8 citations · 19 across the 15 of their papers we have counts for
26 papers
Fast Forecasting of Unstable Data Streams for On-Demand Service Platforms
Yu Jeffrey Hu, Jeroen Rombouts, Ines Wilms
On-demand service platforms face a challenging problem of forecasting a large collection of high-frequency regional demand data streams that exhibit instabilities. This paper devel…
Sparse High-Dimensional Vector Autoregressive Bootstrap
Robert Adamek, Stephan Smeekes, Ines Wilms
We introduce a high-dimensional multiplier bootstrap for time series data based on capturing dependence through a sparsely estimated vector autoregressive model. We prove its consi…
Hierarchical Regularizers for Reverse Unrestricted Mixed Data Sampling Regressions
Alain Hecq, Marie Ternes, Ines Wilms
Reverse Unrestricted MIxed DAta Sampling (RU-MIDAS) regressions are used to model high-frequency responses by means of low-frequency variables. However, due to the periodic structu…
Principal Balances of Compositional Data for Regression and Classification using Partial Least Squares
V. Nesrstová, I. Wilms, J. Palarea-Albaladejo +4
High-dimensional compositional data are commonplace in the modern omics sciences amongst others. Analysis of compositional data requires a proper choice of orthonormal coordinate r…
The Influence Function of Graphical Lasso Estimators
Gaëtan Louvet, Jakob Raymaekers, Germain Van Bever +1
The precision matrix that encodes conditional linear dependency relations among a set of variables forms an important object of interest in multivariate analysis. Sparse estimation…
Local Projection Inference in High Dimensions
Robert Adamek, Stephan Smeekes, Ines Wilms
In this paper, we estimate impulse responses by local projections in high-dimensional settings. We use the desparsified (de-biased) lasso to estimate the high-dimensional local pro…