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Journal articles on the topic 'Dependence modelling'

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Published: 4 June 2021
Last updated: 19 February 2022

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1

Kallenberg, Wilbert C. M. "Modelling dependence." Insurance: Mathematics and Economics 42, no. 1 (February 2008): 127–46. http://dx.doi.org/10.1016/j.insmatheco.2007.01.008.

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2

Aminuddin Jafry, Nurul Hanis, Ruzanna Ab Razak, and Noriszura Ismail. "Dependence Modelling using GARCH, EGARCH, and Copula Models:." Asia Proceedings of Social Sciences 2, no. 2 (December 3, 2018): 55–59. http://dx.doi.org/10.31580/apss.v2i2.317.

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Copula become a popular tool to measure the dependency between financial data due to its ability to capture the non-normal distributions. Hence, this paper will inspect the impact of input models towards the parameter estimation of marginal and copula models for KLCI and FBMHS returns series by considering the ARMA-GARCH model and the ARMA-EGARCH model. This study also investigates the dependency of Islamic-conventional pair for Malaysia indices by using static copula and time-varying copula approach. The closing prices of Malaysia indices represented by KLCI (conventional) index and FBMHS (Islamic) index for the period of 21 May 2007 until 28 September 2018 are used as a sample data. The results show that KLCI-FBMHS pair is strongly correlated, different input models (ARMA-GARCH and ARMA-EGARCH) have identical dependence structure but slightly different value of parameter estimated, and the time-varying Gaussian copula is chosen as the best dependence model. Finding suggest that the diversification between Islamic-conventional pair is worthwhile during stable period.
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3

Patton, Andrew J. "MODELLING ASYMMETRIC EXCHANGE RATE DEPENDENCE*." International Economic Review 47, no. 2 (May 2006): 527–56. http://dx.doi.org/10.1111/j.1468-2354.2006.00387.x.

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4

Wadsworth, J. L., J. A. Tawn, A. C. Davison, and D. M. Elton. "Modelling across extremal dependence classes." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 79, no. 1 (February 17, 2016): 149–75. http://dx.doi.org/10.1111/rssb.12157.

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5

Wadsworth, J. L., and J. A. Tawn. "Dependence modelling for spatial extremes." Biometrika 99, no. 2 (March 13, 2012): 253–72. http://dx.doi.org/10.1093/biomet/asr080.

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6

El Hannoun, Wafaa, Salah-Eddine El Adlouni, and Abdelhak Zoglat. "Vine-Copula-Based Quantile Regression for Cascade Reservoirs Management." Water 13, no. 7 (March 31, 2021): 964. http://dx.doi.org/10.3390/w13070964.

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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.
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7

Gitman, I. M., H. Askes, and L. J. Sluys. "Multiscale Modelling of Softening Materials." Key Engineering Materials 348-349 (September 2007): 1–4. http://dx.doi.org/10.4028/www.scientific.net/kem.348-349.1.

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A hierarchical multiscale procedure for softening materials is proposed in this paper. A standard multiscale model has been analysed with respect to macro-level mesh dependence and meso-level cell size dependence. In order to eliminate spurious macro-level mesh dependence and meso-level cell size dependence a coupled-volume approach has been proposed. A discussion on the various interacting length scales in the model is included.
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8

Lakner, G., J. Lakner, G. Racz, and M. Kłos. "Concentration dependence modelling of reverse osmosis." DESALINATION AND WATER TREATMENT 192 (2020): 437–43. http://dx.doi.org/10.5004/dwt.2020.25906.

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9

MONNEY, PAUL-ANDRE, and MOSES CHAN. "MODELLING DEPENDENCE IN DEMPSTER-SHAFER THEORY." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 15, no. 01 (February 2007): 93–114. http://dx.doi.org/10.1142/s021848850700439x.

