the accurate estimation of the corresponding probability requires a huge number of samples. When the explicit form of the cumulative distribution function of each component of the variable is known, the inverse transform likelihood ratio method is directly applicable scheme to estimate the tail probability efficiently. The method is a type of the importance sampling and its efficiency depends on the selection of the importance sampling distribution. When the cumulative distribution of the multivariate random variable is represented by a copula and its marginal distributions, we develop an iterative algorithm to find the optimal importance sampling distribution, and show the convergence of the algorithm. The performance of the proposed scheme is compared with the crude Monte Carlo simulation numerically. [ABSTRACT FROM AUTHOR]

We analyze the time variation in the average dependence within a set of regional monthly house price index returns in a regime-switching multivariate copula model with a high and a low dependence regime. Using equidependent Gaussian copulas, we show that the dependence of house price returns varies across time with changes in credit market conditions, which reduces the gains from the geographic diversification of real estate and mortgage portfolios. More specifically, we show that a decrease in leverage, measured by the loan-to-value ratio, and to a lesser extent an increase in mortgage rates, are associated with a higher probability of moving to and staying in the high dependence regime. [ABSTRACT FROM AUTHOR]

Hip fracture is a severe complication in the elderly. The affected people are at a higher risk of second fracture and death occurrence, and the best treatment for hip fractures is still being debated. Aside from the treatment, many factors, such as comorbidity conditions, may be associated with second fracture and death occurrence. This study aims to identify effective treatments and important covariates and estimate their effects on the progression of second fracture and death occurrence in hip fracture elderly patients using the semi-competing risks framework, because death dependently censors a second fracture but not vice versa. Due to the complex semi-competing risks data, performing variable selection simultaneously for second fracture and death occurrence is difficult. We propose a penalised semi-parametric copula method for semi-competing risks data. Specifically, we use separate Cox semi-parametric models for both margins and employ a copula to model the two margins' dependence. We develop a coordinate-wise optimisation algorithm that takes into account the data structure and copula function's complexities. Simulations show that the proposed method outperforms the traditional penalised marginal method. We apply the proposed method to a population-based cohort study of hip fracture elderly patients, providing new insights into their treatment and clinical management. [ABSTRACT FROM AUTHOR]

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Study region: China Study focus: Compound drought-hot events (CDH) inflict serious socio-economic damages on our society and the natural environment. Given the inverse relationship between average summer temperature and precipitation, this investigation introduces an innovative empirical copula-based compound drought-hot index (CDHI). This index is crafted from the joint distribution of the standardized precipitation evapotranspiration index (SPEI) and standardized temperature index (STI). While previous research has documented a rising trend in these complex events on both regional and global stages, scrutiny into their escalating severity remains limited. To highlight the critical role of climbing temperatures in the increasing severity of CDH events within China, our research utilizes the CDHI in tandem with a path analysis to precisely assess severity shifts in CDH occurrences during the warm season from 1901 to 2022. New hydrological insights for the region: This study used an empirical copula-based CDH index and implemented the path analysis to quantify the response of severity changes of compound drought-hot events to shifts of summer mean temperature and water deficit from the historical perspective. Our findings reveal a marked escalation in CDH severity across much of China. Path analysis divulges that the influence of rising summer temperatures on CDH severity has seen a significant uptick in the last 60 years (1962–2022), displaying a more considerable contribution rate than the earlier 60-year span (1901–1961). This points to the changing impact of temperature on CDH events over recent decades. During the initial interval (1901–1961), we saw a 0.7% and 1.7% per-decade increase in areas affected by severe and moderate CDH events, respectively. Contrastingly, the subsequent period (1962–2022) experienced a more significant rise, with the area affected by severe and moderate events expanding over twice as much. Totally speaking, this exploration enhances our comprehension of the intensification in CDH event severity and the role of climatic drivers. These insights can contribute to improved risk assessments and the development of tailored adaptation and mitigation strategies in the face of ongoing climate change.

