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最新文章

• Exact Bayesian inference in spatiotemporal Cox processes driven by multivariate Gaussian processes AI翻译
  作者：Goncalves, Flavio B.; Gamerman, Dani
  作者单位：Universidade Federal de Minas Gerais; Universidade Federal do Rio de Janeiro
  摘要：We present a novel inference methodology to perform Bayesian inference for spatiotemporal Cox processes where the intensity function depends on a multivariate Gaussian process. Dynamic Gaussian processes are introduced to enable evolution of the intensity function over discrete time. The novelty of the method lies on the fact that no discretization error is involved despite the non-tractability of the likelihood function and infinite dimensionality of the problem. The method is based on a Mark...
• Statistical inference based on randomly generated auxiliary variables AI翻译
  作者：Schouten, Barry
  作者单位：Utrecht University
  摘要：In most real life studies, auxiliary variables are available and are employed to explain and understand missing data patterns and to evaluate and control causal relationships with variables of interest. Usually their availability is assumed to be a fact, even if the variables are measured without the objectives of the study in mind. As a result, inference with missing data and causal inference require some assumptions that cannot easily be validated or checked. In this paper, a framework is co...
• Kernel-based tests for joint independence AI翻译
  作者：Pfister, Niklas; Buhlmann, Peter; Schoelkopf, Bernhard; Peters, Jonas
  作者单位：Max Planck Society; University of Copenhagen
  摘要：We investigate the problem of testing whether d possibly multivariate random variables, which may or may not be continuous, are jointly (or mutually) independent. Our method builds on ideas of the two-variable Hilbert-Schmidt independence criterion but allows for an arbitrary number of variables. We embed the joint distribution and the product of the marginals in a reproducing kernel Hilbert space and define the d-variable Hilbert-Schmidt independence criterion dHSIC as the squared distance be...
• Another look at distance-weighted discrimination AI翻译
  作者：Wang, Boxiang; Zou, Hui
  作者单位：University of Minnesota System; University of Minnesota Twin Cities
  摘要：Distance-weighted discrimination (DWD) is a modern margin-based classifier with an interesting geometric motivation. It was proposed as a competitor to the support vector machine (SVM). Despite many recent references on DWD, DWD is far less popular than the SVM, mainly because of computational and theoretical reasons. We greatly advance the current DWD methodology and its learning theory. We propose a novel thrifty algorithm for solving standard DWD and generalized DWD, and our algorithm can b...
• Optimal a priori balance in the design of controlled experiments AI翻译
  作者：Kallus, Nathan
  作者单位：Cornell University
  摘要：We develop a unified theory of designs for controlled experiments that balance baseline covariates a priori (before treatment and before randomization) using the framework of minimax variance and a new method called kernel allocation. We show that any notion of a priori balance must go hand in hand with a notion of structure, since with no structure on the dependence of outcomes on baseline covariates complete randomization (no special covariate balance) is always minimax optimal. Restricting ...
• Expectation propagation in the large data limit AI翻译
  作者：Dehaene, Guillaume; Barthelme, Simon
  作者单位：Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne; University of Geneva
  摘要：Expectation propagation (EP) is a widely successful algorithm for variational inference. EP is an iterative algorithm used to approximate complicated distributions, typically to find a Gaussian approximation of posterior distributions. In many applications of this type, EP performs extremely well. Surprisingly, despite its widespread use, there are very few theoretical guarantees on Gaussian EP, and it is quite poorly understood. To analyse EP, we first introduce a variant of EP: averaged EP, ...
• Report of the Editors-2017 AI翻译
  作者：Dunson, David; Fryzlewicz, Piotr
• Inference for empirical Wasserstein distances on finite spaces AI翻译
  作者：Sommerfeld, Max; Munk, Axel
  作者单位：University of Gottingen; Max Planck Society
  摘要：The Wasserstein distance is an attractive tool for data analysis but statistical inference is hindered by the lack of distributional limits. To overcome this obstacle, for probability measures supported on finitely many points, we derive the asymptotic distribution of empirical Wasserstein distances as the optimal value of a linear programme with random objective function. This facilitates statistical inference (e.g. confidence intervals for sample-based Wasserstein distances) in large general...
• Goodness-of-fit tests for high dimensional linear models AI翻译
  作者：Shah, Rajen D.; Buhlmann, Peter
  作者单位：University of Cambridge
  摘要：We propose a framework for constructing goodness-of-fit tests in both low and high dimensional linear models. We advocate applying regression methods to the scaled residuals following either an ordinary least squares or lasso fit to the data, and using some proxy for prediction error as the final test statistic. We call this family residual prediction tests. We show that simulation can be used to obtain the critical values for such tests in the low dimensional setting and demonstrate using bot...
• False discovery proportion estimation by permutations: confidence for significance analysis of microarrays AI翻译
  作者：Hemerik, Jesse; Goeman, Jelle J.
  作者单位：Leiden University - Excl LUMC; Leiden University; Leiden University Medical Center (LUMC)
  摘要：Significance analysis of microarrays (SAM) is a highly popular permutation-based multiple-testing method that estimates the false discovery proportion (FDP): the fraction of false positive results among all rejected hypotheses. Perhaps surprisingly, until now this method had no known properties. This paper extends SAM by providing 1- upper confidence bounds for the FDP, so that exact confidence statements can be made. As a special case, an estimate of the FDP is obtained that underestimates th...