P-values for high-dimensional regression (2008)
Meinshausen, Nicolai, Meier, Lukas, Bühlmann, Peter
Assigning significance in high-dimensional regression is challenging. Most computationally efficient selection algorithms cannot guard against inclusion of noise variables. Asymptotically valid...
Estimating high-dimensional intervention effects from observational data (2008)
Maathuis, Marloes H., Kalisch, Markus, Bühlmann, Peter
We assume that we have observational data, generated from an unknown underlying directed acyclic graph (DAG) model. A DAG is typically not identifiable from observational data, but it is possible to...
We propose a functional gradient descent algorithm (FGD) for estimating volatility and conditional covariances (given the past) for very high-dimensional financial time series of asset price returns....
We propose a functional gradient descent algorithm (FGD) for estimating volatility and conditional covariances (given the past) for very high-dimensional financial time series of asset price returns....
Synchronizing multivariate financial time series (2005)
Audrino, Francesco, Bühlmann, Peter
Prices or returns of financial assets are most often collected in local times of the trading markets. The need to synchronize multivariate time series of financial prices or returns is motivated by...
Synchronizing multivariate financial time series (2005)
Audrino, Francesco, Bühlmann, Peter
Prices or returns of financial assets are most often collected in local times of the trading markets. The need to synchronize multivariate time series of financial prices or returns is motivated by...
Model Selection for Variable Length Markov Chains and Tuning the Context Algorithm
Bootstrap, zero-one loss, final prediction error, finite-memory source, FSMX model, Kullback-Leibler information, L 2 loss, optimal tree pruning, resampling, tree model,