TY - JOUR A1 - Bachoc, Francois A1 - Blanchard, Gilles A1 - Neuvial, Pierre T1 - On the post selection inference constant under restricted isometry properties JF - Electronic journal of statistics N2 - Uniformly valid confidence intervals post model selection in regression can be constructed based on Post-Selection Inference (PoSI) constants. PoSI constants are minimal for orthogonal design matrices, and can be upper bounded in function of the sparsity of the set of models under consideration, for generic design matrices. In order to improve on these generic sparse upper bounds, we consider design matrices satisfying a Restricted Isometry Property (RIP) condition. We provide a new upper bound on the PoSI constant in this setting. This upper bound is an explicit function of the RIP constant of the design matrix, thereby giving an interpolation between the orthogonal setting and the generic sparse setting. We show that this upper bound is asymptotically optimal in many settings by constructing a matching lower bound. KW - Inference post model-selection KW - confidence intervals KW - PoSI constants KW - linear regression KW - high-dimensional inference KW - sparsity KW - restricted isometry property Y1 - 2018 U6 - https://doi.org/10.1214/18-EJS1490 SN - 1935-7524 VL - 12 IS - 2 SP - 3736 EP - 3757 PB - Institute of Mathematical Statistics CY - Cleveland ER - TY - JOUR A1 - Beinrucker, Andre A1 - Dogan, Urun A1 - Blanchard, Gilles T1 - Extensions of stability selection using subsamples of observations and covariates JF - Statistics and Computing N2 - We introduce extensions of stability selection, a method to stabilise variable selection methods introduced by Meinshausen and Buhlmann (J R Stat Soc 72:417-473, 2010). We propose to apply a base selection method repeatedly to random subsamples of observations and subsets of covariates under scrutiny, and to select covariates based on their selection frequency. We analyse the effects and benefits of these extensions. Our analysis generalizes the theoretical results of Meinshausen and Buhlmann (J R Stat Soc 72:417-473, 2010) from the case of half-samples to subsamples of arbitrary size. We study, in a theoretical manner, the effect of taking random covariate subsets using a simplified score model. Finally we validate these extensions on numerical experiments on both synthetic and real datasets, and compare the obtained results in detail to the original stability selection method. KW - Variable selection KW - Stability selection KW - Subsampling Y1 - 2016 U6 - https://doi.org/10.1007/s11222-015-9589-y SN - 0960-3174 SN - 1573-1375 VL - 26 SP - 1059 EP - 1077 PB - Springer CY - Dordrecht ER - TY - BOOK A1 - Blanchard, Gilles T1 - Komplexitätsanalyse in Statistik und Lerntheorie : Antrittsvorlesung 2011-05-04 N2 - Gilles Blanchards Vortrag gewährt Einblicke in seine Arbeiten zur Entwicklung und Analyse statistischer Eigenschaften von Lernalgorithmen. In vielen modernen Anwendungen, beispielsweise bei der Schrifterkennung oder dem Spam- Filtering, kann ein Computerprogramm auf der Basis vorgegebener Beispiele automatisch lernen, relevante Vorhersagen für weitere Fälle zu treffen. Mit der mathematischen Analyse der Eigenschaften solcher Methoden beschäftigt sich die Lerntheorie, die mit der Statistik eng zusammenhängt. Dabei spielt der Begriff der Komplexität der erlernten Vorhersageregel eine wichtige Rolle. Ist die Regel zu einfach, wird sie wichtige Einzelheiten ignorieren. Ist sie zu komplex, wird sie die vorgegebenen Beispiele "auswendig" lernen und keine Verallgemeinerungskraft haben. Blanchard wird erläutern, wie Mathematische Werkzeuge dabei helfen, den richtigen Kompromiss zwischen diesen beiden Extremen zu finden. Y1 - 2011 UR - http://info.ub.uni-potsdam.de/multimedia/show_multimediafile.php?mediafile_id=551 PB - Univ.-Bibl. CY - Potsdam ER - TY - JOUR A1 - Blanchard, Gilles A1 - Carpentier, Alexandra A1 - Gutzeit, Maurilio T1 - Minimax Euclidean separation rates for testing convex hypotheses in R-d JF - Electronic journal of statistics N2 - We consider composite-composite testing problems for the expectation in the Gaussian sequence model where the null hypothesis corresponds to a closed convex subset C of R-d. We adopt a minimax point of view and our primary objective is to describe the smallest Euclidean distance between the null and alternative hypotheses such that there is a test with small total error probability. In particular, we focus on the dependence of this distance on the dimension d and variance 1/n giving rise to the minimax separation rate. In this paper we discuss lower and upper bounds on this rate