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作者：Binkiewicz, N.; Vogelstein, J. T.; Rohe, K.
作者单位：University of Wisconsin System; University of Wisconsin Madison; Johns Hopkins University
摘要：Biological and social systems consist of myriad interacting units. The interactions can be represented in the form of a graph or network. Measurements of these graphs can reveal the underlying structure of these interactions, which provides insight into the systems that generated the graphs. Moreover, in applications such as connectomics, social networks, and genomics, graph data are accompanied by contextualizing measures on each node. We utilize these node covariates to help uncover latent c...
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作者：Farewell, D. M.; Huang, C.; Didelez, V.
作者单位：Cardiff University; Cardiff University; Leibniz Association; Leibniz Institute for Prevention Research & Epidemiology (BIPS)
摘要：Likelihood factors that can be disregarded for inference are termed ignorable. We demonstrate that close ties exist between ignorability and identification of causal effects by covariate adjustment. A graphical condition, stability, plays a role analogous to that of missingness at random, but is applicable to general longitudinal data. Our formulation of ignorability does not depend on any notion of missing data, so is appealing in situations where missing data may not actually exist. Several ...
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作者：Lin, Yuanyuan; Luo, Yang; Xie, Shangyu; Chen, Kani
作者单位：Chinese University of Hong Kong; Hong Kong University of Science & Technology; University of International Business & Economics
摘要：Semiparametric transformation models with random effects are useful in analysing recurrent and clustered data. With specified error and random effect distributions, Zeng & Lin (2007a) proved that nonparametric maximum likelihood estimators are semiparametric efficient. In this paper we consider a more general class of transformation models with random effects, under which an unknown monotonic transformation of the response is linearly related to the covariates and the random effects with unspe...
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作者：Chang, Jinyuan; Yao, Qiwei; Zhou, Wen
作者单位：Southwestern University of Finance & Economics - China; University of London; London School Economics & Political Science; Colorado State University System; Colorado State University Fort Collins
摘要：We propose a new omnibus test for vector white noise using the maximum absolute auto-correlations and cross-correlations of the component series. Based on an approximation by the L-infinity-norm of a normal random vector, the critical value of the test can be evaluated by bootstrapping from a multivariate normal distribution. In contrast to the conventional white noise test, the new method is proved to be valid for testing departure from white noise that is not independent and identically dist...
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作者：Han, Fang; Chen, Shizhe; Liu, Han
作者单位：University of Washington; University of Washington Seattle; Columbia University; Princeton University
摘要：We consider the testing of mutual independence among all entries in a d-dimensional random vector based on n independent observations. We study two families of distribution-free test statistics, which include Kendall's tau and Spearman's rho as important examples. We show that under the null hypothesis the test statistics of these two families converge weakly to Gumbel distributions, and we propose tests that control the Type I error in the high-dimensional setting where d > n. We further show...
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作者：Singh, S. S.; Lindsten, F.; Moulines, E.
作者单位：University of Cambridge; Uppsala University; Institut Polytechnique de Paris; Ecole Polytechnique; ENSTA Paris
摘要：Sampling from the posterior probability distribution of the latent states of a hidden Markov model is nontrivial even in the context of Markov chain Monte Carlo. To address this, Andrieu et al. (2010) proposed a way of using a particle filter to construct a Markov kernel that leaves the posterior distribution invariant. Recent theoretical results have established the uniform ergodicity of this Markov kernel and shown that the mixing rate does not deteriorate provided the number of particles gr...
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作者：Canale, A.; Lijoi, A.; Nipoti, B.; Prunster, I.
作者单位：University of Padua; Bocconi University; Trinity College Dublin
摘要：For the most popular discrete nonparametric models, beyond the Dirichlet process, the prior guess at the shape of the data-generating distribution, also known as the base measure, is assumed to be diffuse. Such a specification greatly simplifies the derivation of analytical results, allowing for a straightforward implementation of Bayesian nonparametric inferential procedures. However, in several applied problems the available prior information leads naturally to the incorporation of an atom i...
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作者：Eck, D. J.; Cook, R. D.
作者单位：University of Minnesota System; University of Minnesota Twin Cities
摘要：Envelope methodology can provide substantial efficiency gains in multivariate statistical problems, but in some applications the estimation of the envelope dimension can induce selection volatility that may mitigate those gains. Current envelope methodology does not account for the added variance that can result from this selection. In this article, we circumvent dimension selection volatility through the development of a weighted envelope estimator. Theoretical justification is given for our ...
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作者：Shin, Seung Jun; Wu, Yichao; Zhang, Hao Helen; Liu, Yufeng
作者单位：Korea University; North Carolina State University; University of Arizona; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina School of Medicine
摘要：Sufficient dimension reduction is popular for reducing data dimensionality without stringent model assumptions. However, most existing methods may work poorly for binary classification. For example, sliced inverse regression (Li, 1991) can estimate at most one direction if the response is binary. In this paper we propose principal weighted support vector machines, a unified framework for linear and nonlinear sufficient dimension reduction in binary classification. Its asymptotic properties are...
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作者：Liu, Yukun; Li, Pengfei; Qin, Jing
作者单位：East China Normal University; University of Waterloo; National Institutes of Health (NIH) - USA; NIH National Institute of Allergy & Infectious Diseases (NIAID)
摘要：Capture-recapture experiments are widely used to collect data needed for estimating the abundance of a closed population. To account for heterogeneity in the capture probabilities, Huggins (1989) and Alho (1990) proposed a semiparametric model in which the capture probabilities are modelled parametrically and the distribution of individual characteristics is left unspecified. A conditional likelihood method was then proposed to obtain point estimates andWald-type confidence intervals for the a...
