S. Geer

发表

Applications of empirical process theory

S. Geer, 2000 .

Empirical Processes in M-Estimation

S. Geer, 2000 .

Adaptivity of Support Vector Machines with ` 1 Penalty

S. Geer, B. Tarigan, 2004 .

On the conditions used to prove oracle results for the Lasso

S. Geer, Peter Buhlmann, 2009, 0910.0722.

Estimating a Regression Function

S. Geer, 1990 .

Locally adaptive regression splines

S. Geer, E. Mammen, 1997 .

On asymptotically optimal confidence regions and tests for high-dimensional models

S. Geer, Y. Ritov, Peter Buhlmann, 2013, 1303.0518.

Classifiers of support vector machine type with \ell1 complexity regularization

S. Geer, B. Tarigan, 2006 .

Regularization in statistics

S. Geer, P. Bickel, A. Tsybakov, 2006 .

A New Approach to Least-Squares Estimation, with Applications

S. Geer, 1986 .

On Hoeffding's Inequality for Dependent Random Variables

S. Geer, 2002 .

Consistency for the least squares estimator in nonparametric regression

S. Geer, M. Wegkamp, 1996 .

Statistics for High-Dimensional Data: Methods, Theory and Applications

Sara van de Geer, Peter Bhlmann, S. Geer, 2011 .

Oracle Inequalities and Optimal Inference under Group Sparsity

S. Geer, A. Tsybakov, M. Pontil, 2010, 1007.1771.

Regression analysis and empirical processes

S. Geer, 1988 .

ℓ1-penalization for mixture regression models

S. Geer, P. Bühlmann, N. Städler, 2010, 1202.6046.

High-dimensional additive modeling

S. Geer, Peter Buhlmann, L. Meier, 2008, 0806.4115.

Weakly decomposable regularization penalties and structured sparsity

S. Geer, 2012, 1204.4813.

Theory for the Lasso

S. Geer, P. Bühlmann, 2011 .

HIGH-DIMENSIONAL GENERALIZED LINEAR MODELS AND THE LASSO

S. Geer, 2008, 0804.0703.

On higher order isotropy conditions and lower bounds for sparse quadratic forms

S. Geer, Alan Muro, 2014, 1405.5995.

On the total variation regularized estimator over a class of tree graphs

S. Geer, Francesco Ortelli, 2018, 1806.01009.

The Bernstein–Orlicz norm and deviation inequalities

S. Geer, Johannes Lederer, 2011, 1111.2450.

Statistics for big data: A perspective

S. Geer, P. Bühlmann, 2018 .

Nemirovskis Inequalities Revisited

S. Geer, J. Wellner, L. Dümbgen, 2010 .

High-dimensional data: p >> n in mathematical statistics and bio-medical applications

S. Geer, H. V. Houwelingen, 2004 .

Inference in High-Dimensional Graphical Models

S. Geer, Jana Janková, 2018, Handbook of Graphical Models.

Exponential Inequalities for Martingales, with Application to Maximum Likelihood Estimation for Counting Processes

S. Geer, 1995 .

$\ell_0$-penalized maximum likelihood for sparse directed acyclic graphs

S. Geer, Peter Buhlmann, 2012, 1205.5473.

The adaptive and the thresholded Lasso for potentially misspecified models (and a lower bound for the Lasso)

S. Geer, Peter Buhlmann, Shuheng Zhou, 2011 .

On the uniform convergence of empirical norms and inner products, with application to causal inference