for different smooth and non-smooth choices for C. KW - Minimax hypothesis testing KW - Gaussian sequence model KW - nonasymptotic minimax separation rate Y1 - 2018 U6 - https://doi.org/10.1214/18-EJS1472 SN - 1935-7524 VL - 12 IS - 2 SP - 3713 EP - 3735 PB - Institute of Mathematical Statistics CY - Cleveland ER - TY - JOUR A1 - Blanchard, Gilles A1 - Delattre, Sylvain A1 - Roquain, Etienne T1 - Testing over a continuum of null hypotheses with False Discovery Rate control JF - Bernoulli : official journal of the Bernoulli Society for Mathematical Statistics and Probability N2 - We consider statistical hypothesis testing simultaneously over a fairly general, possibly uncountably infinite, set of null hypotheses, under the assumption that a suitable single test (and corresponding p-value) is known for each individual hypothesis. We extend to this setting the notion of false discovery rate (FDR) as a measure of type I error. Our main result studies specific procedures based on the observation of the p-value process. Control of the FDR at a nominal level is ensured either under arbitrary dependence of p-values, or under the assumption that the finite dimensional distributions of the p-value process have positive correlations of a specific type (weak PRDS). Both cases generalize existing results established in the finite setting. Its interest is demonstrated in several non-parametric examples: testing the mean/signal in a Gaussian white noise model, testing the intensity of a Poisson process and testing the c.d.f. of i.i.d. random variables. KW - continuous testing KW - false discovery rate KW - multiple testing KW - positive correlation KW - step-up KW - stochastic process Y1 - 2014 U6 - https://doi.org/10.3150/12-BEJ488 SN - 1350-7265 SN - 1573-9759 VL - 20 IS - 1 SP - 304 EP - 333 PB - International Statistical Institute CY - Voorburg ER - TY - INPR A1 - Blanchard, Gilles A1 - Delattre, Sylvain A1 - Roquain, Étienne T1 - Testing over a continuum of null hypotheses N2 - We introduce a theoretical framework for performing statistical hypothesis testing simultaneously over a fairly general, possibly uncountably infinite, set of null hypotheses. This extends the standard statistical setting for multiple hypotheses testing, which is restricted to a finite set. This work is motivated by numerous modern applications where the observed signal is modeled by a stochastic process over a continuum. As a measure of type I error, we extend the concept of false discovery rate (FDR) to this setting. The FDR is defined as the average ratio of the measure of two random sets, so that its study presents some challenge and is of some intrinsic mathematical interest. Our main result shows how to use the p-value process to control the FDR at a nominal level, either under arbitrary dependence of p-values, or under the assumption that the finite dimensional distributions of the p-value process have positive correlations of a specific type (weak PRDS). Both cases generalize existing results established in the finite setting, the latter one leading to a less conservative procedure. The interest of this approach is demonstrated in several non-parametric examples: testing the mean/signal in a Gaussian white noise model, testing the intensity of a Poisson process and testing the c.d.f. of i.i.d. random variables. Conceptually, an interesting feature of the setting advocated here is that it focuses directly on the intrinsic hypothesis space associated with a testing model on a random process, without referring to an arbitrary discretization. T3 - Preprints des Instituts für Mathematik der Universität Potsdam - 1 (2012) 1 Y1 - 2012 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus-56877 ER - TY - JOUR A1 - Blanchard, Gilles A1 - Dickhaus, Thorsten A1 - Roquain, Etienne A1 - Villers, Fanny T1 - On least favorable configurations for step-up-down tests JF - Statistica Sinica KW - False discovery rate KW - least favorable configuration KW - multiple testing; Y1 - 2014 U6 - https://doi.org/10.5705/ss.2011.205 SN - 1017-0405 SN - 1996-8507 VL - 24 IS - 1 SP - 1 EP - U31 PB - Statistica Sinica, Institute of Statistical Science, Academia Sinica CY - Taipei ER - TY - JOUR A1 - Blanchard, Gilles A1 - Flaska, Marek A1 - Handy, Gregory A1 - Pozzi, Sara A1 - Scott, Clayton T1 - Classification with asymmetric label noise: Consistency and maximal denoising JF - Electronic journal of statistics N2 - In many real-world classification problems, the labels of training examples are randomly corrupted. Most previous theoretical work on classification with label noise assumes that the two classes are