Sparse Causal Discovery in Multivariate Time Series (2009)
Stefan Haufe, Klaus-robert Müller, Guido Nolte, Nicole Krämer, Isabelle Guyon, Dominik Janzing, ...
Our goal is to estimate causal interactions in multivariate time series. Using vector autoregressive (VAR) models, these can be defined based on non-vanishing coefficients belonging to respective...
Comparison of Granger Causality and Phase Slope Index (2009)
Guido Nolte, Andreas Ziehe, Nicole Krämer, Florin Popescu, Klaus-robert Müller, Isabelle Guyon, ...
We recently proposed a new measure, termed Phase Slope Index (PSI), It estimates the causal direction of interactions robustly with respect to instantaneous mixtures of independent sources with...
Partial Least Squares Regression for Graph Mining (2009)
Hiroto Saigo, Nicole Krämer, Koji Tsuda
Attributed graphs are increasingly more common in many application domains such as chemistry, biology and text processing. A central issue in graph mining is how to collect informative subgraph...
Krämer, Nicole, Schäfer, Juliane, Boulesteix, Anne-Laure
Graphical Gaussian models are popular tools for the estimation of (undirected) gene association networks from microarray data. A key issue when the number of variables greatly exceeds the number of...
Lanczos Approximations for the Speedup of Kernel Partial Least Squares Regression (2009)
Krämer, Nicole, Sugiyama, Masashi, Braun, Mikio
The runtime for Kernel Partial Least Squares (KPLS) to compute the fit is quadratic in the number of examples. However, the necessity of obtaining sensitivity measures as degrees of freedom for model...
Comparison of Granger Causality and Phase Slope Index (2009)
Nolte, Guido, Ziehe, Andreas, Krämer, Nicole, Poupescu, Florin, Müller, Klaus-Robert
We recently proposed a new measure, termed Phase Slope Index (PSI), to estimate the causal direction of interactions designed to be robust to instantaneous mixtures of independent sources with...
The Feature Importance Ranking Measure (2009)
Zien, Alexander, Krämer, Nicole, Sonnenburg, Sören, Rätsch, Gunnar
Most accurate predictions are typically obtained by learning machines with complex feature spaces (e.g., as induced by kernels). Unfortunately, such decision rules are hardly accessible to humans and...
Sparse Causal Discovery in Multivariate Time Series (2008)
Haufe, Stefan, Müller, Klaus-Robert, Nolte, Guido, Krämer, Nicole
Our goal is to estimate causal interactions in multivariate time series. Using vector autoregressive (VAR) models, these can be defined based on non-vanishing coefficients belonging to respective...
Partial Least Squares Regression for Graph Mining (2008)
Saigo, Hiroto, Krämer, Nicole, Tsuda, Koji
Attributed graphs are increasingly more common in many application domains such as chemistry, biology and text processing. A central issue in graph mining is how to collect informative subgraph...
Comments on: "Augmenting the Bootstrap to Analyze High-Dimensional Genomic Data" (2008)
Boulesteix, Anne-Laure, Kondylis, Athanassios, Krämer, Nicole
This is an invited discussion item.
Regularized Estimation of Large Scale Gene Regulatory Networks (2008)
Nicole Krämer, Juliane Schäfer, Anne-laure Boulesteix
When dealing with graphical Gaussian models for gene regulatory networks, the major problem is to compute the matrix of partial correlations. Based on the close connection between partial...
Krämer, Nicole, Boulesteix, Anne-Laure, Tutz, Gerhard
We propose a novel framework that combines penalization techniques with Partial Least Squares (PLS). We focus on two important applications. (1) We combine PLS with a roughness penalty to estimate...
Robustly estimating the flow direction of information in complex physical systems (2008)
Nolte, Guido, Ziehe, Andreas, Nikulin, Vadim, Schlögl, Alois, Krämer, Nicole, Brismar, Tom, ...
We propose a new measure (phase-slope index) to estimate the direction of information flux in multivariate time series. This measure (a) is insensitive to mixtures of independent sources, (b) gives...
Robustly estimating the flow direction of information in complex physical systems (2007)
Nolte, Guido, Ziehe, Andreas, Nikulin, Vadim V., Schlögl, Alois, Krämer, Nicole, Brismar, Tom, ...
We propose a new measure to estimate the direction of information flux in multivariate time series from complex systems. This measure, based on the slope of the phase spectrum (Phase Slope Index) has...
Robustly estimating the flow direction of information in complex physical systems (2007)
Nolte, Guido, Ziehe, Andreas, Nikulin, Vadim, Schlögl, Alois, Krämer, Nicole, Brismar, Tom, ...
We propose a new measure to estimate the direction of information flux in multivariate time series from complex systems. This measure, based on the slope of the phase spectrum (Phase Slope Index) has...
ppls: penalized partial least squares (2007)
Krämer, Nicole, Boulesteix, Anne-Laure
This package contains functions to estimate linear and nonlinear regression methods with Penalized Partial Least Squares. Partial Leasts Squares (PLS) is a regression method that constructs latent...
Kernelizing PLS, Degrees of Freedom, and Efficient Model Selection (2007)
Krämer, Nicole, Braun, Mikio L.
Kernelizing partial least squares (PLS), an algorithm which has been particularly popular in chemometrics, leads to kernel PLS which has several interesting properties, including a sub-cubic runtime...
An Overview on the Shrinkage Properties of Partial Least Squares Regression (2007)
The aim of this paper is twofold. In the first part, we recapitulate the main results regarding the shrinkage properties of partial least squares (PLS) regression. In particular, we give an...
Krämer, Nicole, Boulesteix, Anne-Laure, Tutz, Gerhard
We propose a novel framework that combines penalization with Partial Least Squares (PLS). Starting with a generalized additive model, we expand each additive component in terms of a generous amount...
Analysis of High-Dimensional Data with Partial Least Squares and Boosting (2007)
The crucial task in the statistical analysis of high-dimensional data is to model relationships between a large amount p of variables based on a small number n of observations.The high dimensionality...
Trepte, Sabine, Krämer, Nicole
In this paper we propose that Tajfels (1979) social identity theory (SIT) and self-categorization theory (SCT, Turner, Brown & Tajfel, 1987) is a relevant and helpful theoretical groundwork to...
Kernelizing PLS, degrees of freedom, and efficient model selection (2007)
Kernelizing partial least squares (PLS), an algorithm which has been particularly popular in chemometrics, leads to kernel PLS which has several interesting properties, including a sub-cubic runtime...
Overview and recent advances in partial least squares (2006)
Partial Least Squares (PLS) is a wide class of methods for modeling relations between sets of observed variables by means of latent variables. It comprises of regression and classification tasks as...
An overview on the shrinkage properties of partial least squares regression
Linear regression, Biased estimators, Mean squared error,