Ivan Kojadinovic

A review of methods for capacity identification in Choquet integral based multi-attribute utility theory: Applications of the Kappalab R package

The application of multi-attribute utility theory whose aggregation process is based on the Choquet integral requires the prior identification of a capacity. The main approaches to capacity identification proposed in the literature are reviewed and their advantages and inconveniences are discussed. All the reviewed methods have been implemented within the Kappalab R package. Their application is illustrated on a detailed example.

A goodness-of-fit test for multivariate multiparameter copulas based on multiplier central limit theorems

Recent large scale simulations indicate that a powerful goodness-of-fit test for copulas can be obtained from the process comparing the empirical copula with a parametric estimate of the copula derived under the null hypothesis. A first way to compute approximate p-values for statistics derived from this process consists of using the parametric bootstrap procedure recently thoroughly revisited by Genest and Rémillard. Because it heavily relies on random number generation and estimation, the resulting goodness-of-fit test has a very high computational cost that can be regarded as an obstacle to its application as the sample size increases. An alternative approach proposed by the authors consists of using a multiplier procedure. The study of the finite-sample performance of the multiplier version of the goodness-of-fit test for bivariate one-parameter copulas showed that it provides a valid alternative to the parametric bootstrap-based test while being orders of magnitude faster. The aim of this work is to extend the multiplier approach to multivariate multiparameter copulas and study the finite-sample performance of the resulting test. Particular emphasis is put on elliptical copulas such as the normal and the t as these are flexible models in a multivariate setting. The implementation of the procedure for the latter copulas proves challenging and requires the extension of the Plackett formula for the t distribution to arbitrary dimension. Extensive Monte Carlo experiments, which could be carried out only because of the good computational properties of the multiplier approach, confirm in the multivariate multiparameter context the satisfactory behavior of the goodness-of-fit test.

Minimum variance capacity identification

Abstract In the framework of multi-criteria decision making whose aggregation process is based on the Choquet integral, we present a maximum entropy like method enabling to determine, if it exists, the “least specific” capacity compatible with the initial preferences of the decision maker. The proposed approach consists in solving a strictly convex quadratic program whose objective function is equivalently either the opposite of a generalized entropy measure or the variance of the capacity. The application of the proposed approach is illustrated on two examples.

An axiomatic approach to the definition of the entropy of a discrete Choquet capacity

To extend the classical Shannon entropy to nonadditive measures, Marichal recently introduced the concept of generalized entropy for discrete Choquet capacities. We provide a first axiomatization of this new concept on the basis of three axioms: the symmetry property, a boundary condition for which the entropy reduces to the Shannon entropy, and a generalized version of the well-known recursivity property. We also show that this generalized entropy fulfills several properties considered as requisites for defining an entropy-like measure. Lastly, we provide an interpretation of it in the framework of aggregation by the discrete Choquet integral.

Axiomatic characterizations of probabilistic and cardinal-probabilistic interaction indices

Abstract In the framework of cooperative game theory, the concept of interaction index, which can be regarded as an extension of that of value, has been recently proposed to measure the interaction phenomena among players. Axiomatizations of two classes of interaction indices, namely probabilistic interaction indices and cardinal-probabilistic interaction indices, generalizing probabilistic values and semivalues, respectively, are first proposed. The axioms we utilize are based on natural generalizations of axioms involved in the axiomatizations of values. In the second half of the paper, existing instances of cardinal-probabilistic interaction indices encountered thus far in the literature are also axiomatized.