IntroductionStability analysis of earthen slopes is important for the investigation of road embankments, river walls and upstream and downstream slopes of earthen dams. The sloping earthen walls of the river are always subject to instability and failure caused by various factors such as floods, variations of the water level in the river. In investigating the stability of the earthen sloping walls of rivers, the role of hydraulic and geotechnical variables and their correlations are important as factors of creating uncertainty in the analyzes. The aim of current research is to investigate the effect of water level changes on the stability of the soil slopes of the river wall under the conditions of applying the correlation of effective random geotechnical variables with the use of copula functions. Also, comparing the performance of copula functions used in combination with limit equilibrium methods is another goal of this research.MethodologyThe uncertainty and correlation of the random soil properties including the internal friction angle and the cohesion of the river sloping wall were investigated at four cross-sections of Shalmanrood river in Guilan province of Iran. To this end, a computer code was developed in MATLAB and five copula functions were applied to the soil properties and the calculated correlations and distributions were compared using Akaike and Bayesian information criteria to determine the best copula. Then in GeoStudio software, using three limit equilibrium methods including Bishop, Spencer and Morgenstern-Price the slope stability was approximated. The distributions of factor of safety were obtained for three scenarios of water level including the maximum observed level and the water level decline by 20 and 40 %. Results and discussionFor the maximum water level in the river cross-section 1, the distribution of factor of safety was obtained in the range of 1.4-3.6 using the limit equilibrium method. By decreasing the water level by 20%, the factor of safety changes to 1.2-1.9 and declining the water level by 40% , the range is obtained 1.06-2.8. The same trends of decreasing the factor of safety by the water level decline in the river are observed in three other cross-sections. For the maximum river water level in cross-section 1, the maximum error of normal and GEV distributions were obtained equal to 7.8 and 11.4%, respectively. In addition, the error of normal and GEV distributions for water level decline by 20% were13.4% and 13.6% respectively. Finally, the error of 9% and 14.5% were obtained for the water level decline by 40% for the normal and GEV, respectively. It is observed that normal distribution offers better performance for all water levels and the errors of both distributions increase with water level decline. This trend is also observed in the all studied cross-sections.ConclusionsThe performance of the Morgenstern-Price method compared to other limit equilibrium methods is not affected by the variations of the water level in the river and always offers acceptable results. Three limit equilibrium methods used (Bishop, Spencer, and Morgenstern-Price) provide similar results at lower values of the factor of safety (with lower cumulative probability, CDF). Frank copula is the best function to model the correlation of random variables affecting the studied sloping walls of the river. The highest rate of change in factor of safety is observed for the water level decline by 20%.

Setiap perseorangan pasti mempunyai risiko yang ditanggungnya, baik itu risiko yang ditanggung pada masa kini ataupun masa depan. Risiko kematian adalah risiko yang pasti akan terjadi bagi perseorangan. Oleh karena itu, dibutuhkan salah satu jenis perlindungan agar bisa mengatasi risiko kematian. Salah satu jenis perlindungan yang biasa digunakan adalah mengikuti asuransi jiwa. Asuransi jiwa yang umumnya diikuti oleh pasangan suami istri adalah asuransi jiwa joint life. Pemegang polis dari asuransi tersebut umumnya adalah pasangan suami dan istri. Penelitian ini membandingkan estimasi premi asuransi jiwa joint life dengan menggunakan asumsi kebebasan mortalita dan metode copula Archimedean. Data penelitian ini menggunakan data Tabel Mortalitas Badan Penyelenggara Jaminan Sosial (BPJS) tahun 2022 dengan berbagai asumsi, yaitu asumsi usia pasangan suami istri, jangka waktu jenis asuransi, dan suku bunga yang dipakai sebesar 6\% per tahun. Premi yang dihitung menggunakan asumsi risiko kematian tidak saling bebas menggunakan metode copula clayton lebih rendah dibandingkan dengan menggunakan asumsi kebebasan mortalita, copula Frank, dan copula Gumbel. Selain itu, perbedaan umur antara suami dan istri juga mempengaruhi besarnya nilai premi. Semakin besar perbedaan umur dari suami dan istri, maka semakin besar pula nilai premi yang dibayarkan pertahun.

