62 citations · 176 across the 12 of their papers we have counts for
12 papers
A critical comparison of handling zeros in high-dimensional compositional count data
Wenqi Tang, Kamila Fačevicová, Klaus Nordhausen +1
The growing use of high-throughput sequencing (HTS) has enabled the large-scale production of compositional count data, driving progress in microbiome research. However, such count…
Identifiable Autoregressive Variational Autoencoders for Nonlinear and Nonstationary Spatio-Temporal Blind Source Separation
Mika Sipilä, Klaus Nordhausen, Sara Taskinen
The modeling and prediction of multivariate spatio-temporal data involve numerous challenges. Dimension reduction methods can significantly simplify this process, provided that the…
Independent vector analysis -- an introduction for statisticians
Miro Arvila, Klaus Nordhausen, Mika Sipilä +1
Blind source separation (BSS), particularly independent component analysis (ICA), has been widely used in various fields of science such as biomedical signal processing to recover…
Modelling multivariate spatio-temporal data with identifiable variational autoencoders
Mika Sipilä, Claudia Cappello, Sandra De Iaco +2
Modelling multivariate spatio-temporal data with complex dependency structures is a challenging task but can be simplified by assuming that the original variables are generated fro…
A Comparison of Joint Species Distribution Models for Percent Cover Data
Pekka Korhonen, Francis K. C. Hui, Jenni Niku +2
1. Joint species distribution models (JSDMs) have gained considerable traction among ecologists over the past decade, due to their capacity to answer a wide range of questions at b…
Nonlinear blind source separation exploiting spatial nonstationarity
Mika Sipilä, Klaus Nordhausen, Sara Taskinen
In spatial blind source separation the observed multivariate random fields are assumed to be mixtures of latent spatially dependent random fields. The objective is to recover laten…
Fast, universal estimation of latent variable models using extended variational approximations
Pekka Korhonen, Francis K. C. Hui, Jenni Niku +1
Generalized linear latent variable models (GLLVMs) are a class of methods for analyzing multi-response data which has garnered considerable popularity in recent years, for example,…
Stationary subspace analysis based on second-order statistics
Lea Flumian, Markus Matilainen, Klaus Nordhausen +1
In stationary subspace analysis (SSA) one assumes that the observable p-variate time series is a linear mixture of a k-variate nonstationary time series and a (p-k)-variate station…
Extracting conditionally heteroscedastic components using ICA
Jari Miettinen, Markus Matilainen, Klaus Nordhausen +1
In the independent component model, the multivariate data is assumed to be a mixture of mutually independent latent components, and in independent component analysis (ICA) the aim…
The squared symmetric FastICA estimator
Jari Miettinen, Klaus Nordhausen, Hannu Oja +2
In this paper we study the theoretical properties of the deflation-based FastICA method, the original symmetric FastICA method, and a modified symmetric FastICA method, here called…
Fourth Moments and Independent Component Analysis
Jari Miettinen, Sara Taskinen, Klaus Nordhausen +1
In independent component analysis it is assumed that the components of the observed random vector are linear combinations of latent independent random variables, and the aim is the…
Separation of uncorrelated stationary time series using autocovariance matrices
Jari Miettinen, Katrin Illner, Klaus Nordhausen +3
Blind source separation (BSS) is a signal processing tool, which is widely used in various fields. Examples include biomedical signal separation, brain imaging and economic time se…