Sara van de Geer

Q: What Schools Are Affiliated With Sara van de Geer

Sara van de Geer is affiliated with the following schools: Leiden University, University of Amsterdam, University of Bristol, ETH Zurich, Utrecht University

Sara van de Geer's AcademicInfluence.com Rankings

Sara van de Geer

Mathematics

#3813

World Rank

#5540

Historical Rank

Statistics

#271

World Rank

#330

Historical Rank

mathematics Degrees

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Mathematics

Sara van de Geer's Degrees

PhD Mathematics University of Amsterdam

Why Is Sara van de Geer Influential?

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According to Wikipedia, Sara Anna van de Geer is a Dutch statistician who is a professor in the department of mathematics at ETH Zurich. She is the daughter of psychologist John P. van de Geer. Education She earned a master's degree in 1982 and a doctorate in mathematics in 1987 from Leiden University. Her dissertation, entitled Regression Analysis and Empirical Processes, was supervised by Willem Rutger van Zwet and Richard D. Gill.

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Sara van de Geer's Published Works

Number of citations in a given year to any of this author's works

Total number of citations to an author for the works they published in a given year. This highlights publication of the most important work(s) by the author

Published Works

The group lasso for logistic regression (2008) (1648)
Statistics for High-Dimensional Data: Methods, Theory and Applications (2011) (1597)
Statistics for High-Dimensional Data (2011) (1255)
A global test for groups of genes: testing association with a clinical outcome (2004) (1037)
On asymptotically optimal confidence regions and tests for high-dimensional models (2013) (968)
HIGH-DIMENSIONAL GENERALIZED LINEAR MODELS AND THE LASSO (2008) (726)
On the conditions used to prove oracle results for the Lasso (2009) (706)
High-dimensional additive modeling (2008) (454)
Empirical Processes in M-Estimation (2000) (373)
The relativity of utility : Evidence from panel data (1985) (357)
Oracle Inequalities and Optimal Inference under Group Sparsity (2010) (355)
Locally adaptive regression splines (1997) (333)
ℓ1-penalization for mixture regression models (2010) (291)
Taking Advantage of Sparsity in Multi-Task Learning (2009) (275)
Applications of empirical process theory (2000) (257)
Testing against a high dimensional alternative (2006) (238)
Hellinger-Consistency of Certain Nonparametric Maximum Likelihood Estimators (1993) (205)
Regularization in statistics (2006) (201)
Estimating a Regression Function (1990) (195)
Correlated variables in regression: Clustering and sparse estimation (2012) (178)
Confidence intervals for high-dimensional inverse covariance estimation (2014) (167)
Ecole d'été de probabilités de Saint-Flour XLV (2016) (156)
Penalized quasi-likelihood estimation in partial linear models (1997) (141)
The adaptive and the thresholded Lasso for potentially misspecified models (2010) (129)
Exponential Inequalities for Martingales, with Application to Maximum Likelihood Estimation for Counting Processes (1995) (114)
Least Squares Estimation (2005) (105)
Confidence sets in sparse regression (2012) (103)
The Smooth-Lasso and other ℓ1+ℓ2-penalized methods (2011) (96)
$\ell_0$-penalized maximum likelihood for sparse directed acyclic graphs (2012) (83)
Adaptive Lasso for High Dimensional Regression and Gaussian Graphical Modeling (2009) (82)
The Bernstein–Orlicz norm and deviation inequalities (2011) (81)
Square root penalty: Adaptation to the margin in classification and in edge estimation (2005) (75)
Honest confidence regions and optimality in high-dimensional precision matrix estimation (2015) (66)
Nemirovski's Inequalities Revisited (2008) (61)
