Dependence Modeling最新文献_第7页

Two symmetric and computationally efficient Gini correlations 两个对称且计算效率高的基尼系数相关性

IF 0.7

Dependence Modeling Pub Date : 2020-01-01 DOI: 10.1515/demo-2020-0020

Courtney Vanderford, Yongli Sang, Xin Dang

{"title":"Two symmetric and computationally efficient Gini correlations","authors":"Courtney Vanderford, Yongli Sang, Xin Dang","doi":"10.1515/demo-2020-0020","DOIUrl":"https://doi.org/10.1515/demo-2020-0020","url":null,"abstract":"Abstract Standard Gini correlation plays an important role in measuring the dependence between random variables with heavy-tailed distributions. It is based on the covariance between one variable and the rank of the other. Hence for each pair of random variables, there are two Gini correlations and they are not equal in general, which brings a substantial difficulty in interpretation. Recently, Sang et al (2016) proposed a symmetric Gini correlation based on the joint spatial rank function with a computation cost of O(n2) where n is the sample size. In this paper, we study two symmetric and computationally efficient Gini correlations with the computational complexity of O(n log n). The properties of the new symmetric Gini correlations are explored. The influence function approach is utilized to study the robustness and the asymptotic behavior of these correlations. The asymptotic relative efficiencies are considered to compare several popular correlations under symmetric distributions with different tail-heaviness as well as an asymmetric log-normal distribution. Simulation and real data application are conducted to demonstrate the desirable performance of the two new symmetric Gini correlations.","PeriodicalId":43690,"journal":{"name":"Dependence Modeling","volume":"8 1","pages":"373 - 395"},"PeriodicalIF":0.7,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/demo-2020-0020","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49002518","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: 1

Relations between ageing and dependence for exchangeable lifetimes with an extension for the IFRA/DFRA property 老化和可交换寿命依赖性与IFRA/DFRA特性扩展之间的关系

IF 0.7

Dependence Modeling Pub Date : 2020-01-01 DOI: 10.1515/demo-2020-0001

G. Nappo, F. Spizzichino

{"title":"Relations between ageing and dependence for exchangeable lifetimes with an extension for the IFRA/DFRA property","authors":"G. Nappo, F. Spizzichino","doi":"10.1515/demo-2020-0001","DOIUrl":"https://doi.org/10.1515/demo-2020-0001","url":null,"abstract":"Abstract We first review an approach that had been developed in the past years to introduce concepts of “bivariate ageing” for exchangeable lifetimes and to analyze mutual relations among stochastic dependence, univariate ageing, and bivariate ageing. A specific feature of such an approach dwells on the concept of semi-copula and in the extension, from copulas to semi-copulas, of properties of stochastic dependence. In this perspective, we aim to discuss some intricate aspects of conceptual character and to provide the readers with pertinent remarks from a Bayesian Statistics standpoint. In particular we will discuss the role of extensions of dependence properties. “Archimedean” models have an important role in the present framework. In the second part of the paper, the definitions of Kendall distribution and of Kendall equivalence classes will be extended to semi-copulas and related properties will be analyzed. On such a basis, we will consider the notion of “Pseudo-Archimedean” models and extend to them the analysis of the relations between the ageing notions of IFRA/DFRA-type and the dependence concepts of PKD/NKD.","PeriodicalId":43690,"journal":{"name":"Dependence Modeling","volume":"8 1","pages":"1 - 33"},"PeriodicalIF":0.7,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/demo-2020-0001","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46821595","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: 1

A new extreme value copula and new families of univariate distributions based on Freund’s exponential model 基于Freund指数模型的一种新的极值联结式和新的单变量分布族