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Belief functions can only be combined by Dempster's rule when they are based on independent items of evidence. This paper proposes a method for handling the case where there is some probabilistic dependence among the items of evidence. The method relies on compact representations of joint probability distributions on the assumption variables associated with the belief functions. These distributions are then used to compute degrees of support of hypotheses of interest. It is shown that the theory of hints is the appropriate general framework for this method.
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10

Ledford, Anthony W., and Jonathan A. Tawn. "Modelling Dependence within Joint Tail Regions." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 59, no. 2 (1997): 475–99. http://dx.doi.org/10.1111/1467-9868.00080.

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11

Lakner, G., J. Lakner, G. Racz, and M. Kłos. "Temperature dependence modelling of reverse osmosis." DESALINATION AND WATER TREATMENT 192 (2020): 431–36. http://dx.doi.org/10.5004/dwt.2020.25803.

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12

Zhang, Ming-Heng. "Modelling total tail dependence along diagonals." Insurance: Mathematics and Economics 42, no. 1 (February 2008): 73–80. http://dx.doi.org/10.1016/j.insmatheco.2007.01.002.

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13

Masseran, Nurulkamal, and Saiful Izzuan Hussain. "Copula Modelling on the Dynamic Dependence Structure of Multiple Air Pollutant Variables." Mathematics 8, no. 11 (October 31, 2020): 1910. http://dx.doi.org/10.3390/math8111910.

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A correlation analysis of pollutant variables provides comprehensive information on dependency behaviour and is thus useful in relating the risk and consequences of pollution events. However, common correlation measurements fail to capture the various properties of air pollution data, such as their non-normal distribution, heavy tails, and dynamic changes over time. Hence, they cannot generate highly accurate information. To overcome this issue, this study proposes a combination of the Generalized Autoregressive Conditional Heteroskedasticity model, Generalized Pareto distribution, and stochastic copulas as a tool to investigate the dependence structure between the PM10 variable and other pollutant variables, including CO, NO2, O3, and SO2. Results indicate that the dynamic dependence structure between PM10 and other pollutant variables can be described with a ranking of PM10–CO > PM10–SO2 > PM10–NO2 > PM10–O3 for the overall time paths (δ) and the upper tail (τU) or lower tail (τL) dependency measures. This study reveals an evident correlation among pollutant variables that changes over time; such correlation reflects dynamic dependency.
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14

Zhou, Rui. "MODELLING MORTALITY DEPENDENCE WITH REGIME-SWITCHING COPULAS." ASTIN Bulletin 49, no. 2 (April 24, 2019): 373–407. http://dx.doi.org/10.1017/asb.2019.10.

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AbstractWe propose a two-regime Markov switching copula to depict the evolution of mortality dependence. One regime represents periods of high dependence and the other regime represents periods of low dependence. Each regime features a regular vine (R-vine) copula that, built on bivariate copulas, provides great flexibility for modelling complex high-dimensional dependence. Our estimated model indicates that the years of recovery from extreme mortality deterioration and the years of health care reform more likely fall into the low regime, while the years in which extreme mortality deteriorating events break out and the peaceful years without major mortality-impacting events more likely fall into the high regime. We use a case study to illustrate how the regime-switching copula can be applied to assess the effectiveness of longevity risk hedge with different beliefs about future mortality dependence evolution incorporated.
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15

Ben Salah, T., H. Morel, and S. Mtimet. "Toward SiC-JFETs modelling with temperature dependence." European Physical Journal Applied Physics 52, no. 2 (October 6, 2010): 20301. http://dx.doi.org/10.1051/epjap/2010139.

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16

Dutfoy, A., and R. Lebrun. "Practical approach to dependence modelling using copulas." Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 223, no. 4 (August 3, 2009): 347–61. http://dx.doi.org/10.1243/1748006xjrr226.

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17

Carbno, Collin. "Uncertainty Analysis With High Dimensional Dependence Modelling." Technometrics 49, no. 1 (February 2007): 108. http://dx.doi.org/10.1198/tech.2007.s464.

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18

Naveau, P., A. Guillou, D. Cooley, and J. Diebolt. "Modelling pairwise dependence of maxima in space." Biometrika 96, no. 1 (January 24, 2009): 1–17. http://dx.doi.org/10.1093/biomet/asp001.