S. Geer, 2013, 1310.5523.

Adaptive Lasso for High Dimensional Regression and Gaussian Graphical Modeling

S. Geer, Peter Buhlmann, Shuheng Zhou, 2009, 0903.2515.

The Lasso, correlated design, and improved oracle inequalities

S. Geer, Johannes Lederer, 2011, 1107.0189.

Hellinger-Consistency of Certain Nonparametric Maximum Likelihood Estimators

S. Geer, 1993 .

The Smooth-Lasso and other ℓ1+ℓ2-penalized methods

S. Geer, Mohamed Hebiri, 2011 .

The Smooth-Lasso and other ℓ1+ℓ2-penalized methods

S. Geer, Mohamed Hebiri, 2011 .

Robust low-rank matrix estimation

S. Geer, A. Elsener, 2016, The Annals of Statistics.

S. Geer, Cun-Hui Zhang, Peter Buhlmann, 2012, 1209.5908.

Least Squares Estimation

S. Geer, 2005 .

Adaptive estimation with soft thresholding penalties

S. Geer, Jean-Michel Loubes, 2002 .

ℓ1-regularization in High-dimensional Statistical Models

S. Geer, 2011 .

Quasi-Likelihood and/or Robust Estimation in High Dimensions

S. Geer, P. Muller, 2012, 1206.6721.

Statistics for High-Dimensional Data

Sara van de Geer, Peter Bühlmann, S. Geer, 2011 .

Penalized quasi-likelihood estimation in partial linear models

Sara van de Geer, Enno Mammen, S. Geer, 1997 .

Square root penalty: Adaptation to the margin in classification and in edge estimation

S. Geer, A. Tsybakov, A. Tsybakov, 2005, math/0507422.

DFG-SNF Research Group FOR 916 Statistical Regularization and Qualitative Constraints

S. Geer, P. Bühlmann, N. Städler, 2010 .

Rejoinder: ℓ1-penalization for mixture regression models

S. Geer, P. Bühlmann, N. Städler, 2010 .

Confidence intervals for high-dimensional inverse covariance estimation

S. Geer, Jana Janková, 2014, 1403.6752.

Some exercises with the Lasso and its compatibility constant

S. Geer, 2017, 1701.03326.

The mathematical work of Evarist Giné

R. Nickl, S. Geer, J. Wellner, 2016 .

Mini-Workshop: Mathematics of Machine Learning

G. Lugosi, S. Geer, L. Györfi, 2011 .

Lectures on Empirical Processes: Theory and Statistical Applications

S. A. van de Geer, Paul Deheuvels, S. Geer, 2007 .

Generic chaining and the ℓ1-penalty

S. Geer, 2012, 1205.3703.

Sparse spectral estimation with missing and corrupted measurements

Andreas Elsener, S. Geer, A. Elsener, 2018, Stat.

A moment inequality for multicategory support vector machines

S. Geer, B. Tarigan, 2007 .

On threshold-based classification rules

Sara van de Geer, L. Mohammadi, S. Geer, 2003 .

M L ] 2 F eb 2 00 9 High-Dimensional Additive Modeling

S. Geer, P. Bühlmann, L. Meier, 2009 .

M L ] 2 5 Ju n 20 08 High-Dimensional Additive Modeling

S. Geer, P. Bühlmann, L. Meier, 2009 .

Logistic regression with total variation regularization

S. Geer, 2020, 2003.02678.

Book Reviews

S. Geer, Eva Riccomagno, P. Gibilisco, 2011 .

Some Worked-Out Examples

S. Geer, 2016 .

2 Logistic Group Lasso 2 . 1 Model Setup

S. Geer, P. Bühlmann, L. Meier, 2007 .

$\chi^2$-confidence sets in high-dimensional regression

S. Geer, Benjamin Stucky, 2015, 1502.07131.

Regularization in statistics: Discussion

S. Geer, P. Bickel, A. Tsybakov, 2006 .

Classifiers of support vector machine type with \ell1 complexity regularization

Sara A. van de Geer, Bernadetta Tarigan, S. Geer, 2006 .

Oracle inequalities for square root analysis estimators with application to total variation penalties

S. Geer, Francesco Ortelli, 2019, Information and Inference: A Journal of the IMA.

Synthesis and analysis in total variation regularization

S. Geer, Francesco Ortelli, 2019, 1901.06418.

Empirical Process Theory for Dual Norms

S. Geer, 2016 .

Discussion: "A significance test for the lasso"

S. Geer, Peter Buhlmann, L. Meier, 2014, 1405.6792.

General oracle inequalities for model selection

S. Geer, Charles Mitchell, 2009 .

Optimal oracle inequalities for model selection

S. Geer, Charles Mitchell, 2008, 0806.4140.

A note on rates of convergence in least squares estimation

S. Geer, van de, 1986 .

Adaptive quantile regression

S. Geer, 2003 .

Classifiers of support vector machine type with \ell1 complexity regularization

S. Geer, B. Tarigan, Bernadetta Tar Igan, 2006 .

The Square-Root Lasso

S. Geer, 2016 .

Asymptotically Linear Estimators of the Precision Matrix

S. Geer, 2016 .

Brouwer’s Fixed Point Theorem and Sparsity

S. Geer, 2016 .

Concentration behavior of the penalized least squares estimator

S. Geer, Alan Muro, 2015, 1511.08698.

Tensor denoising with trend filtering

Sara van de Geer, Francesco Ortelli, S. Geer, 2021, Mathematical Statistics and Learning.

