Search Results (18)

Many musicologists and researchers of popular music have recently stressed the omnipresence of covers in today’s music industry. In the sociolinguistics of music, however, studio-recorded covers and their potential differences from ‘original’ compositions have certainly been acknowledged in passing, but very few sociolinguists concerned with the study of song seem to have systematically explored how language use may differ in such re-imagined musical outputs. This article reports on a study which examines the language use of 45 blues artists from three distinct time periods (viz., 1960s, 1980s, and 2010s) and three specific social groups (viz., African American; non-African American, US-based; and non-African American, non-US based) distributed over 270 studio-recorded original and cover performances. Through gradient boosting decision tree classification, it aims to analyze the artists’ use of eight phonological and lexico-grammatical features that are traditionally associated with African American English (viz., /aɪ/ monophthongization, post-consonantal word-final /t/ deletion, post-consonantal word-final /d/ deletion, alveolar nasal /n/ in <ing> ultimas, post-vocalic word-final /r/ deletion, copula deletion, third-person singular <s> deletion, and not-contraction). Our analysis finds song type (i.e., the distinction between covers and originals) to have no meaningful impact on artists’ use of the examined features of African American English. Instead, our analysis reveals how performers seem to rely on these features to a great extent and do so markedly consistently, regardless of factors such as time period, socio-cultural background, or song type. This paper hence builds on our previous work on the language use of blues performers by further teasing out the complex indexical and iconic relationships between features of African American English, authenticity, and the blues genre in its various manifestations of time, place, and performance types. Full article

While using sap-flow sensor measurements is a well-established technique for quantifying leaf water transpiration in tree species, installing and maintaining a large number of sensors and data loggers in large-scale plantations to obtain accurate measurements is both costly and time-consuming. We developed a copula-based approach to predict sap flows based on readily available vapor pressure deficits (VPDs) and found that the Normal copula was the best among five commonly used copulas. The Normal-copula approach was validated using our field-measured eastern cottonwood (Populus deltoides (Bartr. ex Marsh.)) sap flow data, yielding solid statistical measures, including Mann–Kendall’s τ = 0.59, R² = 0.81, and p-value < 0.01. The approach was applied to predict sap flows of eastern cottonwood during the growing period from 1 March to 31 October 2015 as well as the 5-year growing period from 2019 to 2023. It successfully replicated the characteristic diurnal sap flow pattern, with rates increasing during the day and decreasing at night, as well as the typical seasonal pattern, with rates rising from winter to summer and decreasing from summer to next winter. Our study suggests that the copula-based approach is a reliable tool for estimating sap flows based on VPD data. Full article

In forestry, growth functions form the basis of research and are widely used for the mathematical modeling of stand variables, e.g., tree or stand basal area, stand height, stand volume, site index, and many more. In this study, to estimate five-dimensional dependencies between tree diameter at breast height, potentially available area, height, crown area and crown base height, we used a normal copula approach whereby the growths of individual variables are described using a stochastic differential equation with mixed-effect parameters. The normal copula combines the marginal distributions of tree diameter at breast height, potentially available area, height, crown area, and crown base height into a joint multivariate probability distribution. Copula models have the advantage of being able to use collected longitudinal, multivariate, and discrete data for which the number of measurements of individual variables does not match. This study introduced a normalized multivariate interaction information measure based on differential entropy to assess the causality between tree size variables. In order to accurately and quantitatively assess the stochastic processes of the tree size variables’ growth and to provide a scientific basis for the formalization of models, an analysis method of the synergetic theory of information entropy has been proposed. Theoretical findings are illustrated using an uneven-aged, mixed-species empirical dataset of permanent experimental plots in Lithuania. Full article

Stochastic differential equations and Copula theories are important topics that have many advantages for applications in almost every discipline. Many studies in forestry collect longitudinal, multi-dimensional, and discrete data for which the amount of measurement of individual variables does not match. For example, during sampling experiments, the diameters of all trees, the heights of approximately 10% of the trees, and the tree crown base height and crown width for a significantly smaller number of trees are measured. In this study, for estimating five-dimensional dependencies, we used a normal copula approach, where the dynamics of individual tree variables (diameter, potentially available area, height, crown base height, and crown width) are described by a stochastic differential equation with mixed-effect parameters. The approximate maximum likelihood method was used to obtain parameter estimates of the presented stochastic differential equations, and the normal copula dependence parameters were estimated using the pseudo-maximum likelihood method. This study introduced the normalized multi-dimensional interaction information index based on differential entropy to capture dependencies between state variables. Using conditional copula-type probability density functions, the exact form equations defining the links among the diameter, potentially available area, height, crown base height, and crown width were derived. All results were implemented in the symbolic algebra system MAPLE. Full article