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19

Lowe, D. B., and T. R. Finlayson. "Modelling the dependence from magnetic relaxation measurements." Superconductor Science and Technology 11, no. 10 (October 1, 1998): 1032–35. http://dx.doi.org/10.1088/0953-2048/11/10/027.

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20

Journel, A. G. "Modelling uncertainty and spatial dependence: Stochastic imaging." International journal of geographical information systems 10, no. 5 (July 1996): 517–22. http://dx.doi.org/10.1080/02693799608902094.

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21

Journel, A. G. "Modelling uncertainty and spatial dependence: Stochastic imaging." Geographical Information Systems 10, no. 5 (August 1, 1996): 517–22. http://dx.doi.org/10.1080/026937996137846.

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22

Leonelli, Manuele, and Dani Gamerman. "Semiparametric bivariate modelling with flexible extremal dependence." Statistics and Computing 30, no. 2 (May 28, 2019): 221–36. http://dx.doi.org/10.1007/s11222-019-09878-w.

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23

Bernardi, Mauro, and Leopoldo Catania. "Portfolio optimisation under flexible dynamic dependence modelling." Journal of Empirical Finance 48 (September 2018): 1–18. http://dx.doi.org/10.1016/j.jempfin.2018.05.002.

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24

Merz, Michael, Mario V. Wüthrich, and Enkelejd Hashorva. "Dependence modelling in multivariate claims run-off triangles." Annals of Actuarial Science 7, no. 1 (September 5, 2012): 3–25. http://dx.doi.org/10.1017/s1748499512000140.

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AbstractA central issue in claims reserving is the modelling of appropriate dependence structures. Most classical models cannot cope with this task. We define a multivariate log-normal model that allows to model both, dependence between different sub-portfolios and dependence within sub-portfolios such as claims inflation. In this model we derive closed form solutions for claims reserves and the corresponding prediction uncertainty.
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25

Gumarov, Gali S., Vladimir V. Konovalov, Amangeldy E. Sarsenov, Zhanna K. Kubasheva, and Abelshaek A. Rakhimov. "Mathematical modelling of traction resistance of the improved opener of grain seeder." BIO Web of Conferences 17 (2020): 00044. http://dx.doi.org/10.1051/bioconf/20201700044.

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The object of the study is a double disc coulter with a pressure plate. The aim of the research was to simulate the operation of the double disc coulter using a grain seed drill as an example. Analytical dependencies for determining the traction resistance of the improved coulter are presented. The method of calculating the drag resistance of a double disc coulter with an original pressure plate installed on it is given. The components of the traction resistance balance have been determined and the dependence on the sowing depth has been established. 3D graphical dependences of the traction resistance of serial and advanced coulters on the speed and depth of its movement have been constructed based on the results of calculations. The main influence is the placement depth. Speed of movement of the seeder in the considered interval of its values does not render essential influence on traction resistance. Installation of an additional pressure plate increases traction resistance to 5 %.
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26

Oh, Dong Hwan, and Andrew J. Patton. "Modelling Dependence in High Dimensions with Factor Copulas." Finance and Economics Discussion Series 2015, no. 051 (July 2015): 1–41. http://dx.doi.org/10.17016/feds.2015.051.

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27

Boland, Philip J., Frank Proschan, and Y. L. Tong. "Modelling dependence in simple and indirect majority systems." Journal of Applied Probability 26, no. 1 (March 1989): 81–88. http://dx.doi.org/10.2307/3214318.

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Majority systems are encountered in both decision theory and reliability theory. In decision theory for example a jury or committee employing a majority rule will make the ‘correct' decision if a majority of the individuals do so. In reliability theory some coherent systems function if and only if a majority of the components work properly. In this paper results concerning the reliability of majority systems are developed which are applicable in both areas. Two models incorporating dependence between individuals or components in majority systems are introduced, and various monotonicity results for their reliability functions are established. Comparisons are also made between direct (or simple) and indirect majority systems.
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28

Boland, Philip J., Frank Proschan, and Y. L. Tong. "Modelling dependence in simple and indirect majority systems." Journal of Applied Probability 26, no. 01 (March 1989): 81–88. http://dx.doi.org/10.1017/s0021900200041814.

