Biometrika

最新文章

• Goodness-of-fit test in parametric mixed effects models based on estimation of the error distribution AI翻译
  作者：Gonzalez-Manteiga, Wenceslao; Dolores Martinez-Miranda, Maria; Van Keilegom, Ingrid
  作者单位：Universidade de Santiago de Compostela; University of Granada; Universite Catholique Louvain
  摘要：We address the problem of testing for a parametric function of fixed effects in mixed models. We propose a test based on the distance between two empirical error distribution functions, which are constructed from residuals calculated under the opposing hypotheses. The proposed test statistic has power against all alternatives, and its asymptotic distribution is derived. A simulation study shows that the test outperforms others in the literature. The test is applied to longitudinal data from an...
• A goodness-of-fit test for structural nested mean models AI翻译
  作者：Yang, S.; Lok, J. J.
  作者单位：Harvard University; Harvard T.H. Chan School of Public Health
  摘要：Coarse structural nested mean models are tools for estimating treatment effects from longitudinal observational data with time-dependent confounding. There is, however, no guidance on how to specify the treatment effect model, and model misspecification can lead to bias. We derive a goodness-of-fit test based on modified over-identification restrictions tests for evaluating a treatment effect model, and show that our test is doubly robust in the sense that, with a correct treatment effect mode...
• 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...