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作者：Rodrigues, Alexandre; Diggle, Peter J.
作者单位：Universidade Federal do Espirito Santo; Lancaster University; Lancaster University
摘要：In this article, we propose a method for conducting likelihood-based inference for a class of nonstationary spatiotemporal log-Gaussian Cox processes. The method uses convolution-based models to capture spatiotemporal correlation structure, is computationally feasible even for large datasets, and does not require knowledge of the underlying spatial intensity of the process. We describe an application to a surveillance system for detecting emergent spatiotemporal clusters of homicides in Belo H...
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作者：Zhu, Ruoqing; Kosorok, Michael R.
作者单位：University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina; University of North Carolina Chapel Hill
摘要：We propose recursively imputed survival tree (RIST) regression for right-censored data. This new nonparametric regression procedure uses a novel recursive imputation approach combined with extremely randomized trees that allows significantly better use of censored data than previous tree-based methods, yielding improved model fit and reduced prediction error. The proposed method can also be viewed as a type of Monte Carlo EM algorithm, which generates extra diversity in the tree-based fitting ...
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作者：Leng, Chenlei; Tang, Cheng Yong
作者单位：National University of Singapore; Children's Hospital Colorado; University of Colorado System; University of Colorado Anschutz Medical Campus; University of Colorado Denver
摘要：Matrix-variate observations are frequently encountered in many contemporary statistical problems due to a rising need to organize and analyze data with structured information. In this article, we propose a novel sparse matrix graphical model for these types of statistical problems. By penalizing, respectively, two precision matrices corresponding to the rows and columns, our method yields a sparse matrix graphical model that synthetically characterizes the underlying conditional independence s...
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作者：Sussman, Daniel L.; Tang, Minh; Fishkind, Donniell E.; Priebe, Carey E.
作者单位：Johns Hopkins University
摘要：We present a method to estimate block membership of nodes in a random graph generated by a stochastic blockmodel. We use an embedding procedure motivated by the random dot product graph model, a particular example of the latent position model. The embedding associates each node with a vector; these vectors are clustered via minimization of a square error criterion. We prove that this method is consistent for assigning nodes to blocks, as only a negligible number of nodes will be misassigned. W...
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作者：Fisher, Thomas J.; Gallagher, Colin M.
作者单位：University of Missouri System; University of Missouri Kansas City; Clemson University
摘要：We exploit ideas from high-dimensional data analysis to derive new portmanteau tests that are based on the trace of the square of the mth order autocorrelation matrix. The resulting statistics are weighted sums of the squares of the sample autocorrelation coefficients that, unlike many other tests appearing in the literature, are numerically stable even when the number of lags considered is relatively close to the sample size. The statistics behave asymptotically as a linear combination of chi...
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作者：Sharma, Gaurav; Mathew, Thomas
作者单位：University System of Maryland; University of Maryland Baltimore
摘要：The computation of tolerance intervals in mixed and random effects models has not been satisfactorily addressed in a general setting when the data are unbalanced and/or when covariates are present. This article derives satisfactory one-sided and two-sided tolerance intervals in such a general scenario, by applying small-sample asymptotic procedures. In the case of one-sided tolerance limits, the problem reduces to the interval estimation of a percentile, and accurate confidence limits are deri...
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作者：La Vecchia, Davide; Ronchetti, Elvezio; Trojani, Fabio
作者单位：Monash University; University of Geneva; University of Geneva; Universita della Svizzera Italiana; Swiss Finance Institute (SFI)
摘要：Using the von Mises expansion, we study the higher-order infinitesimal robustness of a general M-functional and characterize its second-order properties. We show that second-order robustness is equivalent to the boundedness of both the estimator's estimating function and its derivative with respect to the parameter. It implies, at the same time, (i) variance robustness and (ii) robustness of higher-order saddlepoint approximations to the estimator's finite sample density. The proposed construc...
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作者：Rosen, Ori; Wood, Sally; Stoffer, David S.
作者单位：University of Texas System; University of Texas El Paso; University of Melbourne; Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh
摘要：We propose a method for analyzing possibly nonstationary time series by adaptively dividing the time series into an unknown but finite number of segments and estimating the corresponding Meal spectra by smoothing splines. The model is formulated in a Bayesian framework, and the estimation relies on reversible jump Markov chain Monte Carlo (RJMCMC) methods. For a given segmentation of the time,series, the likelihood function is approximated via a product of local Whittle likelihoods. Thus, no p...
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作者：Hai Nguyen; Cressie, Noel; Braverman, Amy
作者单位：California Institute of Technology; National Aeronautics & Space Administration (NASA); NASA Jet Propulsion Laboratory (JPL); University System of Ohio; Ohio State University; University System of Ohio; Ohio State University
摘要：Aerosols are tiny solid or liquid particles suspended in the atmosphere; examples of aerosols include windblown dust, sea salts, volcanic ash, smoke from wildfires, and pollution from factories. The global distribution of aerosols is a topic of great interest in climate studies since aerosols can either cool or warm the atmosphere depending on their location, type, and interaction with clouds. Aerosol concentrations are important input components of global climate models, and it is crucial to ...
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作者：Picciotto, Sally; Hernan, Miguel A.; Page, John H.; Young, Jessica G.; Robins, James M.
作者单位：Harvard University; Harvard T.H. Chan School of Public Health; Harvard University; Harvard T.H. Chan School of Public Health; Harvard University; Harvard T.H. Chan School of Public Health
摘要：In the presence of time-varying confounders affected by prior treatment, standard statistical methods for failure time analysis may be biased. Methods that correctly adjust for this type of covariate include the parametric g-formula, inverse probability weighted estimation of marginal structural Cox proportional hazards models, and g-estimation of structural nested accelerated failure time models. In this article, we propose a novel method to estimate the causal effect of a time-dependent trea...