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Majority systems are encountered in both decision theory and reliability theory. In decision theory for example a jury or committee employing a majority rule will make the ‘correct' decision if a majority of the individuals do so. In reliability theory some coherent systems function if and only if a majority of the components work properly. In this paper results concerning the reliability of majority systems are developed which are applicable in both areas. Two models incorporating dependence between individuals or components in majority systems are introduced, and various monotonicity results for their reliability functions are established. Comparisons are also made between direct (or simple) and indirect majority systems.
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29

Medir Tejado, Lluís, and Esther Pano. "Modelling local autonomy and dependence through cooperative relations." International Journal of Public Sector Management 31, no. 4 (May 14, 2018): 410–25. http://dx.doi.org/10.1108/ijpsm-01-2017-0013.

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Purpose Given the spread of multi-level governance tools, interaction between local and regional governments has become an important mechanism for service delivery and the implementation of public policies. The purpose of this paper is to empirically test a model of cooperative relations affecting local governments and thus having impact on local autonomy and dependence. Design/methodology/approach This paper takes previous typologies as a starting point to theoretically build and empirically test a model of cooperative relations based on two selected indicators: the degree of autonomy-dependence and the degree of rigidity-flexibility of the cooperative system. In a second step, the authors stimulate the model numerically to match real data on it coming from South European local governments to assess the functioning of the theory in a concrete space and time. Findings The combination of the aforementioned concepts, creates a four-option theoretical model that describes four possible situations where cooperative intergovernmental relations can be empirically placed. Originality/value This paper points out the need to incorporate empirical studies to trace the characteristics and evolution of the cooperative relations between local governments and upper tiers. This is particularly relevant if the authors are referring to mechanisms that can vary through time. In the current big and open data era, this empirical process will become easier and more affordable. In this context, local government studies benefit from particular features that improve the operation of this kind of analysis: large “N” configuration (a large number of units to be included) and a reasonable equivalence in concepts and bodies that allows comparability.
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30

Niazi, Muhammad Hassan Khan, Oswaldo Morales Nápoles, and Bregje K. Van Wesenbeeck. "VEGETATED HYDRODYNAMIC SYSTEM: PARAMETERIZATION AND STOCHASTIC DEPENDENCE MODELLING." Coastal Engineering Proceedings, no. 36v (December 28, 2020): 25. http://dx.doi.org/10.9753/icce.v36v.waves.25.

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Vegetation as a nature-based solution for increasing flood risk has convincingly shown potential for flood hazard (wave load) reduction but lacks generalized results. In this study we have introduced stochastic dependence modeling using non-parametric Bayesian networks (NPBN) for vegetated coastal systems where the system was parametrized using continuous marginal distributions, and likely (conditional) correlations among variables. The model represented a consistent joint probability distribution and hence can be used to generate physically realistic conditions in data-scare environments. It adds value to numerical modeling by reducing the number of simulations required to get meaningful generalized results. Main findings, that were derived by using a NPBN, help to pave way for implementation of nature-based solutions for a range of realistic conditions that can be found across global coastal foreshores.Recorded Presentation from the vICCE (YouTube Link): https://youtu.be/T6TP0DH0qMw
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31

Chen, Song Xi, and Tzee-Ming Huang. "Nonparametric estimation of copula functions for dependence modelling." Canadian Journal of Statistics 35, no. 2 (June 2007): 265–82. http://dx.doi.org/10.1002/cjs.5550350205.

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32

Spyridis, Panagiotis, Alexios Tamparopoulos, and Konrad Bergmeister. "Effects of multivariate dependence modelling on reliability estimates." Beton- und Stahlbetonbau 113 (September 2018): 42–47. http://dx.doi.org/10.1002/best.201800049.

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33

Czado, Claudia, Eugen Ivanov, and Yarema Okhrin. "Modelling temporal dependence of realized variances with vines." Econometrics and Statistics 12 (October 2019): 198–216. http://dx.doi.org/10.1016/j.ecosta.2019.03.003.

