70 citations · 372 across the 30 of their papers we have counts for
30 papers
Model Selection for Ordinary Differential Equations: a Statistical Testing Approach
Itai Dattner, Shota Gugushvili, Oleksandr Laskorunskyi
Ordinary differential equations (ODEs) are foundational in modeling intricate dynamics across a gamut of scientific disciplines. Yet, a possibility to represent a single phenomenon…
Weak solutions to gamma-driven stochastic differential equations
Denis Belomestny, Shota Gugushvili, Moritz Schauer +1
We study a stochastic differential equation driven by a gamma process, for which we give results on the existence of weak solutions under conditions on the volatility function. To…
Nonparametric Bayesian volatility estimation for gamma-driven stochastic differential equations
Denis Belomestny, Shota Gugushvili, Moritz Schauer +1
We study a nonparametric Bayesian approach to estimation of the volatility function of a stochastic differential equation driven by a gamma process. The volatility function is mode…
Decompounding discrete distributions: A non-parametric Bayesian approach
Shota Gugushvili, Ester Mariucci, Frank van der Meulen
Suppose that a compound Poisson process is observed discretely in time and assume that its jump distribution is supported on the set of natural numbers. In this paper we propose a…
Bayesian wavelet de-noising with the caravan prior
Shota Gugushvili, Frank van der Meulen, Moritz Schauer +1
According to both domain expert knowledge and empirical evidence, wavelet coefficients of real signals tend to exhibit clustering patterns, in that they contain connected regions o…
Nonparametric Bayesian volatility learning under microstructure noise
Shota Gugushvili, Frank van der Meulen, Moritz Schauer +1
In this work, we study the problem of learning the volatility under market microstructure noise. Specifically, we consider noisy discrete time observations from a stochastic differ…