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作者：Ye, Ting; Shao, Jun
作者单位：East China Normal University; University of Wisconsin System; University of Wisconsin Madison
摘要：Covariate-adaptive randomization is popular in clinical trials with sequentially arrived patients for balancing treatment assignments across prognostic factors that may have influence on the response. However, existing theory on tests for the treatment effect under covariate-adaptive randomization is limited to tests under linear or generalized linear models, although the covariate-adaptive randomization method has been used in survival analysis for a long time. Often, practitioners will simpl...
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作者：Gorgi, Paolo
作者单位：Vrije Universiteit Amsterdam; Tinbergen Institute
摘要：The paper introduces a general class of heavy-tailed auto-regressions for modelling integer-valued time series with outliers. The specification proposed is based on a heavy-tailed mixture of negative binomial distributions that features an observation-driven dynamic equation for the conditional expectation. The existence of a stationary and ergodic solution for the class of auto-regressive processes is shown under general conditions. The estimation of the model can be easily performed by maxim...
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作者：Richardson, Robert; Kottas, Athanasios; Sanso, Bruno
作者单位：Brigham Young University; University of California System; University of California Santa Cruz
摘要：An integro-difference equation can be represented as a hierarchical spatiotemporal dynamic model using appropriate parameterizations. The dynamics of the process defined by an integro-difference equation depends on the choice of a bivariate kernel distribution, where more flexible shapes generally result in more flexible models. Under a Bayesian modelling framework, we consider the use of the stable family of distributions for the kernel, as they are infinitely divisible and offer a variety of...
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作者：Pollock, Murray; Fearnhead, Paul; Johansen, Adam M.; Roberts, Gareth O.
作者单位：University of Warwick; Lancaster University
摘要：This paper introduces a class of Monte Carlo algorithms which are based on the simulation of a Markov process whose quasi-stationary distribution coincides with a distribution of interest. This differs fundamentally from, say, current Markov chain Monte Carlo methods which simulate a Markov chain whose stationary distribution is the target. We show how to approximate distributions of interest by carefully combining sequential Monte Carlo methods with methodology for the exact simulation of dif...
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作者：Tang, Yanbo; Reid, Nancy
作者单位：University of Toronto; Vector Institute for Artificial Intelligence
摘要：We examine a higher order approximation to the significance function with increasing numbers of nuisance parameters, based on the normal approximation to an adjusted log-likelihood root. We show that the rate of the correction for nuisance parameters is larger than the correction for non-normality, when the parameter dimensionpisO(n(alpha)) for alpha<12. We specialize the results to linear exponential families and location-scale families and illustrate these with simulations.
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作者：Wang, Gao; Sarkar, Abhishek; Carbonetto, Peter; Stephens, Matthew
作者单位：University of Chicago
摘要：We introduce a simple new approach to variable selection in linear regression, with a particular focus onquantifying uncertainty in which variables should be selected. The approach is based on a new model-the 'sum of single effects' model, called 'SuSiE'-which comes from writing the sparse vector of regression coefficients as a sum of 'single-effect' vectors, each with one non-zero element. We also introduce a corresponding new fitting procedure-iterative Bayesian stepwise selection (IBSS)-whi...
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作者：Mozgunov, Pavel; Jaki, Thomas
作者单位：Lancaster University; University of Cambridge
摘要：The question of selecting the 'best' among different choices is a common problem in statistics. In drug development, our motivating setting, the question becomes, for example, which treatment gives the best response rate. Motivated by recent developments in the theory of context-dependent information measures, we propose a flexible response-adaptive experimental design based on a novel criterion governing treatment arm selections which can be used in adaptive experiments with simple (e.g. bina...
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作者：Mukhopadhyay, Minerva; Li, Didong; Dunson, David B.
作者单位：Indian Institute of Technology System (IIT System); Indian Institute of Technology (IIT) - Kanpur; Duke University
摘要：Current tools for multivariate density estimation struggle when the density is concentrated near a non-linear subspace or manifold. Most approaches require the choice of a kernel, with the multivariate Gaussian kernel by far the most commonly used. Although heavy-tailed and skewed extensions have been proposed, such kernels cannot capture curvature in the support of the data. This leads to poor performance unless the sample size is very large relative to the dimension of the data. The paper pr...
