Journal of the Royal Statistical Society: Series B

最新文章

• 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...
• On structure testing for component covariance matrices of a high dimensional mixture AI翻译
  作者：Li, Weiming; Yao, Jianfeng
  作者单位：Shanghai University of Finance & Economics; University of Hong Kong
  摘要：By studying the family of p-dimensional scale mixtures, the paper shows for the first time a non-trivial example where the eigenvalue distribution of the corresponding sample covariance matrix does not converge to the celebrated Marenko-Pastur law. A different and new limit is found and characterized. The reasons for failure of the Marenko-Pastur limit in this situation are found to be a strong dependence between the p-co-ordinates of the mixture. Next, we address the problem of testing whethe...
• Bayesian regression tree ensembles that adapt to smoothness and sparsity AI翻译
  作者：Linero, Antonio R.; Yang, Yun
  作者单位：State University System of Florida; Florida State University; University of Illinois System; University of Illinois Urbana-Champaign
  摘要：Ensembles of decision trees are a useful tool for obtaining flexible estimates of regression functions. Examples of these methods include gradient-boosted decision trees, random forests and Bayesian classification and regression trees. Two potential shortcomings of tree ensembles are their lack of smoothness and their vulnerability to the curse of dimensionality. We show that these issues can be overcome by instead considering sparsity inducing soft decision trees in which the decisions are tr...
• Semi-supervised approaches to efficient evaluation of model prediction performance AI翻译
  作者：Gronsbell, Jessica L.; Cai, Tianxi
  作者单位：Harvard University
  摘要：In many modern machine learning applications, the outcome is expensive or time consuming to collect whereas the predictor information is easy to obtain. Semi-supervised (SS) learning aims at utilizing large amounts of unlabelled' data along with small amounts of labelled' data to improve the efficiency of a classical supervised approach. Though numerous SS learning classification and prediction procedures have been proposed in recent years, no methods currently exist to evaluate the prediction...
• 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 ...
• Matrix variate regressions and envelope models AI翻译
  作者：Ding, Shanshan; Cook, R. Dennis
  作者单位：University of Delaware; University of Minnesota System; University of Minnesota Twin Cities
  摘要：Modern technology often generates data with complex structures in which both response and explanatory variables are matrix valued. Existing methods in the literature can tackle matrix-valued predictors but are rather limited for matrix-valued responses. We study matrix variate regressions for such data, where the response Y on each experimental unit is a random matrix and the predictor X can be either a scalar, a vector or a matrix, treated as non-stochastic in terms of the conditional distrib...
• 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, ...
• Multiple matrix Gaussian graphs estimation AI翻译
  作者：Zhu, Yunzhang; Li, Lexin
  作者单位：University System of Ohio; Ohio State University; University of California System; University of California Berkeley
  摘要：Matrix-valued data, where the sampling unit is a matrix consisting of rows and columns of measurements, are emerging in numerous scientific and business applications. Matrix Gaussian graphical models are a useful tool to characterize the conditional dependence structure of rows and columns. We employ non-convex penalization to tackle the estimation of multiple graphs from matrix-valued data under a matrix normal distribution. We propose a highly efficient non-convex optimization algorithm that...
• AdaPT: an interactive procedure for multiple testing with side information AI翻译
  作者：Lei, Lihua; Fithian, William
  作者单位：University of California System; University of California Berkeley
  摘要：We consider the problem of multiple-hypothesis testing with generic side information: for each hypothesis H-i we observe both a p-value p(i) and some predictor x(i) encoding contextual information about the hypothesis. For large-scale problems, adaptively focusing power on the more promising hypotheses (those more likely to yield discoveries) can lead to much more powerful multiple-testing procedures. We propose a general iterative framework for this problem, the adaptive p-value thresholding ...