-
作者：Aharoni, Ehud; Rosset, Saharon
作者单位：International Business Machines (IBM); IBM ISRAEL; Tel Aviv University
摘要：The increasing prevalence and utility of large public databases necessitates the development of appropriate methods for controlling false discovery. Motivated by this challenge, we discuss the generic problem of testing a possibly infinite stream of null hypotheses. In this context, Foster and Stine suggested a novel method named alpha-investing for controlling a false discovery measure known as mFDR. We develop a more general procedure for controlling mFDR, of which alpha-investing is a speci...
-
作者：Polson, Nicholas G.; Scott, James G.; Windle, Jesse
作者单位：University of Chicago; University of Texas System; University of Texas Austin
摘要：We propose the Bayesian bridge estimator for regularized regression and classification. Two key mixture representations for the Bayesian bridge model are developed: a scale mixture of normal distributions with respect to an a-stable random variable; a mixture of Bartlett-Fejer kernels (or triangle densities) with respect to a two-component mixture of gamma random variables. Both lead to Markov chain Monte Carlo methods for posterior simulation, and these methods turn out to have complementary ...
-
作者：Hall, Peter; Ma, Yanyuan
作者单位：University of Melbourne; University of California System; University of California Davis; Texas A&M University System; Texas A&M University College Station
摘要：Differential equations are customarily used to describe dynamic systems. Existing methods for estimating unknown parameters in those systems include parameter cascade, which is a spline-based technique, and pseudo-least-squares, which is a local-polynomial-based two-step method. Parameter cascade is often referred to as a 'one-step method', although it in fact involves at least two stages: one to choose the tuning parameter and another to select model parameters. We propose a class of fast, ea...
-
作者：Kleiner, Ariel; Talwalkar, Ameet; Sarkar, Purnamrita; Jordan, Michael I.
作者单位：University of California System; University of California Berkeley
摘要：The bootstrap provides a simple and powerful means of assessing the quality of estimators. However, in settings involving large data sets-which are increasingly prevalent-the calculation of bootstrap-based quantities can be prohibitively demanding computationally. Although variants such as subsampling and the m out of n bootstrap can be used in principle to reduce the cost of bootstrap computations, these methods are generally not robust to specification of tuning parameters (such as the numbe...
-
作者：Doss, Hani; Tan, Aixin
作者单位：State University System of Florida; University of Florida; University of Iowa
摘要：In the classical biased sampling problem, we have k densities pi(1)(.), ... , pi(k)(.), each known up to a normalizing constant, i.e., for l = 1, ... , k, pi(l)(.) = v(l)(.)/m(l), where v(l)(.)is a known function and m(l) is an unknown constant. For each l, we have an independent and identically distributed sample from pi(l), and the problem is to estimate the ratios m(l)/m(s) for all l and all s. This problem arises frequently in several situations in both frequentist and Bayesian inference. ...
-
作者：Rolling, Craig A.; Yang, Yuhong
作者单位：University of Minnesota System; University of Minnesota Twin Cities
摘要：Researchers often believe that a treatment's effect on a response may be heterogeneous with respect to certain baseline covariates. This is an important premise of personalized medicine. Several methods for estimating heterogeneous treatment effects have been proposed. However, little attention has been given to the problem of choosing between estimators of treatment effects. Models that best estimate the regression function may not be best for estimating the effect of a treatment; therefore, ...
-
作者：Chu, Tingjin; Wang, Haonan; Zhu, Jun
作者单位：Renmin University of China; Colorado State University System; Colorado State University Fort Collins; University of Wisconsin System; University of Wisconsin Madison
摘要：We develop a semiparametric approach to geostatistical modelling and inference. In particular, we consider a geostatistical model with additive components, where the form of the covariance function of the spatial random error is not prespecified and thus is flexible. A novel, local Karhunen-Loeve expansion is developed and a likelihood-based method is devised for estimating the model parameters and statistical inference. A simulation study demonstrates sound finite sample properties and a real...