Weakly decomposable regularization penalties and structured sparsity (2012) (60)
High-dimensional inference in misspecified linear models (2015) (59)
Classifiers of support vector machine type with \ell1 complexity regularization (2006) (58)
A New Approach to Least-Squares Estimation, with Applications (1986) (54)
On Hoeffding's Inequality for Dependent Random Variables (2002) (54)
The Lasso, correlated design, and improved oracle inequalities (2011) (54)
Rates of convergence for the maximum likelihood estimator in mixture models (1996) (50)
The Impact of Changes in Income and Family Composition on Subjective Measures of Well-Being (1985) (45)
The likelihood ratio test for the change point problem for exponentially distributed random variables (1987) (43)
Inference in High-Dimensional Graphical Models (2018) (42)
Estimating Multiplicative and Additive Hazard Functions by Kernel Methods (2001) (40)
Lectures on Empirical Processes: Theory and Statistical Applications (2007) (40)
The Smooth-Lasso and other l 1 + l 2-penalized methods (2012) (38)
Adaptive estimation with soft thresholding penalties (2002) (36)
The Partial Linear Model in High Dimensions (2013) (35)
On the uniform convergence of empirical norms and inner products, with application to causal inference (2013) (33)
Robust low-rank matrix estimation (2016) (32)
New concentration inequalities for suprema of empirical processes (2011) (32)
Semiparametric efficiency bounds for high-dimensional models (2016) (31)
Convergence Rates for Penalized Least Squares Estimators in PDE Constrained Regression Problems (2018) (30)
Asymptotic Normality in Mixture Models (1997) (29)
On higher order isotropy conditions and lower bounds for sparse quadratic forms (2014) (29)
Statistics for big data: A perspective (2018) (27)
Sharp Oracle Inequalities for Square Root Regularization (2015) (27)
$χ^2$-confidence sets in high-dimensional regression (2016) (26)
Asymptotic theory for maximum likelihood in nonparametric mixture models (2003) (26)
Consistency for the least squares estimator in nonparametric regression (1996) (26)
Quasi-Likelihood and/or Robust Estimation in High Dimensions (2012) (26)
On Concentration for (Regularized) Empirical Risk Minimization (2015) (25)
M-estimation using penalties or sieves (2002) (25)
Regression analysis and empirical processes (1988) (25)
Estimation and Testing Under Sparsity: École d'Été de Probabilités de Saint-Flour XLV – 2015 (2016) (23)
Adaptivity of Support Vector Machines with ` 1 Penalty (2004) (22)
Confidence regions for high-dimensional generalized linear models under sparsity (2016) (21)
Rejoinder: ℓ1-penalization for mixture regression models (2010) (20)
Censored linear model in high dimensions (2014) (18)
Probabilistic analysis of the minimum weighted flowtime scheduling problem (1992) (18)
On the total variation regularized estimator over a class of tree graphs (2018) (17)
Prediction bounds for higher order total variation regularized least squares (2019) (17)
De-biased sparse PCA: Inference and testing for eigenstructure of large covariance matrices (2018) (16)
Regularization in statistics: Discussion (2006) (15)
A Framework for the Construction of Upper Bounds on the Number of Affine Linear Regions of ReLU Feed-Forward Neural Networks (2018) (15)
Worst possible sub-directions in high-dimensional models (2014) (14)
On rates of convergence and asymptotic normality in the multiknapsack problem (1991) (14)
Adaptive quantile regression (2003) (14)
The Smooth-Lasso and other $\ell_1+\ell_2$-penalized methods (2010) (14)
Generic chaining and the ℓ1-penalty (2012) (14)
Statistical Theory for High-Dimensional Models (2014) (13)
On non-asymptotic bounds for estimation in generalized linear models with highly correlated design (2007) (13)
On Tight Bounds for the Lasso (2018) (13)