Relevance measures for subset variable selection in regression problems based on k-additive mutual information

In the framework of subset variable selection for regression, relevance measures based on the notion of mutual information are studied. Results on the estimation of this index of stochastic dependence in a continuous setting are first presented. They are grounded on kernel density estimation which makes the overall estimation of the mutual information quadratic. The behavior of the mutual information as a relevance measure is then empirically studied on several regression problems. The considered problems are artificially generated to contain irrelevant and redundant candidate explanatory variables as well as strongly nonlinear relationships. Next, still in a subset variable selection context, computationally more efficient approximations of the mutual information based on the notion of k-additive truncation are proposed. The 2- and 3-additive truncations appear to be of practical interest as relevance measures. The 2-additive truncation is based on the computation of the approximate relevance of a set of potential predictors from the relevance values of the singletons and pairs it contains. The 3-additive truncation additionally involves the relevance values of the 3-element subsets. The lower the amount of redundancy among the candidate explanatory variables, the better these approximations. The sample behavior of the two resulting relevance measures is finally empirically studied on the previously generated nonlinear artificial regression problems.

Nonparametric rank-based tests of bivariate extreme-value dependence

A new class of tests of extreme-value dependence for bivariate copulas is proposed. It is based on the process comparing the empirical copula with a natural nonparametric rank-based estimator of the unknown copula under extreme-value dependence. A multiplier technique is used to compute approximate p-values for several candidate test statistics. Extensive Monte Carlo experiments were carried out to compare the resulting procedures with the tests of extreme-value dependence recently studied in Ben Ghorbal et al. (2009) [1] and Kojadinovic and Yan (2010) [19]. The finite-sample performance study of the tests is complemented by local power calculations.

Nonparametric tests for change-point detection à la Gombay and Horváth

The nonparametric test for change-point detection proposed by Gombay and Horvath is revisited and extended in the broader setting of empirical process theory. The resulting testing procedure for potentially multivariate observations is based on a sequential generalization of the functional multiplier central limit theorem and on modifications of Gombay and Horvaths seminal approach that appears to improve the finite-sample behavior of the tests. A large number of candidate test statistics based on processes indexed by lower-left orthants and half-spaces are considered and their performance is studied through extensive Monte Carlo experiments involving univariate, bivariate and trivariate data sets. Finally, practical recommendations are provided and the tests are illustrated on trivariate hydrological data.

A dependent multiplier bootstrap for the sequential empirical copula process under strong mixing

Two key ingredients to carry out inference on the copula of multivariate observations are the empirical copula process and an appropriate resampling scheme for the latter. Among the existing techniques used for i.i.d. observations, the multiplier bootstrap of Remillard and Scaillet (2009) frequently appears to lead to inference procedures with the best finite-sample properties. Bucher and Ruppert (2013) recently proposed an extension of this technique to strictly stationary strongly mixing observations by adapting the dependent multiplier bootstrap of Buhlmann (1993, Section 3.3) to the empirical copula process. The main contribution of this work is a generalization of the multiplier resampling scheme proposed by Bucher and Ruppert (2013) along two directions. First, the resampling scheme is now genuinely sequential, thereby allowing to transpose to the strongly mixing setting all of the existing multiplier tests on the unknown copula, including nonparametric tests for change-point detection. Second, the resampling scheme is now fully automatic as a data-adaptive procedure is proposed which can be used to estimate the bandwidth (block length) parameter. A simulation study is used to investigate the nitesample performance of the resampling scheme and provides suggestions on how to choose several additional parameters. As by-products of this work, the weak convergence of the sequential empirical copula process is obtained under many serial dependence conditions, and the validity of a sequential version of the dependent multiplier bootstrap for empirical processes of Buhlmann is obtained under weaker conditions on the strong mixing coecients and the multipliers.

Detecting changes in cross-sectional dependence in multivariate time series

Classical and more recent tests for detecting distributional changes in multivariate time series often lack power against alternatives that involve changes in the cross-sectional dependence structure. To be able to detect such changes better, a test is introduced based on a recently studied variant of the sequential empirical copula process. In contrast to earlier attempts, ranks are computed with respect to relevant subsamples, with beneficial consequences for the sensitivity of the test. For the computation of p -values we propose a multiplier resampling scheme that takes the serial dependence into account. The large-sample theory for the test statistic and the resampling scheme is developed. The finite-sample performance of the procedure is assessed by Monte Carlo simulations. Two case studies involving time series of financial returns are presented as well.