This paper introduces a multivariate extension of Raftery copula. The proposed copula is exchangeable and expressed in terms of order statistics. Several properties of this copula are established. In particular, the multivariate Kendall’s tau and Spearman’s rho, as well as the density function, of the suggested copula are derived. The lower and upper tail dependence of the proposed copula are also established. The dependence parameter estimator of this new copula is examined based on the maximum likelihood procedure. A simulation study shows a satisfactory performance of the presented estimator. Finally, the proposed copula is successfully applied to a real data set on black cherry trees. Full article

For the monitoring and management of forest resources, the main index is the stand volume, which is determined on the basis of the tree diameter, height, and number of trees per hectare of three-dimensional distribution. The development of trees in the forest stand is dynamic and is driven by random phenomena. In this study, the tree diameter, the potentially available area, and the height are described by the mixed-effect parameters of the Gompertz-type diffusion process. A normal copula function is used to connect a three-dimensional distribution to its one-dimensional margins. The newly developed model was illustrated using empirical data from 53 permanent experimental plots (measured for seven cycles), which were characterized as follows: pine forests (Pinus sylvestris), 63.8%; spruce (Picea abies), 30.2%; silver birch (Betula pendula Roth and Betula pubescens Ehrh.), 5.8%; and others, 0.2%. An analysis of the tree diameter and height of growth, including current and mean increments and inflection points, is presented. The models for the change in the number of trees per hectare with age are presented on the basis of the probabilistic density functions of the solutions of stochastic differential equations and the copula function. The dynamics of the number of trees per hectare are visualized graphically, and the goodness of fit of the newly developed models is evaluated using standard statistical measures. Full article

By nature, growth regulatory networks in biology are dynamic and stochastic, and feedback regulates their growth function at different ages. In this study, we carried out a stochastic modeling of growth networks and demonstrated this method using three mixed effect four-parameter Gompertz-type diffusion processes and a combination thereof using the conditional normal copula function. Using the conditional normal copula, newly derived univariate distributions can be combined into trivariate and bivariate distributions, and their corresponding conditional bivariate and univariate distributions. The link between the predictor variable and the remaining one or two explanatory variables can be formalized using copula-type densities and a numerical integration procedure. In this study, for parameter estimation, we used a semiparametric maximum pseudo-likelihood estimator procedure, which was characterized by a two-step technique, namely, separately estimating the parameters of the marginal distributions and the parameters of the copula. The results were illustrated using two observed longitudinal datasets, the first of which included the age, diameter, and potentially available area of 39,437 trees (48 stands), while the second included the age, diameter, potentially available area, and height of 8604 trees (47 stands) covering uneven mixed-species (pine, spruce, and birch) stands. All results were implemented using the MAPLE symbolic algebra system. Full article

We propose several numerical algorithms to compute the distribution of gross loss in a positively dependent catastrophe insurance portfolio. Hierarchical risk aggregation is performed using bivariate copula trees. Six common parametric copula families are studied. At every branching node, the distribution of a sum of risks is obtained by discrete copula convolution. This approach is compared to approximation by a weighted average of independent and comonotonic distributions. The weight is a measure of positive dependence through variance of the aggregate risk. During gross loss accumulation, the marginals are distorted by application of insurance financial terms, and the value of the mixing weight is impacted. To accelerate computations, we capture this effect using the ratio of standard deviations of pre-term and post-term risks, followed by covariance scaling. We test the performance of our algorithms using three examples of complex insurance portfolios subject to hurricane and earthquake catastrophes. Full article

With the popularity of big data analysis with insurance claim count data, diverse regression models for count response variable have been developed. However, there is a multicollinearlity issue with multivariate input variables to the count response regression models. Recently, deep learning and neural network models for count response have been proposed, and a Keras and Tensorflow-based deep learning model has been also proposed. To apply the deep learning and neural network models to non-normal insurance claim count data, we perform the root mean square error accuracy comparison of gradient boosting machines (a popular machine learning regression tree algorithm), principal component analysis (PCA)-based Poisson regression, PCA-based negative binomial regression, and PCA-based zero inflated poisson regression to avoid the multicollinearity of multivariate input variables with the simulated normal distribution data and the non-normal simulated data combined with normally distributed data, binary data, copula-based asymmetrical data, and two real data sets, which consist of speeding ticket and Singapore insurance claim count data. Full article

We are looking for tools to identify, model, and measure systemic risk in the insurance sector. To this aim, we investigated the possibilities of using the Dynamic Time Warping (DTW) algorithm in two ways. The first way of using DTW is to assess the suitability of the Minimum Spanning Trees’ (MST) topological indicators, which were constructed based on the tail dependence coefficients determined by the copula-DCC-GARCH model in order to establish the links between insurance companies in the context of potential shock contagion. The second way consists of using the DTW algorithm to group institutions by the similarity of their contribution to systemic risk, as expressed by DeltaCoVaR, in the periods distinguished. For the crises and the normal states identified during the period 2005–2019 in Europe, we analyzed the similarity of the time series of the topological indicators of MST, constructed for 38 European insurance institutions. The results obtained confirm the effectiveness of MST topological indicators for systemic risk identification and the evaluation of indirect links between insurance institutions. Full article