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34

Bolsinova, Maria, Paul de Boeck, and Jesper Tijmstra. "Modelling Conditional Dependence Between Response Time and Accuracy." Psychometrika 82, no. 4 (October 13, 2016): 1126–48. http://dx.doi.org/10.1007/s11336-016-9537-6.

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35

Zolotko, Mikhail, and Ostap Okhrin. "Modelling the general dependence between commodity forward curves." Energy Economics 43 (May 2014): 284–96. http://dx.doi.org/10.1016/j.eneco.2014.02.019.

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36

Smith, Michael Stanley. "Copula modelling of dependence in multivariate time series." International Journal of Forecasting 31, no. 3 (July 2015): 815–33. http://dx.doi.org/10.1016/j.ijforecast.2014.04.003.

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37

Jane, Robert, Luis Cadavid, Jayantha Obeysekera, and Thomas Wahl. "Multivariate statistical modelling of the drivers of compound flood events in south Florida." Natural Hazards and Earth System Sciences 20, no. 10 (October 10, 2020): 2681–99. http://dx.doi.org/10.5194/nhess-20-2681-2020.

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Abstract. Miami-Dade County (south-east Florida) is among the most vulnerable regions to sea level rise in the United States, due to a variety of natural and human factors. The co-occurrence of multiple, often statistically dependent flooding drivers – termed compound events – typically exacerbates impacts compared with their isolated occurrence. Ignoring dependencies between the drivers will potentially lead to underestimation of flood risk and under-design of flood defence structures. In Miami-Dade County water control structures were designed assuming full dependence between rainfall and Ocean-side Water Level (O-sWL), a conservative assumption inducing large safety factors. Here, an analysis of the dependence between the principal flooding drivers over a range of lags at three locations across the county is carried out. A two-dimensional analysis of rainfall and O-sWL showed that the magnitude of the conservative assumption in the original design is highly sensitive to the regional sea level rise projection considered. Finally, the vine copula and Heffernan and Tawn (2004) models are shown to outperform five standard higher-dimensional copulas in capturing the dependence between the principal drivers of compound flooding: rainfall, O-sWL, and groundwater level. The work represents a first step towards the development of a new framework capable of capturing dependencies between different flood drivers that could potentially be incorporated into future Flood Protection Level of Service (FPLOS) assessments for coastal water control structures.
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38

Huang, Xinlin, Jianmin Gao, and Zhiyong Gao. "Precise signed digraph modelling based on causal dependence identification." Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering 227, no. 1 (January 19, 2012): 3–16. http://dx.doi.org/10.1177/0954408911435745.

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The fault diagnosis approach based on signed digraph is promising, but signed digraph models built by existing methods often contain false causalities and make spurious diagnosis results. In this article, a signed digraph modelling method based on causal dependence identification is proposed. Many equations used to describe mechanism of process system can be used to analyse the cause–effect relation between state variables and build precise signed digraph models. The cause–effect relation hided in system equations is extracted through causal dependence identification of algebraic and differential equations. Signed digraph model is then constructed by merging the analysis results. The method of causal dependence identification and strongly connected components identification of process system is investigated in detail. Algorithm of causal dependence identification is summarized and results in a simple but effective signed digraph construction procedure. The validity of the proposed method was tested by the case study of signed digraph modelling for a series connected liquid storage system, and the efficiency of the algorithm was tested by another case study of a compressor unit system. The comparison result shows that the proposed method can extract precise causal dependence relation between state variables from system equations and build signed digraph model effectively with less resource consumption, which is very important for building signed digraph model of large scale process system.
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39

Henshaw, Kira, Corina Constantinescu, and Olivier Menoukeu Pamen. "Stochastic Mortality Modelling for Dependent Coupled Lives." Risks 8, no. 1 (February 11, 2020): 17. http://dx.doi.org/10.3390/risks8010017.

