Journal of the American Statistical Association

最新文章

• Statistical Modeling of Curves Using Shapes and Related Features AI翻译
  作者：Kurtek, Sebastian; Srivastava, Anuj; Klassen, Eric; Ding, Zhaohua
  作者单位：State University System of Florida; Florida State University; State University System of Florida; Florida State University; Vanderbilt University
  摘要：Motivated by the problems of analyzing protein backbones, diffusion tensor magnetic resonance imaging (DT-MRI) fiber tracts in the human brain, and other problems involving curves, in this study we present some statistical models of parameterized curves, in R-3, in terms of combinations of features such as shape, location, scale, and orientation. For each combination of interest, we identify a representation manifold, endow it with a Riemannian metric, and outline tools for computing sample st...
• Structured, Sparse Aggregation AI翻译
  作者：Percival, Daniel
  作者单位：Carnegie Mellon University
  摘要：This article introduces a method for aggregating many least-squares estimators so that the resulting estimate has two properties: sparsity and structure. That is, only a few candidate covariates are used in the resulting model, and the selected covariates follow some structure over the candidate covariates that is assumed to be known a priori. Although sparsity is well studied in many settings, including aggregation, structured sparse methods are still emerging. We demonstrate a general framew...
• Bayesian Estimation and Prediction for Inhomogeneous Spatiotemporal Log-Gaussian Cox Processes Using Low-Rank Models, With Application to Criminal Surveillance AI翻译
  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...
• Recursively Imputed Survival Trees AI翻译
  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 ...
• Sparse Matrix Graphical Models AI翻译
  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...
• A Consistent Adjacency Spectral Embedding for Stochastic Blockmodel Graphs AI翻译
  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...
• New Weighted Portmanteau Statistics for Time Series Goodness of Fit Testing AI翻译
  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...
• One-Sided and Two-Sided Tolerance Intervals in General Mixed and Random Effects Models Using Small-Sample Asymptotics AI翻译
  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...
• Higher-Order Infinitesimal Robustness AI翻译
  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...
• AdaptSPEC: Adaptive Spectral Estimation for Nonstationary Time Series AI翻译
  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...