This paper features an application of Regular Vine (R-vine) copulas, a recently developed statistical tool to assess composite risk. Copula-based dependence modelling is a popular tool in conditional risk assessment, but is usually applied to pairs of variables. By contrast, Vine copulas provide greater flexibility and permit the modelling of complex dependency patterns using a wide variety of bivariate copulas which may be arranged and analysed in a tree structure to explore multiple dependencies. This study emphasises the use of R-vine copulas in an analysis of the co-dependencies of five reservoirs in the cascade of the Saint-John River basin in Eastern Canada. The developed R-vine copulas lead to the joint and conditional return periods of maximum volumes, for hydrologic design and cascade reservoir management in the basin. The main attraction of this approach to risk modelling is the flexibility in the choice of distributions used to model heavy-tailed marginals and co-dependencies. Full article

While the dependence assumption among the components is naturally important in evaluating the reliability of a system, studies investigating the issues of aggregation errors in Bayesian reliability analyses have been focused mainly on systems with independent components. This study developed a copula-based Bayesian reliability model to formulate dependency between components of a parallel system and to estimate the failure rate of the system. In particular, we integrated Monte Carlo simulation and classification tree learning to identify key factors that affect the magnitude of errors in the estimation of posterior means of system reliability (for different Bayesian analysis approaches—aggregate analysis, disaggregate analysis, and simplified disaggregate analysis) to provide important guidelines for choosing the most appropriate approach for analyzing a model of products of a probability and a frequency for parallel systems with dependent components. Full article

In the present work, we analyze the dynamics of indirect connections between insurance companies that result from market price channels. In our analysis, we assume that the stock quotations of insurance companies reflect market sentiments, which constitute a very important systemic risk factor. Interlinkages between insurers and their dynamics have a direct impact on systemic risk contagion in the insurance sector. Herein, we propose a new hybrid approach to the analysis of interlinkages dynamics based on combining the copula-DCC-GARCH model and minimum spanning trees (MST). Using the copula-DCC-GARCH model, we determine the tail dependence coefficients. Then, for each analyzed period we construct MST based on these coefficients. The dynamics are analyzed by means of the time series of selected topological indicators of the MSTs in the years 2005–2019. The contribution to systemic risk of each institution is determined by analyzing the deltaCoVaR time series using the copula-DCC-GARCH model. Our empirical results show the usefulness of the proposed approach to the analysis of systemic risk (SR) in the insurance sector. The times series obtained from the proposed hybrid approach reflect the phenomena occurring in the market. We check whether the analyzed MST topological indicators can be considered as systemic risk predictors. Full article

We present several fast algorithms for computing the distribution of a sum of spatially dependent, discrete random variables to aggregate catastrophe risk. The algorithms are based on direct and hierarchical copula trees. Computing speed comes from the fact that loss aggregation at branching nodes is based on combination of fast approximation to brute-force convolution, arithmetization (regriding) and linear complexity of the method for computing the distribution of comonotonic sum of risks. We discuss the impact of tree topology on the second-order moments and tail statistics of the resulting distribution of the total risk. We test the performance of the presented models by accumulating ground-up loss for 29,000 risks affected by hurricane peril. Full article

Rainfall interception is an important process of the water cycle that can have significant influence on surface runoff and groundwater storage. Since rainfall interception measurements are rare and time consuming, rainfall interception estimation can be made indirectly using different meteorological variables. Experimental data of rainfall interception for birch and pine trees was measured at an experimental plot located in an urban area of Ljubljana, Slovenia in this study. A copula model was applied to predict the rainfall interception using meteorological variables, namely air temperature and vapour pressure deficit data. The copula model performance was compared to some other models such as decision trees, multiple linear regressions, and exponential functions. Using random sampling, we found that the copula model where Khoudraji-Liebscher copula functions were used yielded slightly smaller root mean square error (RMSE) and mean absolute error (MAE) values than other tested methods (i.e., RMSE and MAE results for birch trees were 24.2% and 18.2%, respectively and RMSE and MAE results for pine trees were 25.0% and 19.6%, respectively). The results demonstrate that the copula-based proposed method and other tested models could be used for the prediction of rainfall interception at the considered plot and in the wider surroundings. Furthermore, these models could also be applied for the prediction of rainfall interception for these two tree species in other locations under similar vegetation and meteorological conditions. Full article