Zobrazeno 1 - 10
of 610
pro vyhledávání: '"Filzmoser, Peter"'
Autor:
Neubauer, Lukas, Filzmoser, Peter
A novel framework for hierarchical forecast updating is presented, addressing a critical gap in the forecasting literature. By assuming a temporal hierarchy structure, the innovative approach extends hierarchical forecast reconciliation to effectivel
Externí odkaz:
http://arxiv.org/abs/2411.01528
We address the challenge of correlated predictors in high-dimensional GLMs, where regression coefficients range from sparse to dense, by proposing a data-driven random projection method. This is particularly relevant for applications where the number
Externí odkaz:
http://arxiv.org/abs/2410.00971
A first proposal of a sparse and cellwise robust PCA method is presented. Robustness to single outlying cells in the data matrix is achieved by substituting the squared loss function for the approximation error by a robust version. The integration of
Externí odkaz:
http://arxiv.org/abs/2408.15612
Sparse and outlier-robust Principal Component Analysis (PCA) has been a very active field of research recently. Yet, most existing methods apply PCA to a single dataset whereas multi-source data-i.e. multiple related datasets requiring joint analysis
Externí odkaz:
http://arxiv.org/abs/2407.16299
Autor:
Neubauer, Lukas, Filzmoser, Peter
Forecast reconciliation has become a prominent topic in recent forecasting literature, with a primary distinction made between cross-sectional and temporal hierarchies. This work focuses on temporal hierarchies, such as aggregating monthly time serie
Externí odkaz:
http://arxiv.org/abs/2407.02367
This work introduces the Matrix Minimum Covariance Determinant (MMCD) method, a novel robust location and covariance estimation procedure designed for data that are naturally represented in the form of a matrix. Unlike standard robust multivariate es
Externí odkaz:
http://arxiv.org/abs/2403.03975
We examine the linear regression problem in a challenging high-dimensional setting with correlated predictors where the vector of coefficients can vary from sparse to dense. In this setting, we propose a combination of probabilistic variable screenin
Externí odkaz:
http://arxiv.org/abs/2312.00130
Although robust statistical estimators are less affected by outlying observations, their computation is usually more challenging. This is particularly the case in high-dimensional sparse settings. The availability of new optimization procedures, main
Externí odkaz:
http://arxiv.org/abs/2311.17563
Compositional data are characterized by the fact that their elemental information is contained in simple pairwise logratios of the parts that constitute the composition. While pairwise logratios are typically easy to interpret, the number of possible
Externí odkaz:
http://arxiv.org/abs/2311.13911