Sparse spectral estimation with missing and corrupted measurements (2018) (12)
Synthesis and analysis in total variation regularization (2019) (12)
Asymptotic Confidence Regions for High-Dimensional Structured Sparsity (2017) (12)
Penalized least squares estimation in the additive model with different smoothness for the components (2014) (11)
Semi-parametric efficiency bounds and efficient estimation for high-dimensional models (2016) (11)
ℓ1-regularization in High-dimensional Statistical Models (2011) (11)
Sharp Oracle Inequalities for Stationary Points of Nonconvex Penalized M-Estimators (2018) (11)
Asymptotics in Empirical Risk Minimization (2005) (11)
On threshold-based classification rules (2003) (10)
Concentration behavior of the penalized least squares estimator (2015) (10)
Adaptive Rates for Total Variation Image Denoising. (2020) (10)
The group Lasso (2011) (10)
Classiﬁers of support vector machine type with ‘ 1 complexity regularization (2006) (9)
Prediction and variable selection with the adaptive Lasso (2010) (9)
On the asymptotic variance of the debiased Lasso (2019) (9)
Lasso for linear models (2011) (8)
Selected works of Willem van Zwet (2012) (7)
On robust recursive nonparametric curve estimation. (2000) (7)
Theory for the Lasso (2011) (7)
On the robustness of minimum-norm interpolators (2021) (7)
General oracle inequalities for model selection (2009) (6)
Oracle inequalities for square root analysis estimators with application to total variation penalties (2019) (6)
De-Biased Sparse PCA: Inference for Eigenstructure of Large Covariance Matrices (2021) (6)
On the efficiency of the de-biased Lasso (2017) (6)
Oracle inequalities for image denoising with total variation regularization (2019) (6)
On the total variation regularized estimator over the branched path graph (2018) (5)
Nemirovskis Inequalities Revisited (2010) (5)
Logistic regression with total variation regularization (2020) (5)
Estimation: An Overview (2011) (5)
Tensor denoising with trend filtering (2021) (4)
Asymptotic normality of minimum $L_ 1$-norm estimators in linear regression (1988) (4)
On the robustness of minimum norm interpolators and regularized empirical risk minimizers (2020) (4)
AdaBoost and robust one-bit compressed sensing (2021) (4)
Discussion: "A significance test for the lasso" (2014) (4)
A Moment Bound for Multi-hinge Classifiers (2008) (3)
Non-convex loss functions and ℓ 1 -regularization (2011) (3)
High-dimensional data: p >> n in mathematical statistics and bio-medical applications (2004) (3)
The Lasso with within group structure (2010) (3)
Generalized linear models and the Lasso (2011) (2)
Variable selection with the Lasso (2011) (2)
Classifiers of support vector machine type with ` 1 complexity regularization 1 (2)
Some exercises with the Lasso and its compatibility constant (2017) (1)
Honest confidence regions and optimality in high-dimensional precision matrix estimation (2016) (1)
Censored linear model in high dimensions Penalised linear regression on high-dimensional data with left censored response variable (1)
Boosting and greedy algorithms (2011) (1)
Estimation: General Aspects† (2014) (1)
The mathematical work of Evarist Giné (2016) (1)
Probabilistic Techniques in Modern Statistics (2015) (1)
Theory for ℓ 1 /ℓ 2 -penalty procedures (2011) (1)
Uitkeringen, armoede en welvaart (1985) (1)
Discussion of ``2004 IMS Medallion Lecture: Local Rademacher complexities and oracle inequalities in risk minimization'' by V. Koltchinskii (2007) (1)
Local adaptive regression splines (1997) (1)
Symmetrization, Contraction and Concentration (2016) (1)
High-dimensional statistics, sparsity and inference (2013) (1)
A note on rates of convergence in least squares estimation (1986) (1)
Minimum 𝓁1-norm interpolation via basis pursuit is robust to errors (2020) (1)