Oracle Inequalities for Local and Global Empirical Risk Minimizers

Sara van de Geer, Andreas Elsener, S. Geer, 2019, Applied and Numerical Harmonic Analysis.

De-biased sparse PCA: Inference and testing for eigenstructure of large covariance matrices

Sara van de Geer, S. Geer, Jana Jankov'a, 2018, 1801.10567.

Semiparametric efficiency bounds for high-dimensional models

S. Geer, Jana Janková, 2016, The Annals of Statistics.

Confidence regions for high-dimensional generalized linear models under sparsity

Sara van de Geer, S. Geer, Jana Jankov'a, 2016, 1610.01353.

The Bias of the Lasso and Worst Possible Sub-directions

S. Geer, 2016 .

Metric Structure of Convex Hulls

S. Geer, 2016 .

Honest confidence regions and optimality in high-dimensional precision matrix estimation

Jana Janková, S. Geer, Jana Janková, 2015, TEST.

Censored linear model in high dimensions

S. Geer, Sara Geer, Patric Müller, 2014, 1405.0579.

New concentration inequalities for suprema of empirical processes

Sara van de Geer, Johannes Lederer, S. Geer, 2011, 1111.3486.

The Partial Linear Model in High Dimensions

Sara van de Geer, S. Geer, Patric Muller, 2013, 1307.1067.

A Moment Bound for Multi-hinge Classifiers

Sara van de Geer, Bernadetta Tarigan, S. Geer, 2008 .

A global test for groups of genes: testing association with a clinical outcome

Jelle J. Goeman, Hans C. van Houwelingen, Sara A. van de Geer, 2004, Bioinform..

High-dimensional inference in misspecified linear models

Sara van de Geer, Peter Buhlmann, S. Geer, 2015, 1503.06426.

Nemirovski's Inequalities Revisited

Lutz Dümbgen, Sara A. van de Geer, Mark Veraar, 2008, Am. Math. Mon..

Penalized least squares estimation in the additive model with different smoothness for the components

Sara van de Geer, S. Geer, Alan Muro, 2014, 1405.6584.

M-estimation using penalties or sieves

S. Geer, 2002 .

Rates of convergence for the maximum likelihood estimator in mixture models

Sara van de Geer, S. Geer, 1996 .

Asymptotic normality of minimum $L_ 1$-norm estimators in linear regression

Sara van de Geer, S. Geer, van de, 1988 .

On non-asymptotic bounds for estimation in generalized linear models with highly correlated design

S. Geer, 2007, 0709.0844.

Asymptotics in Empirical Risk Minimization

Leila Mohammadi, Sara A. van de Geer, S. Geer, 2005, J. Mach. Learn. Res..

Comment to “ Generic chaining and the ` 1-penalty ” by

S. Geer, 2012 .

On complex models and weighted empirical processes

S. Geer, 2008 .

Prediction bounds for higher order total variation regularized least squares

Sara van de Geer, Francesco Ortelli, S. Geer, 2019, The Annals of Statistics.

TOTAL VARIATION REGULARIZED LEAST SQUARES By

S. Geer, Francesco Ortelli, 2021 .

Oracle inequalities for image denoising with total variation regularization

S. Geer, Francesco Ortelli, 2019, 1911.07231.

Convergence Rates for Penalized Least Squares Estimators in PDE Constrained Regression Problems

Sara van de Geer, Richard Nickl, Sven Wang, 2018, SIAM/ASA J. Uncertain. Quantification.

Adaptive Rates for Total Variation Image Denoising.

S. Geer, Francesco Ortelli, 2020 .

Prediction and variable selection with the adaptive Lasso

Sara van de Geer, Peter Bühlmann, Shuheng Zhou, 2010 .

