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作者：Griffin, Jim E.; Leisen, Fabrizio
作者单位：University of Kent
摘要：A new class of dependent random measures which we call compound random measures is proposed and the use of normalized versions of these random measures as priors in Bayesian non-parametric mixture models is considered. Their tractability allows the properties of both compound random measures and normalized compound random measures to be derived. In particular, we show how compound random measures can be constructed with gamma, sigma-stable and generalized gamma process marginals. We also deriv...
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作者：Lu, Shu; Liu, Yufeng; Yin, Liang; Zhang, Kai
作者单位：University of North Carolina; University of North Carolina Chapel Hill
摘要：Sparse regression techniques have been popular in recent years because of their ability in handling high dimensional data with built-in variable selection. The lasso is perhaps one of the most well-known examples. Despite intensive work in this direction, how to provide valid inference for sparse regularized methods remains a challenging statistical problem. We take a unique point of view of this problem and propose to make use of stochastic variational inequality techniques in optimization to...
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作者：Stadler, Nicolas; Mukherjee, Sach
作者单位：Netherlands Cancer Institute; Helmholtz Association; German Center for Neurodegenerative Diseases (DZNE)
摘要：We propose new methodology for two-sample testing in high dimensional models. The methodology provides a high dimensional analogue to the classical likelihood ratio test and is applicable to essentially any model class where sparse estimation is feasible. Sparse structure is used in the construction of the test statistic. In the general case, testing then involves non-nested model comparison, and we provide asymptotic results for the high dimensional setting. We put forward computationally eff...
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作者：Fang, Ethan X.; Ning, Yang; Liu, Han
作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; Cornell University; Princeton University
摘要：The paper considers the problem of hypothesis testing and confidence intervals in high dimensional proportional hazards models. Motivated by a geometric projection principle, we propose a unified likelihood ratio inferential framework, including score, Wald and partial likelihood ratio statistics for hypothesis testing. Without assuming model selection consistency, we derive the asymptotic distributions of these test statistics, establish their semiparametric optimality and conduct power analy...
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作者：Radchenko, Peter; Mukherjee, Gourab
作者单位：University of Southern California; University of Sydney
摘要：We study the large sample behaviour of a convex clustering framework, which minimizes the sample within cluster sum of squares under an l(1) fusion constraint on the cluster centroids. This recently proposed approach has been gaining in popularity; however, its asymptotic properties have remained mostly unknown. Our analysis is based on a novel representation of the sample clustering procedure as a sequence of cluster splits determined by a sequence of maximization problems. We use this repres...
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作者：Bastide, Paul; Mariadassou, Mahendra; Robin, Stephane
作者单位：AgroParisTech; Universite Paris Saclay; INRAE; INRAE; Universite Paris Saclay
摘要：Comparative and evolutive ecologists are interested in the distribution of quantitative traits between related species. The classical framework for these distributions consists of a random process running along the branches of a phylogenetic tree relating the species. We consider shifts in the process parameters, which reveal fast adaptation to changes of ecological niches. We show that models with shifts are not identifiable in general. Constraining the models to be parsimonious in the number...
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作者：Sun, Will Wei; Lu, Junwei; Liu, Han; Cheng, Guang
作者单位：Yahoo! Inc; Princeton University; Purdue University System; Purdue University
摘要：We propose a novel sparse tensor decomposition method, namely the tensor truncated power method, that incorporates variable selection in the estimation of decomposition components. The sparsity is achieved via an efficient truncation step embedded in the tensor power iteration. Our method applies to a broad family of high dimensional latent variable models, including high dimensional Gaussian mixtures and mixtures of sparse regressions. A thorough theoretical investigation is further conducted...
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作者：Botev, Z. I.
作者单位：University of New South Wales Sydney
摘要：Simulation from the truncated multivariate normal distribution in high dimensions is a recurrent problem in statistical computing and is typically only feasible by using approximate Markov chain Monte Carlo sampling. We propose a minimax tilting method for exact independently and identically distributed data simulation from the truncated multivariate normal distribution. The new methodology provides both a method for simulation and an efficient estimator to hitherto intractable Gaussian integr...
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作者：Perry, Patrick O.
作者单位：New York University
摘要：Hierarchical models allow for heterogeneous behaviours in a population while simultaneously borrowing estimation strength across all subpopulations. Unfortunately, existing likelihood-based methods for fitting hierarchical models have high computational demands, and these demands have limited their adoption in large-scale prediction and inference problems. The paper proposes a moment-based procedure for estimating the parameters of a hierarchical model which has its roots in a method originall...
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作者：Clertant, M.; O'Quigley, J.
作者单位：Sorbonne Universite
摘要：We describe a new class of dose finding methods to be used in early phase clinical trials. Under some added parametric conditions the class reduces to the family of continual reassessment method (CRM) designs. Under some relaxation of the underlying structure the method is equivalent to the cumulative cohort design, the modified toxicity probability interval method or Bayesian optimal interval design classes of methods, which are non-parametric in nature whereas the CRM class can be viewed as ...
