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作者：Petito, L. C.; Jewell, N. P.
作者单位：University of California System; University of California Berkeley
摘要：Group testing, introduced by Dorfman (1943), has been used to reduce costs when estimating the prevalence of a binary characteristic based on a screening test of k groups that include n independent individuals in total. If the unknown prevalence is low and the screening test suffers from misclassification, it is also possible to obtain more precise prevalence estimates than those obtained from testing all n samples separately (Tu et al., 1994). In some applications, the individual binary respo...
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作者：Xu, Gongjun; Lin, Lifeng; Wei, Peng; Pan, Wei
作者单位：University of Minnesota System; University of Minnesota Twin Cities; University of Minnesota System; University of Minnesota Twin Cities; University of Texas System; University of Texas Health Science Center Houston; University of Texas School Public Health; University of Minnesota System; University of Minnesota Twin Cities
摘要：Several two-sample tests for high-dimensional data have been proposed recently, but they are powerful only against certain alternative hypotheses. In practice, since the true alternative hypothesis is unknown, it is unclear how to choose a powerful test. We propose an adaptive test that maintains high power across a wide range of situations and study its asymptotic properties. Its finite-sample performance is compared with that of existing tests. We apply it and other tests to detect possible ...
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作者：Petersen, Alexander; Mueller, Hans-Georg
作者单位：University of California System; University of California Davis
摘要：For multivariate functional data recorded from a sample of subjects on a common domain, one is often interested in the covariance between pairs of the component functions, extending the notion of a covariance matrix for multivariate data to the functional case. A straightforward approach is to integrate the pointwise covariance matrices over the functional time domain. We generalize this approach by defining the Fr,chet integral, which depends on the metric chosen for the space of covariance m...
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作者：Datta, Jyotishka; Dunson, David B.
作者单位：University of Arkansas System; University of Arkansas Fayetteville; Duke University
摘要：There is growing interest in analysing high-dimensional count data, which often exhibit quasi-sparsity corresponding to an overabundance of zeros and small nonzero counts. Existing methods for analysing multivariate count data via Poisson or negative binomial log-linear hierarchical models with zero-inflation cannot flexibly adapt to quasi-sparse settings. We develop a new class of continuous local-global shrinkage priors tailored to quasi-sparse counts. Theoretical properties are assessed, in...
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作者：Andrieu, C.
作者单位：University of Bristol
摘要：We introduce a simple time-homogeneous Markov embedding of a class of time-inhomogeneous Markov chains widely used in the context of Monte Carlo sampling algorithms, such as systematic-scan Metropolis-within-Gibbs samplers. This allows us to establish that systematic-scan samplers involving two factors are always better than their random-scan counterparts, when asymptotic variance is the criterion of interest. We also show that this embedding sheds some light on the result of Maire et al. (201...
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作者：Yong, Florence H.; Tian, Lu; Yu, Sheng; Cai, Tianxi; Wei, L. J.
作者单位：Harvard University; Stanford University; Tsinghua University
摘要：A common practice in predictive medicine is to use current study data to construct a stratification procedure, which groups subjects according to baseline information and forms stratum-specific prevention or intervention strategies. A desirable stratification scheme would not only have small intra-stratum variation but also have a clinically meaningful discriminatory capability. We show how to obtain optimal stratification rules with such desirable properties from fitting a set of regression m...
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作者：Mealli, Fabrizia; Rubin, Donald B.
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作者：Rao, Vinayak; Lin, Lizhen; Dunson, David B.
作者单位：Purdue University System; Purdue University; University of Texas System; University of Texas Austin; Duke University
摘要：We present a data augmentation scheme to perform Markov chain Monte Carlo inference for models where data generation involves a rejection sampling algorithm. Our idea is a simple scheme to instantiate the rejected proposals preceding each data point. The resulting joint probability over observed and rejected variables can be much simpler than the marginal distribution over the observed variables, which often involves intractable integrals. We consider three problems: modelling flow-cytometry m...
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作者：Dicker, Lee H.; Zhao, Sihai D.
作者单位：Rutgers University System; Rutgers University New Brunswick; University of Illinois System; University of Illinois Urbana-Champaign
摘要：We propose new nonparametric empirical Bayes methods for high-dimensional classification. Our classifiers are designed to approximate the Bayes classifier in a hypothesized hierarchical model, where the prior distributions for the model parameters are estimated nonparametrically from the training data. As is common with nonparametric empirical Bayes, the proposed classifiers are effective in high-dimensional settings even when the underlying model parameters are in fact nonrandom. We use nonpa...
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作者：Lunardon, N.
作者单位：University of Padua
摘要：An adjustment for marginal composite likelihoods is derived to match the second-order theory of the likelihood when inference is for a vector-valued parameter in the absence of nuisance components. The adjustment overcomes the failure of Bartlett identities for marginal composite likelihoods and leads to a Bartlett-correctable marginal composite likelihood ratio statistic.
