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作者：Mainassara, Yacouba Boubacar; Saussereau, Bruno
作者单位：Universite Marie et Louis Pasteur
摘要：In this paper, we derive the asymptotic distribution of normalized residual empirical autocovariances and autocorrelations under weak assumptions on the noise. We propose new portmanteau statistics for vector autoregressive moving average models with uncorrelated but nonindependent innovations by using a self-normalization approach. We establish the asymptotic distribution of the proposed statistics. This asymptotic distribution is quite different from the usual chi-squared approximation used ...
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作者：Sommerfeld, Max; Sain, Stephan; Schwartzman, Armin
作者单位：University of Gottingen; University of California System; University of California San Diego
摘要：The goal of this article is to give confidence regions for the excursion set of a spatial function above a given threshold from repeated noisy observations on a fine grid of fixed locations. Given an asymptotically Gaussian estimator of the target function, a pair of data-dependent nested excursion sets are constructed that are sub- and super-sets of the true excursion set, respectively, with a desired confidence. Asymptotic coverage probabilities are determined via a multiplier bootstrap meth...
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作者：Wang, HaiYing; Zhu, Rong; Ma, Ping
作者单位：University System Of New Hampshire; University of New Hampshire; University of Connecticut; Chinese Academy of Sciences; Academy of Mathematics & System Sciences, CAS; University System of Georgia; University of Georgia
摘要：For massive data, the family of subsampling algorithms is popular to downsize the data volume and reduce computational burden. Existing studies focus on approximating the ordinary least-square estimate in linear regression, where statistical leverage scores are often used to define subsampling probabilities. In this article, we propose fast subsampling algorithms to efficiently approximate the maximum likelihood estimate in logistic regression. We first establish consistency and asymptotic nor...
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作者：Kong, Shengchun; Nan, Bin; Kalbfleisch, John D.; Saran, Rajiv; Hirth, Richard
作者单位：Gilead Sciences; University of Michigan System; University of Michigan; University of Michigan System; University of Michigan; University of Michigan System; University of Michigan
摘要：We consider a random effects model for longitudinal data with the occurrence of an informative terminal event that is subject to right censoring. Existing methods for analyzing such data include the joint modeling approach using latent frailty and the marginal estimating equation approach using inverse probability weighting; in both cases the effect of the terminal event on the response variable is not explicit and thus not easily interpreted. In contrast, we treat the terminal event time as a...
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作者：Linero, Antonio R.
作者单位：State University System of Florida; Florida State University
摘要：Decision tree ensembles are an extremely popular tool for obtaining high-quality predictions in nonparametric regression problems. Unmodified, however, many commonly used decision tree ensemble methods do not adapt to sparsity in the regime in which the number of predictors is larger than the number of observations. A recent stream of research concerns the construction of decision tree ensembles that are motivated by a generative probabilistic model, the most influential method being the Bayes...
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作者：Guo, Beibei; Yuan, Ying
作者单位：University of Texas System; UTMD Anderson Cancer Center
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作者：Hilton, Ross P.; Zheng, Yuchen; Serban, Nicoleta
作者单位：University System of Georgia; Georgia Institute of Technology
摘要：We introduce a modeling approach for characterizing heterogeneity in healthcare utilization using massive medical claims data. We first translate the medical claims observed for a large study population and across five years into individual-level discrete events of care called utilization sequences. We model the utilization sequences using an exponential proportional hazards mixture model to capture heterogeneous behaviors in patients' healthcare utilization. The objective is to cluster patien...
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作者：Thomas, Zachary M.; MacEachern, Steven N.; Peruggia, Mario
作者单位：Eli Lilly; University System of Ohio; Ohio State University
摘要：Methods for summarizing case influence in Bayesian models take essentially two forms: (1) use common divergence measures for calculating distances between the full-data posterior and the case-deleted posterior, and (2) measure the impact of infinitesimal perturbations to the likelihood to study local case influence. Methods based on approach (1) lead naturally to considering the behavior of case-deletion importance sampling weights (the weights used to approximate samples from the case-deleted...
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作者：Chen, Kehui; Lei, Jing
作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh; Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh; Carnegie Mellon University
摘要：The stochastic block model (SBM) and its variants have been a popular tool for analyzing large network data with community structures. In this article, we develop an efficient network cross-validation (NCV) approach to determine the number of communities, as well as to choose between the regular stochastic block model and the degree corrected block model (DCBM). The proposed NCV method is based on a block-wise node-pair splitting technique, combined with an integrated step of community recover...
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作者：Crawford, Forrest W.; Wu, Jiacheng; Heimer, Robert
作者单位：Yale University; Yale University; Yale University; Yale University
摘要：Estimating the size of stigmatized, hidden, or hard-to-reach populations is a major problem in epidemiology, demography, and public health research. Capture-recapture and multiplier methods are standard tools for inference of hidden population sizes, but they require random sampling of target population members, which is rarely possible. Respondent-driven sampling (RDS) is a survey method for hidden populations that relies on social link tracing. The RDS recruitment process is designed to spre...
