5期

最新文章

• THE LASSO WITH GENERAL GAUSSIAN DESIGNS WITH APPLICATIONS TO HYPOTHESIS TESTING AI翻译
  作者：Celentano, Michael; Montanari, Andrea; Wei, Yuting
  作者单位：University of California System; University of California Berkeley; Stanford University; University of Pennsylvania
  摘要：The Lasso is a method for high-dimensional regression, which is now commonly used when the number of covariates p is of the same order or larger than the number of observations n. Classical asymptotic normality theory does not apply to this model due to two fundamental reasons: (1) The regularized risk is nonsmooth; (2) The distance between the estimator 0 ⠂and the true parameters vector 0* cannot be neglected. As a consequence, standard perturbative arguments that are the traditional basis f...
• ESTIMATION OF EXPECTED EULER CHARACTERISTIC CURVES OF NONSTATIONARY SMOOTH RANDOM FIELDS AI翻译
  作者：Telschow, Fabian J. E.; Cheng, Dan; Pranav, Pratyush; Schwartzman, Armin
  作者单位：Humboldt University of Berlin; Arizona State University; Arizona State University-Tempe; Universite Claude Bernard Lyon 1; Ecole Normale Superieure de Lyon (ENS de LYON); University of California System; University of California San Diego
  摘要：The expected Euler characteristic (EEC) of excursion sets of a smooth Gaussian-related random field over a compact manifold approximates the dis-tribution of its supremum for high thresholds. Viewed as a function of the excursion threshold, the EEC of a Gaussian-related field is expressed by the Gaussian kinematic formula (GKF) as a finite sum of known functions multi-plied by the Lipschitz-Killing curvatures (LKCs) of the generating Gaussian field. This paper proposes consistent estimators of...
• THE IMPACTS OF UNOBSERVED COVARIATES ON COVARIATE-ADAPTIVE RANDOMIZED EXPERIMENTS AI翻译
  作者：Liu, Yang; Hu, Feifang
  作者单位：Renmin University of China; George Washington University
  摘要：Covariate-adaptive randomization (CAR) is commonly implemented in clinical trials to balance observed covariates. Recent studies have demonstrated the advantages of CAR procedures in balancing covariates and improving the subsequent statistical analysis. Covariate balance is crucial, but it is not a panacea for the valid statistical inferences. If the response to a treatment interacts with some unobserved covariates, the conclusion drawn from a CAR experiment may be affected, and thus, be inco...
• SPATIAL QUANTILES ON THE HYPERSPHERE AI翻译
  作者：Konen, Dimitri; Paindaveine, Davy
  作者单位：Universite Libre de Bruxelles; Universite Libre de Bruxelles
  摘要：We propose a concept of quantiles for probability measures on the unit hypersphere Sd-1 of Rd. The innermost quantile is the Frechet median, that is, the L1-analog of the Frechet mean. The proposed quantiles mu au are di-rectional in nature: they are indexed by a scalar order alpha e [0, 1] and a unit vector u in the tangent space TmSd-1 to Sd-1 at m. To ensure computability in any dimension d, our quantiles are essentially obtained by considering the Euclidean (Chaudhuri (J. Amer. Statist. As...
• OPTIMAL NONPARAMETRIC TESTING OF MISSING COMPLETELY AT RANDOM AND ITS CONNECTIONS TO COMPATIBILITY AI翻译
  作者：Berrett, Thomas b.; Samworth, Richard j.
  作者单位：University of Warwick; University of Cambridge
  摘要：Given a set of incomplete observations, we study the nonparametric problem of testing whether data are Missing Completely At Random (MCAR). Our first contribution is to characterise precisely the set of alternatives that can be distinguished from the MCAR null hypothesis. This reveals interesting and novel links to the theory of Frechet classes (in particular, compatible distributions) and linear programming, that allow us to propose MCAR tests that are consistent against all detectable altern...
• ADAPTIVE AND ROBUST MULTI-TASK LEARNING AI翻译
  作者：Duan, Yaqi; Wang, Kaizheng
  作者单位：New York University; Columbia University; Columbia University
  摘要：We study the multitask learning problem that aims to simultaneously an-alyze multiple data sets collected from different sources and learn one model for each of them. We propose a family of adaptive methods that automatically utilize possible similarities among those tasks while carefully handling their differences. We derive sharp statistical guarantees for the methods and prove their robustness against outlier tasks. Numerical experiments on synthetic and real data sets demonstrate the effic...
• ESTIMATION OF MIXED FRACTIONAL STABLE PROCESSES USING HIGH-FREQUENCY DATA AI翻译
  作者：Mies, Fabian; Podolskij, Mark
  作者单位：Delft University of Technology; University of Luxembourg
  摘要：The linear fractional stable motion generalizes two prominent classes of stochastic processes, namely stable Levy processes, and fractional Brownian motion. For this reason, it may be regarded as a basic building block for con-tinuous time models. We study a stylized model consisting of a superposition of independent linear fractional stable motions and our focus is on parame-ter estimation of the model. Applying an estimating equations approach, we construct estimators for the whole set of pa...
• DIFFERENTIALLY PRIVATE INFERENCE VIA NOISY OPTIMIZATION AI翻译
  作者：Avella-medina, Marco; Bradshaw, Casey; Loh, Po-ling
  作者单位：Columbia University; University of Cambridge
  摘要：We propose a general optimization-based framework for computing differentially private M-estimators and a new method for constructing differentially private confidence regions. First, we show that robust statistics can be used in conjunction with noisy gradient descent or noisy Newton methods in order to obtain optimal private estimators with global linear or quadratic convergence, respectively. We establish local and global convergence guarantees, under both local strong convexity and self-co...
• NONPARAMETRIC CONDITIONAL LOCAL INDEPENDENCE TESTING AI翻译
  作者：Christgau, Alexander Mangulad; Petersen, Lasse; Hansen, Niels richard
  作者单位：University of Copenhagen
  摘要：Conditional local independence is an asymmetric independence relation among continuous time stochastic processes. It describes whether the evolu-tion of one process is directly influenced by another process given the histo-ries of additional processes, and it is important for the description and learn-ing of causal relations among processes. We develop a model-free framework for testing the hypothesis that a counting process is conditionally locally in-dependent of another process. To this end...
• INFERENCE FOR EXTREMAL REGRESSION WITH DEPENDENT HEAVY-TAILED DATA AI翻译
  作者：Daouia, Abdelaati; Stupfler, Gilles; Usseglio-carleve, Antoine
  作者单位：Universite de Toulouse; Universite Toulouse 1 Capitole; Toulouse School of Economics; Centre National de la Recherche Scientifique (CNRS); Universite d'Angers; Avignon Universite
  摘要：Nonparametric inference on tail conditional quantiles and their least squares analogs, expectiles, remains limited to i.i.d. data. We develop a fully operational inferential theory for extreme conditional quantiles and expectiles in the challenging framework of alpha-mixing, conditional heavy-tailed data whose tail index may vary with covariate values. This requires a dedicated treatment to deal with data sparsity in the far tail of the response, in addition to handling difficulties inherent t...