separable, that the label noise is independent of the true class label, or that the noise proportions for each class are known. In this work, we give conditions that are necessary and sufficient for the true class-conditional distributions to be identifiable. These conditions are weaker than those analyzed previously, and allow for the classes to be nonseparable and the noise levels to be asymmetric and unknown. The conditions essentially state that a majority of the observed labels are correct and that the true class-conditional distributions are "mutually irreducible," a concept we introduce that limits the similarity of the two distributions. For any label noise problem, there is a unique pair of true class-conditional distributions satisfying the proposed conditions, and we argue that this pair corresponds in a certain sense to maximal denoising of the observed distributions. Our results are facilitated by a connection to "mixture proportion estimation," which is the problem of estimating the maximal proportion of one distribution that is present in another. We establish a novel rate of convergence result for mixture proportion estimation, and apply this to obtain consistency of a discrimination rule based on surrogate loss minimization. Experimental results on benchmark data and a nuclear particle classification problem demonstrate the efficacy of our approach. KW - Classification KW - label noise KW - mixture proportion estimation KW - surrogate loss KW - consistency Y1 - 2016 U6 - https://doi.org/10.1214/16-EJS1193 SN - 1935-7524 VL - 10 SP - 2780 EP - 2824 PB - Institute of Mathematical Statistics CY - Cleveland ER - TY - JOUR A1 - Blanchard, Gilles A1 - Hoffmann, Marc A1 - Reiss, Markus T1 - Optimal adaptation for early stopping in statistical inverse problems JF - SIAM/ASA Journal on Uncertainty Quantification N2 - For linear inverse problems Y = A mu + zeta, it is classical to recover the unknown signal mu by iterative regularization methods ((mu) over cap,(m) = 0,1, . . .) and halt at a data-dependent iteration tau using some stopping rule, typically based on a discrepancy principle, so that the weak (or prediction) squared-error parallel to A((mu) over cap (()(tau)) - mu)parallel to(2) is controlled. In the context of statistical estimation with stochastic noise zeta, we study oracle adaptation (that is, compared to the best possible stopping iteration) in strong squared- error E[parallel to((mu) over cap (()(tau)) - mu)parallel to(2)]. For a residual-based stopping rule oracle adaptation bounds are established for general spectral regularization methods. The proofs use bias and variance transfer techniques from weak prediction error to strong L-2-error, as well as convexity arguments and concentration bounds for the stochastic part. Adaptive early stopping for the Landweber method is studied in further detail and illustrated numerically. KW - linear inverse problems KW - early stopping KW - discrepancy principle KW - adaptive estimation KW - oracle inequality KW - Landweber iteration Y1 - 2018 U6 - https://doi.org/10.1137/17M1154096 SN - 2166-2525 VL - 6 IS - 3 SP - 1043 EP - 1075 PB - Society for Industrial and Applied Mathematics CY - Philadelphia ER - TY - JOUR A1 - Blanchard, Gilles A1 - Hoffmann, Marc A1 - Reiss, Markus T1 - Early stopping for statistical inverse problems via truncated SVD estimation JF - Electronic journal of statistics N2 - We consider truncated SVD (or spectral cut-off, projection) estimators for a prototypical statistical inverse problem in dimension D. Since calculating the singular value decomposition (SVD) only for the largest singular values is much less costly than the full SVD, our aim is to select a data-driven truncation level (m) over cap is an element of {1, . . . , D} only based on the knowledge of the first (m) over cap singular values and vectors. We analyse in detail whether sequential early stopping rules of this type can preserve statistical optimality. Information-constrained lower bounds and matching upper bounds for a residual based stopping rule are provided, which give a clear picture in which situation optimal sequential adaptation is feasible. Finally, a hybrid two-step approach is proposed which allows for classical oracle inequalities while considerably reducing numerical complexity. KW - Linear inverse problems KW - truncated SVD KW - spectral cut-off KW - early stopping KW - discrepancy principle KW - adaptive estimation KW - oracle inequalities Y1 - 2018 U6 - https://doi.org/10.1214/18-EJS1482 SN - 1935-7524 VL - 12 IS - 2 SP - 3204 EP - 3231 PB - Institute of Mathematical Statistics CY - Cleveland ER -