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Goodness-of-fit testing in high-dimensional generalized linear models (2019).
[ArXiv]
[JRSSB]
Jana Janková, Rajen Shah, Peter Bühlmann and Richard Samworth.
Journal of the Royal Statistical Society Series B 82.3 (2020): 773-795.
The accompanying R package GRPtests is available from CRAN and GitHub.
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De-biased sparse PCA: Inference and testing for eigenstructure
of large covariance matrices (2019).
To appear in IEEE Transactions on Information Theory. [ArXiv]
Jana Janková and Sara van de Geer.
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Semi-parametric efficiency bounds and efficient estimation
for high-dimensional models.
Jana Janková and Sara van de Geer.
Annals of Statistics, 46, Number 5 (2018), 2336-2359. [Euclid] [ArXiv]
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Inference for high-dimensional graphical models.
[ArXiv]
Jana Janková and Sara van de Geer.
Handbook of Graphical Models. CRC Press, 2019. 325-348 (Editors M. Drton, M. Maathuis, S. Lauritzen, M. Wainwright)
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Honest confidence regions and optimality in high-dimensional precision matrix estimation.
[Test]
[ArXiv]
Jana Janková and Sara van de Geer.
TEST, 26, pages 143–162 (2017), 2016.
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Confidence regions for high-dimensional generalized linear
models under sparsity (2016).
[ArXiv]
Jana Janková and Sara van de Geer.
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Confidence intervals
for high-dimensional inverse covariance estimation.
[Euclid]
[ArXiv]
Jana Janková and Sara van de Geer
Electronic Journal of Statistics 9, 1205-1229, 2015.
Theses
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Asymptotic Inference in Sparse High-dimensional Models (2017).
PhD Thesis, ETH Zurich.
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Asymptotic Properties of Support Vector Machines.
MSc Thesis, Comenius University Bratislava and Vrije Universiteit Amsterdam.