505期

最新文章

• Detection of Unusual Increases in MRI Lesion Counts in Individual Multiple Sclerosis Patients AI翻译
  作者：Zhao, Yinshan; Li, David K. B.; Petkau, A. John; Riddehough, Andrew; Traboulsee, Anthony
  作者单位：University of British Columbia; University of British Columbia; University of British Columbia; University of British Columbia
  摘要：Data Safety and Monitoring Boards (DSMBs) for multiple sclerosis clinical trials consider an increase of contrast-enhancing lesions on repeated magnetic resonance imaging an indicator for potential adverse events. However, there are no published studies that clearly identify what should be considered an unexpected increase of lesion activity for a patient. To address this problem, we consider as an index the likelihood of observing lesion counts as large as those observed on the recent scans o...
• Bayes Variable Selection in Semiparametric Linear Models AI翻译
  作者：Kundu, Suprateek; Dunson, David B.
  作者单位：Texas A&M University System; Texas A&M University College Station; Duke University
  摘要：There is a rich literature on Bayesian variable selection for parametric models. Our focus is on generalizing methods and asymptotic theory established for mixtures of g-priors to semiparametric linear regression models having unknown residual densities. Using a Dirichlet process location mixture for the residual density, we propose a semiparametric g-prior which incorporates an unknown matrix of cluster allocation indicators. For this class of priors, posterior computation can proceed via a s...
• Expectation Propagation for Likelihood-Free Inference AI翻译
  作者：Barthelme, Simon; Chopin, Nicolas
  作者单位：University of Geneva; Institut Polytechnique de Paris; ENSAE Paris
  摘要：Many models of interest in the natural and social sciences have no closed-form likelihood function, which means that they cannot be treated using the usual techniques of statistical inference. In the case where such models can be efficiently simulated, Bayesian inference is still possible thanks to the approximate Bayesian computation (ABC) algorithm. Although many refinements have been suggested, ABC inference is still far from routine. ABC is often excruciatingly slow due to very low accepta...
• Gradient-Based Kernel Dimension Reduction for Regression AI翻译
  作者：Fukumizu, Kenji; Leng, Chenlei
  作者单位：Research Organization of Information & Systems (ROIS); Institute of Statistical Mathematics (ISM) - Japan; University of Warwick; National University of Singapore
  摘要：This article proposes a novel approach to linear dimension reduction for regression using nonparametric estimation with positive-definite kernels or reproducing kernel Hilbert spaces (RKHSs). The purpose of the dimension reduction is to find such directions in the explanatory variables that explain the response sufficiently: this is called sufficient dimension reduction. The proposed method is based on an estimator for the gradient of the regression function considered for the feature vectors ...
• Probit Transformation for Kernel Density Estimation on the Unit Interval AI翻译
  作者：Geenens, Gery
  作者单位：University of New South Wales Sydney
  摘要：Kernel estimation of a probability density function supported on the unit interval has proved difficult, because of the well-known boundary bias issues a conventional kernel density estimator would necessarily face in this situation. Transforming the variable of interest into a variable whose density has unconstrained support, estimating that density, and obtaining an estimate of the density of the original variable through back-transformation, seems a natural idea to easily get rid of the bou...
• Large Sample Randomization Inference of Causal Effects in the Presence of Interference AI翻译
  作者：Liu, Lan; Hudgens, Michael G.
  作者单位：Harvard University; Harvard University; University of North Carolina; University of North Carolina Chapel Hill
  摘要：Recently, there has been increasing interest in making causal inference when interference is possible. In the presence of interference, treatment may have several types of effects. In this article, we consider inference about such effects when the population consists of groups of individuals where interference is possible within groups but not between groups. A two-stage randomization design is assumed where in the first stage groups are randomized to different treatment allocation strategies ...
• Principal Flows AI翻译
  作者：Panaretos, Victor M.; Pham, Tung; Yao, Zhigang
  作者单位：Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne
  摘要：We revisit the problem of extending the notion of principal component analysis (PCA) to multivariate datasets that satisfy nonlinear constraints, therefore lying on Riemannian manifolds. Our aim is to determine curves on the manifold that retain their canonical interpretability as principal components, while at the same time being flexible enough to capture nongeodesic forms of variation. We introduce the concept of a principal flow, a curve on the manifold passing through the mean of the data...
• Multivariate Functional Halfspace Depth AI翻译
  作者：Claeskens, Gerda; Hubert, Mia; Slaets, Leen; Vakili, Kaveh
  作者单位：KU Leuven; KU Leuven; European Organisation for Research & Treatment of Cancer
  摘要：This article defines and studies a depth for multivariate functional data. By the multivariate nature and by including a weight function, it acknowledges important characteristics of functional data, namely differences in the amount of local amplitude, shape, and phase variation. We study both population and finite sample versions. The multivariate sample of curves may include warping functions, derivatives, and integrals of the original curves for a better overall representation of the functi...
• Bootstrap Testing of the Rank of a Matrix via Least-Squared Constrained Estimation AI翻译
  作者：Portier, Francois; Delyon, Bernard
  作者单位：Universite Catholique Louvain; Universite de Rennes
  摘要：To test if an unknown matrix M-0 has a given rank (null hypothesis noted H-0), we consider a statistic that is a squared distance between an estimator (M) over cap and the submanifold of fixed-rank matrix. Under H-0, this statistic converges to a weighted chi-squared distribution. We introduce the constrained bootstrap (CS bootstrap) to estimate the law of this statistic under H-0. An important point is that even if H-0 fails, the CS bootstrap reproduces the behavior of the statistic under H-0...
• Quantile Association Regression Models AI翻译
  作者：Li, Ruosha; Cheng, Yu; Fine, Jason P.
  作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh; Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh; Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh; University of North Carolina; University of North Carolina Chapel Hill
  摘要：It is often important to study the association between two continuous variables. In this work, we propose a novel regression framework for assessing conditional associations on quantiles. We develop general methodology which permits covariate effects on both the marginal quantile models for the two variables and their quantile associations. The proposed quantile copula models have straightforward interpretation, facilitating a comprehensive view of association structure which is much richer th...