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作者：Wu, Hulin; Lu, Tao; Xue, Hongqi; Liang, Hua
作者单位：University of Rochester; State University of New York (SUNY) System; University at Albany, SUNY; George Washington University
摘要：The gene regulation network (GRN) is a high-dimensional complex system, which can be represented by various mathematical or statistical models. The ordinary differential equation (ODE) model is one of the popular dynamic GRN models. High-dimensional linear ODE models have been proposed to identify GRNs, but with a limitation of the linear regulation effect assumption. In this article, we propose a sparse additive ODE (SA-ODE) model, coupled with ODE estimation methods and adaptive group least ...
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作者：Cook, R. Dennis; Zhang, Xin
作者单位：University of Minnesota System; University of Minnesota Twin Cities
摘要：When studying the regression of a univariate variable Y on a vector x of predictors, most existing sufficient dimension-reduction (SDR) methods require the construction of slices of Y to estimate moments of the conditional distribution of X given Y. But there is no widely accepted method for choosing the number of slices, while a poorly chosen slicing scheme may produce miserable results. We propose a novel and easily implemented fusing method that can mitigate the problem of choosing a slicin...
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作者：Rockova, Veronika; George, Edward I.
作者单位：University of Pennsylvania
摘要：Despite rapid developments in stochastic search algorithms, the practicality of Bayesian variable selection methods has continued to pose challenges. High-dimensional data are now routinely analyzed, typically with many more covariates than observations. To broaden the applicability of Bayesian variable selection for such high-dimensional linear regression contexts, we propose EMVS, a deterministic alternative to stochastic search based on an EM algorithm which exploits a conjugate mixture pri...
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作者：de Carvalho, Miguel; Davison, Anthony C.
作者单位：Pontificia Universidad Catolica de Chile; Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne
摘要：The modeling of multivariate extremes has received increasing recent attention because of its importance in risk assessment. In classical statistics of extremes, the joint distribution of two or more extremes has a nonparametric form, subject to moment constraints. This article develops a semiparametric model for the situation where several multivariate extremal distributions are linked through the action of a covariate on an unspecified baseline distribution, through a so-called density ratio...
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作者：Koenker, Roger; Mizera, Ivan
作者单位：University of Illinois System; University of Illinois Urbana-Champaign; University of Alberta
摘要：Estimation of mixture densities for the classical Gaussian compound decision problem and their associated (empirical) Bayes rules is considered from two new perspectives. The first, motivated by Brown and Greenshtein, introduces a nonparametric maximum likelihood estimator of the mixture density subject to a monotonicity constraint on the resulting Bayes rule. The second, motivated by Jiang and Zhang, proposes a new approach to computing the Kiefer-Wolfowitz nonparametric maximum likelihood es...
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作者：Chen, Kun; Chan, Kung-Sik; Stenseth, Nils Chr.
作者单位：University of Connecticut; University of Iowa; University of Oslo
摘要：The problem of reconstructing the source-sink dynamics arises in many biological systems. Our research is motivated by marine applications where newborns are passively dispersed by ocean currents from several potential spawning sources to settle in various nursery regions that collectively constitute the sink. The reconstruction of the sparse source-sink linkage pattern, that is, to identify which sources contribute to which regions in the sink, is a challenging task in marine ecology. We deri...
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作者：Crawford, Forrest W.; Minin, Vladimir N.; Suchard, Marc A.
作者单位：Yale University; University of Washington; University of Washington Seattle; University of California System; University of California Los Angeles; University of California System; University of California Los Angeles
摘要：Birth-death processes (BDPs) are continuous-time Markov chains that track the number of particles in a system over time. While widely used in population biology, genetics, and ecology, statistical inference of the instantaneous particle birth and death rates remains largely limited to restrictive linear BDPs in which per-particle birth and death rates are constant. Researchers often observe the number of particles at discrete times, necessitating data augmentation procedures such as expectatio...
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作者：Zheng, Shuzhuan; Yang, Lijian; Haerdle, Wolfgang K.
作者单位：Michigan State University; Soochow University - China; Humboldt University of Berlin; Singapore Management University
摘要：Functional data analysis (FDA) has become an important area of statistics research in the recent decade, yet a smooth simultaneous confidence corridor (SCC) does not exist in the literature for the mean function of sparse functional data. SCC is a powerful tool for making statistical inference on an entire unknown function, nonetheless classic Hungarian embedding techniques for establishing asymptotic correctness of SCC completely fail for sparse functional data. We propose a local linear SCC ...
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作者：Chan, Ngai Hang; Yau, Chun Yip; Zhang, Rong-Mao
作者单位：Chinese University of Hong Kong; Renmin University of China; Zhejiang University
摘要：Consider a structural break autoregressive (SBAR) process Y-1 = Sigma(m+1)(j=1) (sic)beta jT Yt-1 + sigma(Yt-1, ...,Yt-q)epsilon 1(sic) I(t(j-1) <= t < t(1) < ... < t(m) vertical bar 1 = n + 1, sigma (.) is a measurable function on R-q, and {epsilon(t)} are white noise with unit variance. In practice, the number of change-points m is usually assumed to be known and small, because a large m would involve a huge amount of computational burden for parameters estimation. By reformulating the probl...
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作者：Delaigle, Aurore; Hall, Peter
作者单位：University of Melbourne; University of California System; University of California Davis
摘要：Nonparametric estimation of a density from contaminated data is a difficult problem, for which convergence rates are notoriously slow. We introduce parametrically assisted nonparametric estimators which can dramatically improve on the performance of standard nonparametric estimators when the assumed model is close to the true density, without degrading much the quality of purely nonparametric estimators in other cases. We establish optimal convergence rates for our problem and discuss estimato...