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Broken-heart syndrome is the most common form of short-term dependence, inducing a temporary increase in an individual’s force of mortality upon the occurrence of extreme events, such as the loss of a spouse. Socioeconomic influences on bereavement processes allow for suggestion of variability in the significance of short-term dependence between couples in countries of differing levels of economic development. Motivated by analysis of a Ghanaian data set, we propose a stochastic mortality model of the joint mortality of paired lives and the causal relation between their death times, in a less economically developed country than those considered in existing studies. The paired mortality intensities are assumed to be non-mean-reverting Cox–Ingersoll–Ross processes, reflecting the reduced concentration of the initial loss impact apparent in the data set. The effect of the death on the mortality intensity of the surviving spouse is given by a mean-reverting Ornstein–Uhlenbeck process which captures the subsiding nature of the mortality increase characteristic of broken-heart syndrome. Inclusion of a population wide volatility parameter in the Ornstein–Uhlenbeck bereavement process gives rise to a significant non-diversifiable risk, heightening the importance of the dependence assumption in this case. Applying the model proposed to an insurance pricing problem, we obtain the appropriate premium under consideration of dependence between coupled lives through application of the indifference pricing principle.
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40

Franchi, Franca, Roberta Nibbi, and Brian Straughan. "Continuous dependence on modelling for temperature-dependent bidispersive flow." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 473, no. 2208 (December 2017): 20170485. http://dx.doi.org/10.1098/rspa.2017.0485.

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We consider a model for flow in a porous medium which has a double porosity structure. There is the usual porosity herein called macro porosity, but in addition, we allow for a porosity due to cracks or fissures in the solid skeleton. The cracks give rise to a micro porosity. The model considered also allows for temperature effects with a single temperature T . This paper analyses three aspects of structural stability. The first establishes continuous dependence of the solution on the interaction coefficient between the velocities associated with the macro and micro porosity. The second analyses continuous dependence on the viscosity coefficients, while the third establishes continuous dependence on the radiation constant when Newton’s law of cooling is involved on the boundary.
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41

Avanzi, Benjamin, Greg Taylor, and Bernard Wong. "CORRELATIONS BETWEEN INSURANCE LINES OF BUSINESS: AN ILLUSION OR A REAL PHENOMENON? SOME METHODOLOGICAL CONSIDERATIONS." ASTIN Bulletin 46, no. 2 (January 27, 2016): 225–63. http://dx.doi.org/10.1017/asb.2015.31.

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AbstractThis paper is concerned with dependency between business segments in the non-life insurance industry. When considering the business of an insurance company at the aggregate level, dependence structures can have a major impact in several areas of Enterprise Risk Management, such as in claims reserving and capital modelling. The accurate estimation of the diversification benefits related to the dependence structures between lines of business (LoBs) is crucial for (i) capital efficiency, as one should avoid holding unnecessarily high levels of capital, and (ii) solvency of the insurance company, as an underestimation, on the other hand, may lead to insufficient capitalisation and safety. There seems to be a great deal of preconception as to how dependent insurance claims should be. Often, presence of dependence is taken as a given and rarely discussed or challenged, perhaps because of the lack of extensive datasets to be publicly analysed. In this paper, we take a different approach, and consider how much correlation some real datasets actually display (the Meyers–Shi dataset from the USA, and the AUSI dataset from Australia). We develop a simple theoretical framework that enables us to explain how and why correlations can be illusory (and what we mean by that). We show with some real examples that, sometimes, most (if not all) of the correlation can be “explained” by an appropriate methodology. Two major conclusions stem from our analysis. 1.In any attempt to measure cross-LoB correlations, careful modelling of the data needs to be the order of the day. The exercise will not be well served by rough modelling, such as the use of simple chain ladders, and may indeed result in the prescription of excessive risk margins and/or capital margins.2.Such empirical evidence as examined in the paper reveals cross-LoB correlations that vary only in the range zero to very modest. There is little evidence in favour of the high correlation assumed in some jurisdictions. The evidence suggests that these assumptions derived from either poor modelling or a misconception of the cross-LoB dependencies relevant to the purpose to which they are applied.
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42

Symak, Dmytro, Vira Sabadash, Jaroslaw Gumnitsky, and Zoriana Hnativ. "Kinetic Regularities and Mathematical Modelling of Potassium Chloride Dissolution." Chemistry & Chemical Technology 15, no. 1 (February 15, 2021): 148–52. http://dx.doi.org/10.23939/chcht15.01.148.