Semi-parametric efficiency bounds and efficient estimation for high-dimensional models

Sara van de Geer, Jana Janková, S. Geer, 2016 .

On the robustness of minimum norm interpolators and regularized empirical risk minimizers

S. Geer, Geoffrey Chinot, Matthias Loffler, 2020, The Annals of Statistics.

Taking Advantage of Sparsity in Multi-Task Learning

Massimiliano Pontil, Sara A. van de Geer, Alexandre B. Tsybakov, 2009, COLT.

Worst possible sub-directions in high-dimensional models

Sara A. van de Geer, S. Geer, 2014, J. Multivar. Anal..

Asymptotic theory for maximum likelihood in nonparametric mixture models

Sara A. van de Geer, S. Geer, 2003, Comput. Stat. Data Anal..

On the total variation regularized estimator over the branched path graph

S. Geer, Francesco Ortelli, 2018 .

Sharp Oracle Inequalities for Square Root Regularization

Sara A. van de Geer, Benjamin Stucky, S. Geer, 2015, J. Mach. Learn. Res..

Minimum 𝓁1-norm interpolation via basis pursuit is robust to errors pdf

Sara A. van de Geer, Matthias Löffler, Geoffrey Chinot, 2020, ArXiv.

The Relativity of Utility: Evidence from Panel Data

Sara van de Geer, Arie Kapteyn, S. Geer, 1985 .

The partial linear model in high dimensions High-dimensional partial linear model

S. Geer, Patrice Müller, 2018 .

Estimation: An Overview

Sara A. van de Geer, S. Geer, 2011, International Encyclopedia of Statistical Science.

1 Adaptive quantile regression

S. Geer, 2003 .

On robust recursive nonparametric curve estimation.

S. Geer, E. Belitser, 2000 .

M-estimation and complexity regularization

S. Geer, 2008 .

On Tight Bounds for the Lasso

Sara A. van de Geer, S. Geer, 2018, J. Mach. Learn. Res..

On the asymptotic variance of the debiased Lasso

S. Geer, Sara van de Geer, 2019, Electronic Journal of Statistics.

On the efficiency of the de-biased Lasso

Sara van de Geer, S. Geer, 2017, 1708.07986.

Confidence sets in sparse regression

Sara van de Geer, Richard Nickl, R. Nickl, 2012, 1209.1508.

Confidence Intervals Using the Lasso

S. Geer, 2016 .

$χ^2$-confidence sets in high-dimensional regression

Sara van de Geer, Benjamin Stucky, S. Geer, 2016 .

Statistical Theory for High-Dimensional Models

Sara van de Geer, S. Geer, 2014, 1409.8557.

On the robustness of minimum-norm interpolators

S. Geer, Geoffrey Chinot, Matthias Löffler, 2021 .

AdaBoost and robust one-bit compressed sensing pdf

Sara A. van de Geer, Geoffrey Chinot, Felix Kuchelmeister, 2021, Mathematical Statistics and Learning.

Least Squares Estimation

S. Geer, A., Sara, 2005 .

On rates of convergence and asymptotic normality in the multiknapsack problem

Sara van de Geer, Leen Stougie, S. Geer, 1991, Math. Program..

A New Approach to Least Squares Estimation, with Applications

S. Geer, 2007 .

Multivariate log-concave distributions as a nearly parametric model

S. Geer, J. Wellner, L. Dümbgen, 2008, Am. Math. Mon..

Asymptotic Normality in Mixture Models

Sara van de Geer, S. Geer, 1997 .

High-dimensional additive modeling

S. Geer, Peter Buhlmann, L. Meier, 2008, 0806.4115.

The Smooth-Lasso and other $\ell_1+\ell_2$-penalized methods

Mohamed Hebiri, S. Geer, Mohamed Hebiri, 2010, 1003.4885.

The likelihood ratio test for the change point problem for exponentially distributed random variables

S. Geer, P. Haccou, E. Meelis, 1987 .

The Impact of Changes in Income and Family Composition on Subjective Measures of Well-Being

S. Geer, A. Kapteyn, H. V. D. Stadt, 1985 .

Regularization in statistics

S. Geer, P. Bickel, A. Tsybakov, 2006 .

Robust low-rank matrix estimation

S. Geer, A. Elsener, 2016, The Annals of Statistics.