111 citations · 555 across the 19 of their papers we have counts for
19 papers
Sliced Inverse Regression for Spatial Data
Christoph Muehlmann, Hannu Oja, Klaus Nordhausen
Sliced inverse regression is one of the most popular sufficient dimension reduction methods. Originally, it was designed for independent and identically distributed data and recent…
Notion of information and independent component analysis
Una Radojicic, Klaus Nordhausen, Hannu Oja
Partial orderings and measures of information for continuous univariate random variables with special roles of Gaussian and uniform distributions are discussed. The information mea…
Variable screening based on Gaussian Centered L-moments
Hyowon An, Kai Zhang, Hannu Oja +1
An important challenge in big data is identification of important variables. In this paper, we propose methods of discovering variables with non-standard univariate marginal distri…
Inference on the marginal distribution of clustered data with informative cluster size
Jaakko Nevalainen, Somnath Datta, Hannu Oja
In spite of recent contributions to the literature, informative cluster size settings are not well known and understood. In this paper, we give a formal definition of the problem a…
Nonparametric Analysis of Clustered Multivariate Data
Jaakko Nevalainen, Denis Larocque, Hannu Oja +1
There has been a wide interest to extend univariate and multivariate nonparametric procedures to clustered and hierarchical data. Traditionally, parametric mixed models have been u…
Independent component analysis for multivariate functional data
Joni Virta, Bing Li, Klaus Nordhausen +1
We extend two methods of independent component analysis, fourth order blind identification and joint approximate diagonalization of eigen-matrices, to vector-valued functional data…
Asymptotic and bootstrap tests for the dimension of the non-Gaussian subspace
Klaus Nordhausen, Hannu Oja, David E. Tyler +1
Dimension reduction is often a preliminary step in the analysis of large data sets. The so-called non-Gaussian component analysis searches for a projection onto the non-Gaussian pa…
Projection Pursuit for non-Gaussian Independent Components
Joni Virta, Klaus Nordhausen, Hannu Oja
In independent component analysis it is assumed that the observed random variables are linear combinations of latent, mutually independent random variables called the independent c…
Asymptotic and bootstrap tests for subspace dimension
Klaus Nordhausen, Hannu Oja, David E. Tyler
Most linear dimension reduction methods proposed in the literature can be formulated using an appropriate pair of scatter matrices, see e.g. Ye and Weiss (2003), Tyler et al. (2009…
JADE for Tensor-Valued Observations
Joni Virta, Bing Li, Klaus Nordhausen +1
Independent component analysis is a standard tool in modern data analysis and numerous different techniques for applying it exist. The standard methods however quickly lose their e…
Independent component analysis for tensor-valued data
Joni Virta, Bing Li, Klaus Nordhausen +1
In preprocessing tensor-valued data, e.g. images and videos, a common procedure is to vectorize the observations and subject the resulting vectors to one of the many methods used f…
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…
Joint Use of Third and Fourth Cumulants in Independent Component Analysis
Joni Virta, Klaus Nordhausen, Hannu Oja
The independent component model is a latent variable model where the components of the observed random vector are linear combinations of latent independent variables. The aim is to…
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…
On asymptotics of ICA estimators and their performance indices
Pauliina Ilmonen, Klaus Nordhausen, Hannu Oja +1
Independent component analysis (ICA) has become a popular multivariate analysis and signal processing technique with diverse applications. This paper is targeted at discussing theo…
Averaging orthogonal projectors
Eero Liski, Klaus Nordhausen, Hannu Oja +1
Dimensionality is a major concern in analyzing large data sets. Some well known dimension reduction methods are for example principal component analysis (PCA), invariant coordinate…
Semiparametrically efficient rank-based inference for shape II. Optimal R-estimation of shape
Marc Hallin, Hannu Oja, Davy Paindaveine
A class of R-estimators based on the concepts of multivariate signed ranks and the optimal rank-based tests developed in Hallin and Paindaveine [Ann. Statist. 34 (2006)] is propose…
Discussion of "Breakdown and groups" by P. L. Davies and U. Gather
Hannu Oja
Discussion of ``Breakdown and groups'' by P. L. Davies and U. Gather [math.ST/0508497]