Dependence Modeling最新文献_第5页

Study of partial and average conditional Kendall’s tau 偏条件和平均条件肯德尔τ的研究

IF 0.7

Dependence Modeling Pub Date : 2021-01-01 DOI: 10.1515/demo-2021-0104

I. Gijbels, Margot Matterne

引用次数: 3

On copulas of self-similar Ito processes 关于自相似Ito过程的联结

IF 0.7

Dependence Modeling Pub Date : 2021-01-01 DOI: 10.1515/demo-2021-0112

P. Jaworski, Marcin Krzywda

引用次数: 1

Asymptotic normality of the relative error regression function estimator for censored and time series data 截尾和时间序列数据的相对误差回归函数估计量的渐近正态性

IF 0.7

Dependence Modeling Pub Date : 2021-01-01 DOI: 10.1515/demo-2021-0107

Feriel Bouhadjera, E. O. Saïd

引用次数: 1

Generating unfavourable VaR scenarios under Solvency II with patchwork copulas 用拼凑copula在Solvency II下生成不利的VaR情景

IF 0.7

Dependence Modeling Pub Date : 2021-01-01 DOI: 10.1515/demo-2021-0115

Dietmar Pfeifer, O. Ragulina

引用次数: 3

Special Issue on copulas in memory of Abe Sklar (1925-2020) 纪念亚伯·斯卡拉的系词特刊（1925-2020）

IF 0.7

Dependence Modeling Pub Date : 2021-01-01 DOI: 10.1515/demo-2021-0109

Giovanni Puccetti

引用次数: 0

Diagonal sections of copulas, multivariate conditional hazard rates and distributions of order statistics for minimally stable lifetimes 最小稳定寿命的copula的对角截面，多变量条件危险率和序统计量的分布

IF 0.7

Dependence Modeling Pub Date : 2021-01-01 DOI: 10.1515/demo-2021-0119

Rachele Foschi, G. Nappo, F. Spizzichino

{"title":"Diagonal sections of copulas, multivariate conditional hazard rates and distributions of order statistics for minimally stable lifetimes","authors":"Rachele Foschi, G. Nappo, F. Spizzichino","doi":"10.1515/demo-2021-0119","DOIUrl":"https://doi.org/10.1515/demo-2021-0119","url":null,"abstract":"Abstract As a motivating problem, we aim to study some special aspects of the marginal distributions of the order statistics for exchangeable and (more generally) for minimally stable non-negative random variables T1, ..., Tr. In any case, we assume that T1, ..., Tr are identically distributed, with a common survival function ̄G and their survival copula is denoted by K. The diagonal sections of K, along with ̄G, are possible tools to describe the information needed to recover the laws of order statistics. When attention is restricted to the absolutely continuous case, such a joint distribution can be described in terms of the associated multivariate conditional hazard rate (m.c.h.r.) functions. We then study the distributions of the order statistics of T1, ..., Tr also in terms of the system of the m.c.h.r. functions. We compare and, in a sense, we combine the two different approaches in order to obtain different detailed formulas and to analyze some probabilistic aspects for the distributions of interest. This study also leads us to compare the two cases of exchangeable and minimally stable variables both in terms of copulas and of m.c.h.r. functions. The paper concludes with the analysis of two remarkable special cases of stochastic dependence, namely Archimedean copulas and load sharing models. This analysis will allow us to provide some illustrative examples, and some discussion about peculiar aspects of our results.","PeriodicalId":43690,"journal":{"name":"Dependence Modeling","volume":"9 1","pages":"394 - 423"},"PeriodicalIF":0.7,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43934293","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Multivariate radial symmetry of copula functions: finite sample comparison in the i.i.d case 共轭函数的多元径向对称:i.i.d情况下的有限样本比较

IF 0.7

Dependence Modeling Pub Date : 2021-01-01 DOI: 10.1515/demo-2021-0102

Monica Billio, L. Frattarolo, D. Guégan

引用次数: 1

Hoeffding–Sobol decomposition of homogeneous co-survival functions: from Choquet representation to extreme value theory application 齐次共生存函数的Hoeffding-Sobol分解：从Choquet表示到极值理论的应用

IF 0.7

Dependence Modeling Pub Date : 2021-01-01 DOI: 10.1515/demo-2021-0108

Cécile Mercadier, P. Ressel

引用次数: 1

On convergence of associative copulas and related results 关于结合系词的收敛性及相关结果

IF 0.7

Dependence Modeling Pub Date : 2021-01-01 DOI: 10.1515/demo-2021-0114

Thimo M. Kasper, S. Fuchs, W. Trutschnig

引用次数: 1

Explaining predictive models using Shapley values and non-parametric vine copulas 使用Shapley值和非参数vine Copula解释预测模型

IF 0.7

Dependence Modeling Pub Date : 2021-01-01 DOI: 10.1515/demo-2021-0103

K. Aas, T. Nagler, Martin Jullum, A. Løland

{"title":"Explaining predictive models using Shapley values and non-parametric vine copulas","authors":"K. Aas, T. Nagler, Martin Jullum, A. Løland","doi":"10.1515/demo-2021-0103","DOIUrl":"https://doi.org/10.1515/demo-2021-0103","url":null,"abstract":"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.","PeriodicalId":43690,"journal":{"name":"Dependence Modeling","volume":"9 1","pages":"62 - 81"},"PeriodicalIF":0.7,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/demo-2021-0103","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49658964","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 15