IF 0.7

Dependence Modeling Pub Date : 2020-01-01 DOI: 10.1515/demo-2020-0018

S. Guzmics, G. Pflug

{"title":"A new extreme value copula and new families of univariate distributions based on Freund’s exponential model","authors":"S. Guzmics, G. Pflug","doi":"10.1515/demo-2020-0018","DOIUrl":"https://doi.org/10.1515/demo-2020-0018","url":null,"abstract":"Abstract The use of the exponential distribution and its multivariate generalizations is extremely popular in lifetime modeling. Freund’s bivariate exponential model (1961) is based on the idea that the remaining lifetime of any entity in a bivariate system is shortened when the other entity defaults. Such a model can be quite useful for studying systemic risk, for instance in financial systems. Guzmics and Pflug (2019) revisited Freund’s model, deriving the corresponding bivariate copula and examined some characteristics of it; furthermore, we opened the door for a multivariate setting. Now we present further investigations in the bivariate model: we compute the tail dependence coefficients, we examine the marginal and joint distributions of the componentwise maxima, which leads to an extreme value copula, which – to the best of our knowledge – has not been investigated in the literature yet. The original bivariate model of Freund has been extended to more variables by several authors. We also turn to the multivariate setting, and our focus is different from that of the previous generalizations, and therefore it is novel: examining the distribution of the sum and of the average of the lifetime variables (provided that the shock parameters are all the same) leads to new families of univariate distributions, which we call Exponential Gamma Mixture Type I and Type II (EGM) distributions. We present their basic properties, we provide asymptotics for them, and finally we also provide the limiting distribution for the EGM Type II distribution.","PeriodicalId":43690,"journal":{"name":"Dependence Modeling","volume":"8 1","pages":"330 - 360"},"PeriodicalIF":0.7,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/demo-2020-0018","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42718529","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: 1

Nonparametric relative recursive regression 非参数相对递归回归

IF 0.7

Dependence Modeling Pub Date : 2020-01-01 DOI: 10.1515/demo-2020-0013

Y. Slaoui, S. Khardani

引用次数: 0

Detecting and modeling critical dependence structures between random inputs of computer models 计算机模型随机输入之间的临界依赖结构的检测和建模

IF 0.7

Dependence Modeling Pub Date : 2020-01-01 DOI: 10.1515/demo-2020-0016

N. Benoumechiara, N. Bousquet, B. Michel, Philippe Saint-Pierre

{"title":"Detecting and modeling critical dependence structures between random inputs of computer models","authors":"N. Benoumechiara, N. Bousquet, B. Michel, Philippe Saint-Pierre","doi":"10.1515/demo-2020-0016","DOIUrl":"https://doi.org/10.1515/demo-2020-0016","url":null,"abstract":"Abstract Uncertain information on input parameters of computer models is usually modeled by considering these parameters as random, and described by marginal distributions and a dependence structure of these variables. In numerous real-world applications, while information is mainly provided by marginal distributions, typically from samples, little is really known on the dependence structure itself. Faced with this problem of incomplete or missing information, risk studies that make use of these computer models are often conducted by considering independence of input variables, at the risk of including irrelevant situations. This approach is especially used when reliability functions are considered as black-box models. Such analyses remain weakened in absence of in-depth model exploration, at the possible price of a strong risk misestimation. Considering the frequent case where the reliability output is a quantile, this article provides a methodology to improve risk assessment, by exploring a set of pessimistic dependencies using a copula-based strategy. In dimension greater than two, a greedy algorithm is provided to build input regular vine copulas reaching a minimum quantile to which a reliability admissible limit value can be compared, by selecting pairwise components of sensitive influence on the result. The strategy is tested over toy models and a real industrial case-study. The results highlight that current approaches can provide non-conservative results.","PeriodicalId":43690,"journal":{"name":"Dependence Modeling","volume":"8 1","pages":"263 - 297"},"PeriodicalIF":0.7,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/demo-2020-0016","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45277734","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: 4

Erratum regarding “Optimizing effective numbers of tests by vine copula modeling” 关于“通过藤联结模型优化有效试验数”的勘误表