Probability and moment inequalities (2011) (0)
TOTAL VARIATION REGULARIZED LEAST SQUARES By (2021) (0)
Discussion of the Paper on Concentration for ( Regularized ) Empirical Risk Minimization by (2018) (0)
Brouwer’s Fixed Point Theorem and Sparsity (2016) (0)
M-estimation and complexity regularization (2008) (0)
High-dimensional data: p > n in (2004) (0)
Additive models and many smooth univariate functions (2011) (0)
R Code for simulations in the paper "Sparse spectral estimation with missing and corrupted measurements" and resulting output (2019) (0)
Separable regularization penalties and structured sparsity (2012) (0)
The mathematical work of Evarist Giné. (Catalan) (2017) (0)
Probability Inequalities for Matrices (2016) (0)
A Note on Variable Selection with Concave Penalty (2011) (0)
Asymptotically Linear Estimators of the Precision Matrix (2016) (0)
Sparse Recovery Problems in High Dimensions: Statistical Inference and Learning Theory (2009) (0)
Estimation of functions with spatially inhomogeneous smoothness : an approach based on total variation penalties (1996) (0)
On complex models and weighted empirical processes (2008) (0)
2 Logistic Group Lasso 2 . 1 Model Setup (2007) (0)
Book Reviews (2011) (0)
Chaining Including Concentration (2016) (0)
Metric Structure of Convex Hulls (2016) (0)
Mini-Workshop: Mathematics of Machine Learning (2011) (0)
The partial linear model in high dimensions High-dimensional partial linear model (2018) (0)
In Memoriam: Evarist Giné (2016) (0)
The Square-Root Lasso (2016) (0)
Discussion of Models as Approximations I & II (2019) (0)
Inequalities for the Centred Empirical Risk and Its Derivative (2016) (0)
M L ] 2 5 Ju n 20 08 High-Dimensional Additive Modeling (2009) (0)
Discussion of the paper by M. Mougeot, D. Picard and K. Tribouley: Grouping strategies and thresholding for high dimension linear models (2013) (0)
Learning by empirical L1 risk minimization, with L1 regularization (2004) (0)
Oracle Inequalities for Local and Global Empirical Risk Minimizers (2019) (0)
English summaries (1989) (0)
$\chi^2$-confidence sets in high-dimensional regression (2015) (0)
Discussion of the paper by Piet Groeneboom: Nonparametric (smoothed) likelihood and integral equations (2013) (0)
1 Adaptive quantile regression (2003) (0)
Sharp Oracle Inequalities for Non-Convex Loss (2018) (0)
Some Worked-Out Examples (2016) (0)
Generic chaining and the ℓ1-penalty rejoinder (2013) (0)
Empirical Process Theory for Dual Norms (2016) (0)
Compatibility and the Lasso (2018) (0)
The Bias of the Lasso and Worst Possible Sub-directions (2016) (0)
Adaptive Rates for Image Denoising (2019) (0)
Optimal oracle inequalities for model selection (2008) (0)
P-values for linear models and beyond (2011) (0)
Lower Bounds for Sparse Quadratic Forms (2016) (0)
The Bernstein–Orlicz norm and deviation inequalities (2012) (0)
Confidence Intervals Using the Lasso (2016) (0)
The Debiased Lasso (2018) (0)
Statistical and Computational Aspects of Learning with Complex Structure (2020) (0)
The Margin Condition (2016) (0)
Rejoinder (2017) (0)
A moment inequality for multicategory support vector machines (2007) (0)
Classification and adaptation (2003) (0)
General Loss with Norm-Penalty (2016) (0)
Classificatie en adaptatie (2003) (0)
M L ] 3 D ec 2 00 8 High-Dimensional Additive Modeling (2009) (0)
Censored linear model in high dimensions (2015) (0)
Comment to “ Generic chaining and the ` 1-penalty ” by (2012) (0)
Willem van Zwet, teacher and thesis advisor (2021) (0)
M L ] 2 F eb 2 00 9 High-Dimensional Additive Modeling (2009) (0)
De Finetti ’ s Representation Theorem (2016) (0)
DFG-SNF Research Group FOR 916 Statistical Regularization and Qualitative Constraints (2010) (0)

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