75 citations · 165 across the 31 of their papers we have counts for
12 papers · 1 filter
Parameter estimation in generalized fractional neuronal models
Pauliina Ilmonen, Milla Laurikkala, Enrica Pirozzi +2
We investigate a generalized stochastic fractional neuronal model combining fractional dynamics with correlated stochastic inputs. The proposed framework is described by a fraction…
Non-parametric estimation of non-linear diffusion coefficient in parabolic SPDEs
Martin Andersson, Benny Avelin, Valentin Garino +2
In this article, we introduce a novel non-parametric predictor, based on conditional expectation, for the unknown diffusion coefficient function in the stochastic partial diffe…
Moment Estimator-Based Extreme Quantile Estimation with Erroneous Observations: Application to Elliptical Extreme Quantile Region Estimation
Jaakko Pere, Pauliina Ilmonen, Lauri Viitasaari
In many application areas of extreme value theory, the variables of interest are not directly observable but instead contain errors. In this article, we quantify the effect of thes…
On the Impact of Approximation Errors on Extreme Quantile Estimation with Applications to Functional Data Analysis
Jaakko Pere, Benny Avelin, Valentin Garino +2
We study the effect of approximation errors in assessing the extreme behavior of heavy-tailed random objects. We give conditions for the approximation error such that the standard…
On optimal prediction of missing functional data with memory
Pauliina Ilmonen, Nourhan Shafik, Tommi Sottinen +2
This paper considers the problem of reconstructing missing parts of functions based on their observed segments. It provides, for Gaussian processes and arbitrary bijective transfor…
Latent Model Extreme Value Index Estimation
Joni Virta, Niko Lietzén, Lauri Viitasaari +1
We propose a novel strategy for multivariate extreme value index estimation. In applications such as finance, volatility and risk present in the components of a multivariate time s…
Modeling temporally uncorrelated components for complex-valued stationary processes
Niko Lietzén, Lauri Viitasaari, Pauliina Ilmonen
We consider a complex-valued linear mixture model, under discrete weakly stationary processes. We recover latent components of interest, which have undergone a linear mixing. We st…
Vector-valued Generalised Ornstein-Uhlenbeck Processes
Marko Voutilainen, Lauri Viitasaari, Pauliina Ilmonen +2
Generalisations of the Ornstein-Uhlenbeck process defined through Langevin equation such as fractional Ornstein-Uhlenbeck processes, have recently receiv…
Fast tensorial JADE
Joni Virta, Niko Lietzén, Pauliina Ilmonen +1
In this work, we propose a novel method for tensorial independent component analysis. Our approach is based on TJADE and -JADE, two recently proposed generalizations of the cl…
On model fitting and estimation of strictly stationary processes
Marko Voutilainen, Lauri Viitasaari, Pauliina Ilmonen
Stationary processes have been extensively studied in the literature. Their applications include modeling and forecasting numerous real life phenomena such as natural disasters, sa…
On Asymptotic Properties of the Separating Hill Estimator
Matias Heikkilä, Yves Dominicy, Pauliina Ilmonen
Modeling and understanding multivariate extreme events is challenging, but of great importance in various applications - e.g. in biostatistics, climatology, and finance. The separa…
Semiparametrically efficient inference based on signed ranks in symmetric independent component models
Pauliina Ilmonen, Davy Paindaveine
We consider semiparametric location-scatter models for which the -variate observation is obtained as , where is a -vector, is a full-rank matrix a…