Working with shuffles, we establish a close link between Kendall’s τ\tau , the so-called length measure, and the surface area of bivariate copulas and derive some consequences. While it is well known that Spearman’s ρ\rho of a bivariate copula AA is a rescaled version of the volume of the area under the graph of AA, in this contribution we show that the other famous concordance measure, Kendall’s τ\tau , allows for a simple geometric interpretation as well – it is inextricably linked to the surface area of AA.

The paper deals with Thomas Aquinas’s (1225–1274) theory of predication. Aquinas’s numerous works contain passages devoted to the issue of how predication works, usually in various theological or philosophical contexts. Assuming Aquinas’s account of predication was sufficiently uniform in relation to essential and accidental predications, there are several distinct interpretative models of predication possible in relation to the texts. They differ in ascribing different semantic roles to the copula. The first model sees the copula as expressing inherence of a form expressed by the predicate term in the entity denoted by the subject term. The second model interprets the copula as designating identity. The third model incorporates inherence with the fact that Aquinas combines predicative and existential functions of the copula. I argue that the identity model is closest to what Aquinas has in mind when speaking about predication as opposed to extensional truth conditions.

The probability integral transform of a continuous random variable XX with distribution function FX{F}_{X} is a uniformly distributed random variable U=FX(X)U={F}_{X}\left(X). We define the angular probability integral transform (APIT) as θU=2πU=2πFX(X){\theta }_{U}=2\pi U=2\pi {F}_{X}\left(X), which corresponds to a uniformly distributed angle on the unit circle. For circular (angular) random variables, the sum modulus 2π\pi of absolutely continuous independent circular uniform random variables is a circular uniform random variable, that is, the circular uniform distribution is closed under summation modulus 2π\pi , and it is a stable continuous distribution on the unit circle. If we consider the sum (difference) of the APITs of two random variables, X1{X}_{1} and X2{X}_{2}, and test for the circular uniformity of their sum (difference) modulus 2π\pi , this is equivalent to test of independence of the original variables. In this study, we used a flexible family of nonnegative trigonometric sums (NNTS) circular distributions, which include the uniform circular distribution as a member of the family, to evaluate the power of the proposed independence test by generating samples from NNTS alternative distributions that could be at a closer proximity with respect to the circular uniform null distribution.

The study investigates the nonlinear contagion, tail dependence, and Granger causality relations with TAR-TR-GARCH–copula causality methods for daily Bitcoin, Fintech, energy consumption, and CO2 emissions in addition to examining these series for entropy, long-range dependence, fractionality, complexity, chaos, and nonlinearity with a dataset spanning from 25 June 2012 to 22 June 2024. Empirical results from Shannon, Rényi, and Tsallis entropy measures; Kolmogorov–Sinai complexity; Hurst–Mandelbrot and Lo’s R/S tests; and Phillips’ and Geweke and Porter-Hudak’s fractionality tests confirm the presence of entropy, complexity, fractionality, and long-range dependence. Further, the largest Lyapunov exponents and Hurst exponents confirm chaos across all series. The BDS test confirms nonlinearity, and ARCH-type heteroskedasticity test results support the basis for the use of novel TAR-TR-GARCH–copula causality. The model estimation results indicate moderate to strong levels of positive and asymmetric tail dependence and contagion under distinct regimes. The novel method captures nonlinear causality dynamics from Bitcoin and Fintech to energy consumption and CO2 emissions as well as causality from energy consumption to CO2 emissions and bidirectional feedback between Bitcoin and Fintech. These findings underscore the need to take the chaotic and complex dynamics seriously in policy and decision formulation and the necessity of eco-friendly technologies for Bitcoin and Fintech.

