2016

最新文章

• Partial least squares for dependent data AI翻译
  作者：Singer, Marco; Krivobokova, Tatyana; Munk, Axel; De Groot, Bert
  作者单位：University of Gottingen; Max Planck Society
  摘要：We consider the partial least squares algorithm for dependent data and study the consequences of ignoring the dependence both theoretically and numerically. Ignoring nonstationary dependence structures can lead to inconsistent estimation, but a simple modification yields consistent estimation. A protein dynamics example illustrates the superior predictive power of the proposed method.
• Empirical Bayes deconvolution estimates AI翻译
  作者：Efron, Bradley
  作者单位：Stanford University
  摘要：An unknown prior density g(theta) has yielded realizations Theta(1), ..., Theta(N.) They are unobservable, but each i produces an observable value Xi according to a known probability mechanism, such as Xi similar to Po(Theta(i)). We wish to estimate g(theta) from the observed sample X-1, ..., X-N. Traditional asymptotic calculations are discouraging, indicating very slow nonparametric rates of convergence. In this article we show that parametric exponential family modelling of g(theta) can giv...
• High-dimensional and banded vector autoregressions AI翻译
  作者：Guo, Shaojun; Wang, Yazhen; Yao, Qiwei
  作者单位：Renmin University of China; University of Wisconsin System; University of Wisconsin Madison; University of London; London School Economics & Political Science
  摘要：We consider a class of vector autoregressive models with banded coefficient matrices. This setting represents a type of sparse structure for high-dimensional time series, although the implied auto-covariance matrices are not banded. The structure is also practically meaningful when the component time series are ordered appropriately. We establish the convergence rates of the estimated banded autoregressive coefficient matrices. We also propose a Bayesian information criterion for determining t...
• Semiparametric approach to regression with a covariate subject to a detection limit AI翻译
  作者：Kong, Shengchun; Nan, Bin
  作者单位：Purdue University System; Purdue University; University of Michigan System; University of Michigan
  摘要：We consider generalized linear regression with a covariate left-censored at a lower detection limit. Complete-case analysis, where observations with values below the limit are eliminated, yields valid estimates for regression coefficients but loses efficiency, ad hoc substitution methods are biased, and parametric maximum likelihood estimation relies on parametric models for the unobservable tail probability distribution and may suffer from model misspecification. To obtain robust and more eff...
• Combining eigenvalues and variation of eigenvectors for order determination AI翻译
  作者：Luo, Wei; Li, Bing
  作者单位：City University of New York (CUNY) System; Baruch College (CUNY); Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
  摘要：In applying statistical methods such as principal component analysis, canonical correlation analysis, and sufficient dimension reduction, we need to determine how many eigenvectors of a random matrix are important for estimation. This problem is known as order determination, and amounts to estimating the rank of a matrix. Previous order-determination procedures rely either on the decreasing pattern, or elbow, of the eigenvalues, or on the increasing pattern of the variability in the directions...
• Bootstrap-based testing of equality of mean functions or equality of covariance operators for functional data AI翻译
  作者：Paparoditis, E.; Sapatinas, T.
  作者单位：University of Cyprus
  摘要：We investigate the properties of a simple bootstrap method for testing the equality of mean functions or of covariance operators in functional data. Theoretical size and power results are derived for certain test statistics, whose limiting distributions depend on unknown infinite-dimensional parameters. Simulations demonstrate good size and power of the bootstrap-based functional tests.
• A Bayesian view of doubly robust causal inference AI翻译
  作者：Saarela, O.; Belzile, L. R.; Stephens, D. A.
  作者单位：University of Toronto; Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne; McGill University
  摘要：In causal inference the effect of confounding may be controlled using regression adjustment in an outcome model, propensity score adjustment, inverse probability of treatment weighting or a combination of these. Approaches based on modelling the treatment assignment mechanism, along with their doubly robust extensions, have been difficult to motivate using formal likelihood-based or Bayesian arguments, as the treatment assignment model plays no part in inferences concerning the expected outcom...
• Going off grid: computationally efficient inference for log-Gaussian Cox processes AI翻译
  作者：Simpson, D.; Illian, J. B.; Lindgren, F.; Sorbye, S. H.; Rue, H.
  作者单位：University of Bath; University of St Andrews; University of Bath; UiT The Arctic University of Tromso; Norwegian University of Science & Technology (NTNU)
  摘要：This paper introduces a new method for performing computational inference on log-Gaussian Cox processes. The likelihood is approximated directly by making use of a continuously specified Gaussian random field. We show that for sufficiently smooth Gaussian random field prior distributions, the approximation can converge with arbitrarily high order, whereas an approximation based on a counting process on a partition of the domain achieves only first-order convergence. The results improve upon th...
• A theoretical study of Stein's covariance estimator AI翻译
  作者：Rajaratnam, Bala; Vincenzi, Dario
  作者单位：Stanford University; Universite Cote d'Azur; Centre National de la Recherche Scientifique (CNRS)
  摘要：Stein proposed an estimator to address the poor performance of the sample covariance matrix for samples of small size. The estimator does not impose sparsity conditions and uses an isotonizing algorithm to preserve the order of the sample eigenvalues. Despite its superior numerical performance, its theoretical properties are not well understood. We demonstrate that Stein's covariance estimator gives modest risk reductions when it is not isotonized, and when it is isotonized the risk reductions...
• A pairwise likelihood-based approach for changepoint detection in multivariate time series models AI翻译
  作者：Ma, Ting Fung; Yau, Chun Yip
  作者单位：Chinese University of Hong Kong
  摘要：This paper develops a composite likelihood-based approach for multiple changepoint estimation in multivariate time series. We derive a criterion based on pairwise likelihood and minimum description length for estimating the number and locations of changepoints and for performing model selection in each segment. The number and locations of the changepoints can be consistently estimated under mild conditions and the computation can be conducted efficiently with a pruned dynamic programming algor...