36 citations · 80 across the 15 of their papers we have counts for
21 papers
Inference for heavy-tailed data with Gaussian dependence
Bikramjit Das
We consider a model for multivariate data with heavy-tailed marginal distributions and a Gaussian dependence structure. The different marginals in the model are allowed to have non…
On heavy-tailed risks under Gaussian copula: the effects of marginal transformation
Bikramjit Das, Vicky Fasen-Hartmann
In this paper, we compute multivariate tail risk probabilities where the marginal risks are heavy-tailed and the dependence structure is a Gaussian copula. The marginal heavy-taile…
Aggregating heavy-tailed random vectors: from finite sums to Lévy processes
Bikramjit Das, Vicky Fasen-Hartmann
The tail behavior of aggregates of heavy-tailed random vectors is known to be determined by the so-called principle of "one large jump'', be it for finite sums, random sums, or, Lé…
Asymptotic Behavior of Common Connections in Sparse Random Networks
Bikramjit Das, Tiandong Wang, Gengling Dai
Random network models generated using sparse exchangeable graphs have provided a mechanism to study a wide variety of complex real-life networks. In particular, these models help w…
Growth of Common Friends in a Preferential Attachment Model
Bikramjit Das, Souvik Ghosh
The number of common friends (or connections) in a graph is a commonly used measure of proximity between two nodes. Such measures are used in link prediction algorithms and recomme…
Tail probabilities of random linear functions of regularly varying random vectors
Bikramjit Das, Vicky Fasen-Hartmann, Claudia Klüppelberg
We provide a new extension of Breiman's Theorem on computing tail probabilities of a product of random variables to a multivariate setting. In particular, we give a complete charac…