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作者：Rohrbeck, C.; Costain, D. A.; Frigessi, A.
作者单位：Lancaster University; University of Oslo
摘要：We consider monotonic, multiple regression for contiguous regions. The regression functions vary regionally and may exhibit spatial structure. We develop Bayesian nonparametric methodology that permits estimation of both continuous and discontinuous functional shapes using marked point process and reversible jump Markov chain Monte Carlo techniques. Spatial dependence is incorporated by a flexible prior distribution which is tuned using crossvalidation and Bayesian optimization. We derive the ...
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作者：Weihs, L.; Drton, M.; Meinshausen, N.
作者单位：University of Washington; University of Washington Seattle; Swiss Federal Institutes of Technology Domain; ETH Zurich
摘要：The need to test whether two random vectors are independent has spawned many competing measures of dependence. We focus on nonparametric measures that are invariant under strictly increasing transformations, such as Kendall's tau, Hoeffding's D, and the Bergsma-Dassios sign covariance. Each exhibits symmetries that are not readily apparent from their definitions. Making these symmetries explicit, we define a new class of multivariate nonparametric measures of dependence that we call symmetric ...
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作者：Frazier, D. T.; Martin, G. M.; Robert, C. P.; Rousseau, J.
作者单位：Monash University; Universite PSL; Universite Paris-Dauphine; University of Oxford
摘要：Approximate Bayesian computation allows for statistical analysis using models with intractable likelihoods. In this paper we consider the asymptotic behaviour of the posterior distribution obtained by this method. We give general results on the rate at which the posterior distribution concentrates on sets containing the true parameter, the limiting shape of the posterior distribution, and the asymptotic distribution of the posterior mean. These results hold under given rates for the tolerance ...
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作者：Shively, T. S.; Walker, S. G.
作者单位：University of Texas System; University of Texas Austin; University of Texas System; University of Texas Austin
摘要：We show that the Bayes factor for testing whether a subset of coefficients are zero in the normal linear regression model gives the uniformly most powerful test amongst the class of invariant tests discussed in Lehmann & Romano (2005) if the prior distributions for the regression coefficients are in a specific class of distributions. The priors in this class can have any elliptical distribution, with a specific scale matrix, for the subset of coefficients that are being tested. We also show un...
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作者：Diaz, I.; Savenkov, O.; Ballman, K.
作者单位：Cornell University; Weill Cornell Medicine
摘要：We consider estimation of an optimal individualized treatment rule when a high-dimensional vector of baseline variables is available. Our optimality criterion is with respect to delaying the expected time to occurrence of an event of interest. We use semiparametric efficiency theory to construct estimators with properties such as double robustness. We propose two estimators of the optimal rule, which arise from considering two loss functions aimed at directly estimating the conditional treatme...
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作者：Su, Weijie J.
作者单位：University of Pennsylvania
摘要：Applied statisticians use sequential regression procedures to rank explanatory variables and, in settings of low correlations between variables and strong true effect sizes, expect that variables at the top of this ranking are truly relevant to the response. In a regime of certain sparsity levels, however, we show that the lasso, forward stepwise regression, and least angle regression include the first spurious variable unexpectedly early. We derive a sharp prediction of the rank of the first ...
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作者：Yiu, Sean; Su, Li
作者单位：MRC Biostatistics Unit; University of Cambridge
摘要：Weighting methods offer an approach to estimating causal treatment effects in observational studies. However, if weights are estimated by maximum likelihood, misspecification of the treatment assignment model can lead to weighted estimators with substantial bias and variance. In this paper, we propose a unified framework for constructing weights such that a set of measured pretreatment covariates is unassociated with treatment assignment after weighting. We derive conditions for weight estimat...
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作者：Matias, C.; Rebafka, T.; Villers, F.
作者单位：Sorbonne Universite; Centre National de la Recherche Scientifique (CNRS); Sorbonne Universite
摘要：We propose an extension of the stochastic block model for recurrent interaction events in continuous time, where every individual belongs to a latent group and conditional interactions between two individuals follow an inhomogeneous Poisson process with intensity driven by the individuals' latent groups. We show that the model is identifiable and estimate it with a semiparametric variational expectation-maximization algorithm. We develop two versions of the method, one using a nonparametric hi...
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作者：Molstad, Aaron J.; Rothman, Adam J.
作者单位：Fred Hutchinson Cancer Center; University of Minnesota System; University of Minnesota Twin Cities
摘要：We propose a framework to shrink a user-specified characteristic of a precision matrix estimator that is needed to fit a predictive model. Estimators in our framework minimize the Gaussian negative loglikelihood plus an L-1 penalty on a linear or affine function evaluated at the optimization variable corresponding to the precision matrix. We establish convergence rate bounds for these estimators and propose an alternating direction method of multipliers algorithm for their computation. Our sim...
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作者：Charkhi, Ali; Claeskens, Gerda
作者单位：KU Leuven
摘要：Ignoring the model selection step in inference after selection is harmful. In this paper we study the asymptotic distribution of estimators after model selection using the Akaike information criterion. First, we consider the classical setting in which a true model exists and is included in the candidate set of models. We exploit the overselection property of this criterion in constructing a selection region, and we obtain the asymptotic distribution of estimators and linear combinations thereo...
