16 citations · 31 across the 2 of their papers we have counts for
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stat.ME2019
Goodness-of-fit testing in high-dimensional generalized linear models
Jana Janková, Rajen D. Shah, Peter Bühlmann +1
We propose a family of tests to assess the goodness-of-fit of a high-dimensional generalized linear model. Our framework is flexible and may be used to construct an omnibus test or…
stat.ME2018★ 15 cited
De-biased sparse PCA: Inference and testing for eigenstructure of large covariance matrices
Jana Janková, Sara van de Geer
Sparse principal component analysis (sPCA) has become one of the most widely used techniques for dimensionality reduction in high-dimensional datasets. The main challenge underlyin…
stat.ME2016★ 16 cited
Confidence regions for high-dimensional generalized linear models under sparsity
Jana Janková, Sara van de Geer
We study asymptotically normal estimation and confidence regions for low-dimensional parameters in high-dimensional sparse models. Our approach is based on the -penalized M…