IF 0.7

Dependence Modeling Pub Date : 2020-01-01 DOI: 10.1515/demo-2020-0050

Nicolle Steffen, T. Dickhaus

引用次数: 0

Bivariate box plots based on quantile regression curves 基于分位数回归曲线的二元箱形图

IF 0.7

Dependence Modeling Pub Date : 2020-01-01 DOI: 10.1515/demo-2020-0008

J. Navarro

引用次数: 4

Checkerboard copula defined by sums of random variables 由随机变量和定义的棋盘copula

IF 0.7

Dependence Modeling Pub Date : 2020-01-01 DOI: 10.1515/demo-2020-0004

V. Kuzmenko, Romel Salam, S. Uryasev

{"title":"Checkerboard copula defined by sums of random variables","authors":"V. Kuzmenko, Romel Salam, S. Uryasev","doi":"10.1515/demo-2020-0004","DOIUrl":"https://doi.org/10.1515/demo-2020-0004","url":null,"abstract":"Abstract We consider the problem of finding checkerboard copulas for modeling multivariate distributions. A checkerboard copula is a distribution with a corresponding density defined almost everywhere by a step function on an m-uniform subdivision of the unit hyper-cube. We develop optimization procedures for finding copulas defined by multiply-stochastic matrices matching available information. Two types of information are used for building copulas: 1) Spearman Rho rank correlation coefficients; 2) Empirical distributions of sums of random variables combined with empirical marginal probability distributions. To construct checkerboard copulas we solved optimization problems. The first problem maximizes entropy with constraints on Spearman Rho coefficients. The second problem minimizes some error function to match available data. We conducted a case study illustrating the application of the developed methodology using property and casualty insurance data. The optimization problems were numerically solved with the AORDA Portfolio Safeguard (PSG) package, which has precoded entropy and error functions. Case study data, codes, and results are posted at the web.","PeriodicalId":43690,"journal":{"name":"Dependence Modeling","volume":"8 1","pages":"70 - 92"},"PeriodicalIF":0.7,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/demo-2020-0004","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45078451","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

Insurance applications of dependence modeling 依赖建模的保险应用

IF 0.7

Dependence Modeling Pub Date : 2020-01-01 DOI: 10.1515/demo-2020-0005

C. Genest, M. Scherer

{"title":"Insurance applications of dependence modeling","authors":"C. Genest, M. Scherer","doi":"10.1515/demo-2020-0005","DOIUrl":"https://doi.org/10.1515/demo-2020-0005","url":null,"abstract":"Edward (Jed) Frees is an emeritus professor with the University of WisconsinMadison. He served there from 1983 to 2018, where his most recent appointment was as the Hickman Larson Chair of Actuarial Science. In addition, he currently has a fractional appointment with the Australian National University. He received his PhD in mathematical statistics from the University of North Carolina at Chapel Hill in 1983 and is the only Fellow of both the Society of Actuaries (SoA, 1986) and the American Statistical Association (1997). He has provided extensive service to the profession, including serving as the founding chairperson of the SoA Education and Research Section (1991–92), a member of the SoA Board of Directors (2005–08), a Trustee of the Actuarial Foundation (1998–2001), the Editor of the North American Actuarial Journal (2000–04), and as an actuarial representative to the Social Security Advisory Board’s Technical Panel on Methods and Assumptions (1998–2000). He has written three books (1996, 2004, 2010), edited a two-volume series on Predictive Modeling Applications in Actuarial Science, and is editing an online, open source book titled “Loss Data Analytics.” He has published extensively and won several awards for his research. He twice received the SoA’s Annual Prize for best paper published by the Society (1991, 2018), the SoA’s 1997 Edward A. Lew Award for research in modeling, the Casualty Actuarial Society’s 2010 Hachemeister Prize and 2015 ARIA Prize, and the Halmstad Prize for best paper published in the actuarial literature (1992, 1998, 1999, 2011).","PeriodicalId":43690,"journal":{"name":"Dependence Modeling","volume":"8 1","pages":"93 - 106"},"PeriodicalIF":0.7,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/demo-2020-0005","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44618813","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: 2

Optimizing effective numbers of tests by vine copula modeling 利用藤蔓联结模型优化试验有效次数

IF 0.7

Dependence Modeling Pub Date : 2020-01-01 DOI: 10.1515/demo-2020-0010

Nicolle Steffen, T. Dickhaus

{"title":"Optimizing effective numbers of tests by vine copula modeling","authors":"Nicolle Steffen, T. Dickhaus","doi":"10.1515/demo-2020-0010","DOIUrl":"https://doi.org/10.1515/demo-2020-0010","url":null,"abstract":"Abstract In the multiple testing context, we utilize vine copulae for optimizing the effective number of tests. It is well known that for the calibration of multiple tests for control of the family-wise error rate the dependencies between the marginal tests are of utmost importance. It has been shown in previous work, that positive dependencies between the marginal tests can be exploited in order to derive a relaxed Šidák-type multiplicity correction. This correction can conveniently be expressed by calculating the corresponding „effective number of tests“ for a given (global) significance level. This methodology can also be applied to blocks of test statistics so that the effective number of tests can be calculated by the sum of the effective numbers of tests for each block. In the present work, we demonstrate how the power of the multiple test can be optimized by taking blocks with high inner-block dependencies. The determination of those blocks will be performed by means of an estimated vine copula model. An algorithm is presented which uses the information of the estimated vine copula to make a data-driven choice of appropriate blocks in terms of (estimated) dependencies. Numerical experiments demonstrate the usefulness of the proposed approach.","PeriodicalId":43690,"journal":{"name":"Dependence Modeling","volume":"8 1","pages":"172 - 185"},"PeriodicalIF":0.7,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/demo-2020-0010","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43988619","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: 1