Structural planes are one of the key factors controlling the stability of rock masses. A comprehensive understanding of the spatial distribution characteristics of structural planes is essential for accurately identifying key blocks, analyzing rock mass stability, and addressing various rock engineering challenges. This study compares the effectiveness of four stochastic structural plane generation methods—the Monte Carlo method, the Copula-based method, generative adversarial networks (GAN), and denoised diffusion models (DDPM)—in generating stochastic structural planes and capturing potential correlations between structural plane parameters. The Monte Carlo method employs the mean and variance of three parameters of the measured factual structural planes to generate data that follow the same distributions. The other three methods take the entire set of measured factual structural planes as the overall input to generate structural planes that exhibit the same probability distributions. Five sets of structural planes on four rock slopes in Norway are examined as an example. The validation and analysis were performed using histogram comparison, data feature comparison, scatter plot comparison, and linear regression analysis. The results show that the Monte Carlo method fails to capture the potential correlation between the dip direction and dip angle despite the best fit to the measured factual structural planes. The Copula-based method performs better with smaller datasets, and GAN and DDPM are better at capturing the correlation of measured factual structural planes in the case of large datasets. Therefore, in the case of a limited number of measured structural planes, it is advisable to employ the Copula-based method. In scenarios where the dataset is extensive, the deep generative model is recommended due to its ability to capture complex data structures. The results of this study can be utilized as a valuable point of reference for the accurate generation of stochastic structural planes within rock masses.

The study presents a comparative analysis of climate data under two scenarios: a Gaussian copula marginal regression model for count time series data and a copula-based bivariate count time series model. These models, built after comprehensive simulations, offer adaptable autocorrelation structures considering the daily average temperature and humidity data observed at a regional airport in Mobile, AL.

We propose the “Marshall–Olkin Bivariate Weibull Model with Modified Singularity MOBW-μ”, which focuses on bivariate distributions essential for reliability and survival analyses. Distributions such as the Marshall–Olkin bivariate exponential (MOBE) and the Marshall–Olkin bivariate Weibull (MOBW) are discussed. The MOBW-μ model is introduced, which incorporates a lag parameter μ in the singular part, and probabilistic properties such as the joint survival function, marginal density functions, and the bivariate hazard rate function are explored. In addition, aspects such as the correlation structure and survival copulation are addressed and we show that the correlation of the MOBW-μ is always lower than that of its copula, regardless of the parameters. The latter result implies that the MOBW-μ does not have the Lancaster’s phenomenon that explains that any nonlinear transformation of variables decreases the correlation in absolute value. The article concludes by presenting a robust theoretical framework applicable to various disciplines.

The generation of synthetic data holds significant promise for augmenting limited datasets while avoiding privacy issues, facilitating research, and enhancing machine learning models’ robustness. Generative Adversarial Networks (GANs) stand out as promising tools, employing two neural networks—generator and discriminator—to produce synthetic data that mirrors real data distributions. This study evaluates GAN variants (CTGAN, CopulaGAN), a variational autoencoder, and copulas on diverse real datasets of different complexity encompassing numerical and categorical attributes. The results highlight CTGAN’s sensitivity to training parameters and TVAE’s robustness across datasets. Scalability challenges persist, with GANs demanding substantial computational resources. TVAE stands out for its high utility across all datasets, even for high-dimensional data, though it incurs higher privacy risks, which is indicative of the curse of dimensionality. While no single model universally excels, understanding the trade-offs and leveraging model strengths can significantly enhance synthetic data generation (SDG). Future research should focus on adaptive learning mechanisms, scalability enhancements, and standardized evaluation metrics to advance SDG methods effectively. Addressing these challenges will foster broader adoption and application of synthetic data.

For multivariate non-Gaussian involving copulas, likelihood inference is dominated by the data in the middle, and fitted models might not be very good for joint tail inference, such as assessing the strength of tail dependence. When preliminary data and likelihood analysis suggest asymmetric tail dependence, a method is proposed to improve extreme value inferences based on the joint lower and upper tails. A prior that uses previous information on tail dependence can be used in combination with the likelihood. With the combination of the prior and the likelihood (which in practice has some degree of misspecification) to obtain a tilted log-likelihood, inferences with suitably transformed parameters can be based on Bayesian computing methods or with numerical optimization of the tilted log-likelihood to obtain the posterior mode and Hessian at this mode.