-
作者：Das, Debraj; Lahiri, S. N.
作者单位：Indian Statistical Institute; Indian Statistical Institute Delhi; North Carolina State University
摘要：The lasso is a popular estimation procedure in multiple linear regression. We develop and establish the validity of a perturbation bootstrap method for approximating the distribution of the lasso estimator in a heteroscedastic linear regression model. We allow the underlying covariates to be either random or nonrandom, and show that the proposed bootstrap method works irrespective of the nature of the covariates. We also investigate finite-sample properties of the proposed bootstrap method in ...
-
作者：Vakulenko-Lagun, B.; Qian, J.; Chiou, S. H.; Betensky, R. A.
作者单位：Harvard University; Harvard T.H. Chan School of Public Health; University of Massachusetts System; University of Massachusetts Amherst
摘要：A time to event, X, is left-truncated by T if X can be observed only if T < X. This often results in oversampling of large values of X, and necessitates adjustment of estimation procedures to avoid bias. Simple risk-set adjustments can be made to standard risk-set-based estimators to accommodate left truncation when T and X are quasi-independent. We derive a weaker factorization condition for the conditional distribution of T given X in the observable region that permits risk-set adjustment fo...
-
作者：Marchini, J. L.
作者单位：Regeneron
-
作者：Lee, Youjin; Shen, Cencheng; Priebe, Carey E.; Vogelstein, Joshua T.
作者单位：University of Pennsylvania; University of Delaware; Johns Hopkins University; Johns Hopkins University
摘要：Deciphering the associations between network connectivity and nodal attributes is one of the core problems in network science. The dependency structure and high dimensionality of networks pose unique challenges to traditional dependency tests in terms of theoretical guarantees and empirical performance. We propose an approach to test network dependence via diffusion maps and distance-based correlations. We prove that the new method yields a consistent test statistic under mild distributional a...
-
作者：Genest, C.; Neslehova, J. G.; Remillard, B.; Murphy, O. A.
作者单位：McGill University; Universite de Montreal; HEC Montreal
摘要：Statistics are proposed for testing the hypothesis that arbitrary random variables are mutually independent. The tests are consistent and well behaved for any marginal distributions; they can be used, for example, for contingency tables which are sparse or whose dimension depends on the sample size, as well as for mixed data. No regularity conditions, data jittering, or binning mechanisms are required. The statistics are rank-based functionals of Cramer-von Mises type whose asymptotic behaviou...
-
作者：Xiao, J.; Hudgens, M. G.
作者单位：University of North Carolina; University of North Carolina Chapel Hill
摘要：Doubly truncated survival data arise if failure times are observed only within certain time intervals. The nonparametric maximum likelihood estimator is widely used to estimate the underlying failure time distribution. Using a directed graph representation of the data suggested by Vardi (1985), a certain graphical condition holds if and only if the nonparametric maximum likelihood estimate exists and is unique. If this condition does not hold, then such an estimate may exist but need not be un...
-
作者：Peng, Jiayu; Mukerjee, Rahul; Lin, Dennis K. J.
作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; Indian Institute of Management (IIM System); Indian Institute of Management Calcutta; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
摘要：In an order-of-addition experiment, each treatment is a permutation of m components. It is often unaffordable to test all the m! possible treatments, and thus the design problem arises. We consider a flexible model that incorporates the order of each pair of components and can also account for the distance between the two components in every such pair. Under this model, the optimality of the uniform design measure is established, via the approximate theory, for a broad range of criteria. Coupl...
-
作者：Sesia, M.; Sabatti, C.; Candes, E. J.
作者单位：Stanford University
摘要：Modern scientific studies often require the identification of a subset of explanatory variables. Several statistical methods have been developed to automate this task, and the framework of knockoffs has been proposed as a general solution for variable selection under rigorous Type I error control, without relying on strong modelling assumptions. In this paper, we extend the methodology of knockoffs to problems where the distribution of the covariates can be described by a hidden Markov model. ...
-
作者：Dubey, Paromita; Mueller, Hans-Georg
作者单位：University of California System; University of California Davis
摘要：Frechet mean and variance provide a way of obtaining a mean and variance for metric space-valued random variables, and can be used for statistical analysis of data objects that lie in abstract spaces devoid of algebraic structure and operations. Examples of such data objects include covariance matrices, graph Laplacians of networks and univariate probability distribution functions. We derive a central limit theorem for the Frechet variance under mild regularity conditions, using empirical proc...
-
作者：Xu, Mengyu; Zhang, Danna; Wei Biaowu
作者单位：State University System of Florida; University of Central Florida; University of California System; University of California San Diego; University of Chicago
摘要：We establish an approximation theory for Pearson's chi-squared statistics in situations where the number of cells is large, by using a high-dimensional central limit theorem for quadratic forms of random vectors. Our high-dimensional central limit theorem is proved under Lyapunov-type conditions that involve a delicate interplay between the dimension, the sample size, and the moment conditions. We propose a modified chi-squared statistic and introduce an adjusted degrees of freedom. A simulati...
