Biometrika

最新文章

• Semiparametric causal inference in matched cohort studies AI翻译
  作者：Kennedy, E. H.; Sjolander, A.; Small, D. S.
  作者单位：University of Pennsylvania; Karolinska Institutet; University of Pennsylvania
  摘要：Odds ratios can be estimated in case-control studies using standard logistic regression, ignoring the outcome-dependent sampling. In this paper we discuss an analogous result for treatment effects on the treated in matched cohort studies. Specifically, in studies where a sample of treated subjects is observed along with a separate sample of possibly matched controls, we show that efficient and doubly robust estimators of effects on the treated are computationally equivalent to standard estimat...
• Envelopes and reduced-rank regression AI翻译
  作者：Cook, R. Dennis; Forzani, Liliana; Zhang, Xin
  作者单位：University of Minnesota System; University of Minnesota Twin Cities; National University of the Littoral; State University System of Florida; Florida State University
  摘要：We incorporate the nascent idea of envelopes (Cook et al., Statist. Sinica 20, 927-1010) into reduced-rank regression by proposing a reduced-rank envelope model, which is a hybrid of reduced-rank and envelope regressions. The proposed model has total number of parameters no more than either of reduced-rank regression or envelope regression. The resulting estimator is at least as efficient as both existing estimators. The methodology of this paper can be adapted to other envelope models, such a...
• Variance bounding and geometric ergodicity of Markov chain Monte Carlo kernels for approximate Bayesian computation AI翻译
  作者：Lee, Anthony; Latuszynski, Krzysztof
  作者单位：University of Warwick
  摘要：Approximate Bayesian computation has emerged as a standard computational tool when dealing with intractable likelihood functions in Bayesian inference. We show that many common Markov chain Monte Carlo kernels used to facilitate inference in this setting can fail to be variance bounding and hence geometrically ergodic, which can have consequences for the reliability of estimates in practice. This phenomenon is typically independent of the choice of tolerance in the approximation. We prove that...
• The mode functional is not elicitable AI翻译
  作者：Heinrich, C.
  作者单位：Ruprecht Karls University Heidelberg
  摘要：This article is concerned with point forecasting of a real-valued random variable with a general Lebesgue density. Answering a question of Gneiting (2011), it is shown that the mode is not elicitable, or, in other words, that it is impossible to find a loss or scoring function under which the mode is the Bayes predictor.
• Sparse precision matrix estimation via lasso penalized D-trace loss AI翻译
  作者：Zhang, Teng; Zou, Hui
  作者单位：Princeton University; University of Minnesota System; University of Minnesota Twin Cities
  摘要：We introduce a constrained empirical loss minimization framework for estimating high-dimensional sparse precision matrices and propose a new loss function, called the D-trace loss, for that purpose. A novel sparse precision matrix estimator is defined as the minimizer of the lasso penalized D-trace loss under a positive-definiteness constraint. Under a new irrepresentability condition, the lasso penalized D-trace estimator is shown to have the sparse recovery property. Examples demonstrate tha...
• Logistic regression for spatial Gibbs point processes AI翻译
  作者：Baddeley, Adrian; Coeurjolly, Jean-Francois; Rubak, Ege; Waagepetersen, Rasmus
  作者单位：University of Western Australia; Communaute Universite Grenoble Alpes; Institut National Polytechnique de Grenoble; Universite Grenoble Alpes (UGA); Centre National de la Recherche Scientifique (CNRS); Inria; Aalborg University
  摘要：We propose a computationally efficient technique, based on logistic regression, for fitting Gibbs point process models to spatial point pattern data. The score of the logistic regression is an unbiased estimating function and is closely related to the pseudolikelihood score. Implementation of our technique does not require numerical quadrature, and thus avoids a source of bias inherent in other methods. For stationary processes, we prove that the parameter estimator is strongly consistent and ...
• Circular designs balanced for neighbours at distances one and two AI翻译
  作者：Aldred, R. E. L.; Bailey, R. A.; Mckay, Brendan D.; Wanless, Ian M.
  作者单位：University of Otago; University of St Andrews; Australian National University; Monash University
  摘要：We define three types of neighbour-balanced designs for experiments where the units are arranged in a circle or single line in space or time. The designs are balanced with respect to neighbours at distance one and at distance two. The variants come from allowing or forbidding self-neighbours, and from considering neighbours to be directed or undirected. For two of the variants, we give a method of constructing a design for all values of the number of treatments, except for some small values wh...
• Transformed sufficient dimension reduction AI翻译
  作者：Wang, T.; Guo, X.; Zhu, L.; Xu, P.
  作者单位：Hong Kong Baptist University; Southeast University - China
  摘要：We propose a general framework for dimension reduction in regression to fill the gap between linear and fully nonlinear dimension reduction. The main idea is to first transform each of the raw predictors monotonically and then search for a low-dimensional projection in the space defined by the transformed variables. Both user-specified and data-driven transformations are suggested. In each case, the methodology is first discussed in generality and then a representative method is proposed and e...
• Distances and inference for covariance operators AI翻译
  作者：Pigoli, Davide; Aston, John A. D.; Dryden, Ian L.; Secchi, Piercesare
  作者单位：University of Warwick; University of Cambridge; University of Nottingham; Polytechnic University of Milan
  摘要：A framework is developed for inference concerning the covariance operator of a functional random process, where the covariance operator itself is an object of interest for statistical analysis. Distances for comparing positive-definite covariance matrices are either extended or shown to be inapplicable to functional data. In particular, an infinite-dimensional analogue of the Procrustes size-and-shape distance is developed. Convergence of finite-dimensional approximations to the infinite-dimen...
• Permuting regular fractional factorial designs for screening quantitative factors AI翻译
  作者：Tang, Yu; Xu, Hongquan
  作者单位：Soochow University - China; University of California System; University of California Los Angeles
  摘要：Fractional factorial designs arewidely used in screening experiments. They are often chosen by the minimum aberration criterion, which regards factor levels as symbols. For designs with quantitative factors, however, permuting the levels for one or more factors could alter their geometrical structures and statistical properties. We provide a justification of the minimum beta-aberration criterion for quantitative factors and study level permutations for regular fractional factorial designs in o...