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The dissolution process of potassium chloride particles in the apparatus with two-blade mechanical stirrer was investigated and the mass transfer coefficient was determined. The experimental results were generalized by criterion dependence. The independence of the mass transfer coefficient from the solid particles diameter was confirmed. A countercurrent process of potassium salt dissolution in two apparatuses with a mechanical stirring was considered. A mathematical model for countercurrent dissolution was developed and the efficiency of this process was determined.
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43

Aas, Kjersti, Thomas Nagler, Martin Jullum, and Anders Løland. "Explaining predictive models using Shapley values and non-parametric vine copulas." Dependence Modeling 9, no. 1 (January 1, 2021): 62–81. http://dx.doi.org/10.1515/demo-2021-0103.

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Abstract In this paper the goal is to explain predictions from complex machine learning models. One method that has become very popular during the last few years is Shapley values. The original development of Shapley values for prediction explanation relied on the assumption that the features being described were independent. If the features in reality are dependent this may lead to incorrect explanations. Hence, there have recently been attempts of appropriately modelling/estimating the dependence between the features. Although the previously proposed methods clearly outperform the traditional approach assuming independence, they have their weaknesses. In this paper we propose two new approaches for modelling the dependence between the features. Both approaches are based on vine copulas, which are flexible tools for modelling multivariate non-Gaussian distributions able to characterise a wide range of complex dependencies. The performance of the proposed methods is evaluated on simulated data sets and a real data set. The experiments demonstrate that the vine copula approaches give more accurate approximations to the true Shapley values than their competitors.
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44

Pokoev, A. V., and V. V. Volkov. "Numerical Modelling of Heterodiffusion from the Variable Type Source." Defect and Diffusion Forum 363 (May 2015): 120–23. http://dx.doi.org/10.4028/www.scientific.net/ddf.363.120.

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The algorithm for numerical modeling of the heterodiffusion from a finite thickness layer under restricted impurity solubility in the matrix and concentration dependence of the diffusion coefficient is proposed. It allows obtaining the time dependence of the impurity distributions in conditions which mimic real experiments.
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Maclean, Mairi, and Charles Edward Harvey. "Modelling Path Dependence: Time and Structure in Strategic Change." Academy of Management Proceedings 2017, no. 1 (August 2017): 13963. http://dx.doi.org/10.5465/ambpp.2017.13963abstract.

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Dossou-Gbe, S., B. Some, and D. Barro. "Modelling the Dependence of Parametric Bivariate Extreme Value Copulas." Asian Journal of Mathematics & Statistics 2, no. 3 (August 15, 2009): 41–54. http://dx.doi.org/10.3923/ajms.2009.41.54.

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Dossou-Gbe, S., B. Some, and D. Barro. "Modelling the Dependence of Parametric Bivariate Extreme Value Copulas." Asian Journal of Mathematics & Statistics 3, no. 3 (June 15, 2010): 187–200. http://dx.doi.org/10.3923/ajms.2010.187.200.

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Mahmoud, Ra'ad D., and Chris Done. "Modelling the energy dependence of black hole binary flows." Monthly Notices of the Royal Astronomical Society 473, no. 2 (September 13, 2017): 2084–97. http://dx.doi.org/10.1093/mnras/stx2359.

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Fenech, Jean-pierre, Hamed Vosgha, and Salwa Shafik. "Modelling the dependence structures of Australian iTraxx CDS index." Applied Economics 46, no. 4 (November 2013): 420–31. http://dx.doi.org/10.1080/00036846.2013.849378.

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Hauser, Michael A., Robert M. Kunst, and Erhard Reschenhofer. "Modelling exchange rates: long-run dependence versus conditional heteroscedasticity." Applied Financial Economics 4, no. 3 (June 1994): 233–39. http://dx.doi.org/10.1080/758526904.

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