Zobrazeno 1 - 10
of 225
pro vyhledávání: '"Hofert, Marius"'
An approach to amputation, the process of introducing missing values to a complete dataset, is presented. It allows to construct missingness indicators in a flexible and principled way via copulas and Bernoulli margins and to incorporate dependence i
Externí odkaz:
http://arxiv.org/abs/2407.18572
Weak convergence of maxima of dependent sequences of identically distributed continuous random variables is studied under normalizing sequences arising as subsequences of the normalizing sequences from an associated iid sequence. This general framewo
Externí odkaz:
http://arxiv.org/abs/2405.02833
The class of index-mixed copulas is introduced and its properties are investigated. Index-mixed copulas are constructed from given base copulas and a random index vector, and show a rather remarkable degree of analytical tractability. The analytical
Externí odkaz:
http://arxiv.org/abs/2306.10663
Neural networks are suggested for learning a map from $d$-dimensional samples with any underlying dependence structure to multivariate uniformity in $d'$ dimensions. This map, termed DecoupleNet, is used for dependence model assessment and selection.
Externí odkaz:
http://arxiv.org/abs/2202.03406
A fully nonparametric approach for making probabilistic predictions in multi-response regression problems is introduced. Random forests are used as marginal models for each response variable and, as novel contribution of the present work, the depende
Externí odkaz:
http://arxiv.org/abs/2112.03377
In many stochastic problems, the output of interest depends on an input random vector mainly through a single random variable (or index) via an appropriate univariate transformation of the input. We exploit this feature by proposing an importance sam
Externí odkaz:
http://arxiv.org/abs/2111.07542
The smooth bootstrap for estimating copula functionals in small samples is investigated. It can be used both to gauge the distribution of the estimator in question and to augment the data. Issues arising from kernel density and distribution estimatio
Externí odkaz:
http://arxiv.org/abs/2110.03397
Autor:
Koike, Takaaki, Hofert, Marius
Publikováno v:
In Journal of Multivariate Analysis May 2024 201
Quantifying tail dependence is an important issue in insurance and risk management. The prevalent tail dependence coefficient (TDC), however, is known to underestimate the degree of tail dependence and it does not capture non-exchangeable tail depend
Externí odkaz:
http://arxiv.org/abs/2101.12262