Biometrika

最新文章

• Hierarchical Bayes versus empirical Bayes density predictors under general divergence loss AI翻译
  作者：Ghosh, M.; Kubokawa, T.
  作者单位：State University System of Florida; University of Florida; University of Tokyo
  摘要：Consider the problem of finding a predictive density of a new observation drawn independently of observations sampled from a multivariate normal distribution with the same unknown mean vector and the same known variance under general divergence loss. In this paper, we consider two kinds of prior distribution for the mean vector: one is a multivariate normal distribution with mean based on unknown regression coefficients, and the other further assumes that the regression coefficients have unifo...
• Bootstrap of residual processes in regression: to smooth or not to smooth? AI翻译
  作者：Neumeyer, N.; van Keilegom, I.
  作者单位：University of Hamburg; KU Leuven
  摘要：In this paper we consider regression models with centred errors, independent of the covariates. Given independent and identically distributed data and given an estimator of the regression function, which can be parametric or nonparametric in nature, we estimate the distribution of the error term by the empirical distribution of estimated residuals. To approximate the distribution of this estimator, Koul & Lahiri (1994) and Neumeyer (2009) proposed bootstrap procedures based on smoothing the re...
• The debiased Whittle likelihood AI翻译
  作者：Sykulski, Adam M.; Olhede, Sofia C.; Guillaumin, Arthur P.; Lilly, Jonathan M.; Early, Jeffrey J.
  作者单位：Lancaster University; University of London; University College London; NorthWest Research Associates
  摘要：The Whittle likelihood is a widely used and computationally efficient pseudolikelihood. However, it is known to produce biased parameter estimates with finite sample sizes for large classes of models. We propose a method for debiasing Whittle estimates for second-order stationary stochastic processes. The debiased Whittle likelihood can be computed in the same O(n log n) operations as the standard Whittle approach. We demonstrate the superior performance of our method in simulation studies and...
• Semiparametric inference for the dominance index under the density ratio model AI翻译
  作者：Zhuang, W. W.; Hu, B. Y.; Chen, J.
  作者单位：Chinese Academy of Sciences; University of Science & Technology of China, CAS; University of British Columbia; Yunnan University
  摘要：An important and often discussed research problem in statistics is how to compare several populations; examples arise in medical science, engineering, finance and other fields. Often population means or medians are compared. However, one population may have a higher mean income, for example, because of a small number of super-rich individuals; the mean therefore may not reflect the wealth of the general population. Instead, an index of the degree of stochastic dominance of one population over ...
• On marginal and conditional parameters in logistic regression models AI翻译
  作者：Stanghellini, Elena; Doretti, Marco
  作者单位：University of Perugia; University of Perugia
  摘要：We derive the exact formula linking the parameters of marginal and conditional logistic regression models with binary mediators when no conditional independence assumptions can be made. The formula has the appealing property of being the sum of terms that vanish whenever parameters of the conditional models vanish, thereby recovering well-known results as particular cases. It also permits the disentangling of direct and indirect effects as well as quantifying the distortion induced by the omis...
• Unbiased Hamiltonian Monte Carlo with couplings AI翻译
  作者：Heng, J.; Jacob, P. E.
  作者单位：ESSEC Business School; Harvard University
  摘要：We propose a method for parallelization of Hamiltonian Monte Carlo estimators. Our approach involves constructing a pair of Hamiltonian Monte Carlo chains that are coupled in such away that they meet exactly after some random number of iterations. These chains can then be combined so that the resulting estimators are unbiased. This allows us to produce independent replicates in parallel and average them to obtain estimators that are consistent in the limit of the number of replicates, rather t...
• Wasserstein covariance for multiple random densities AI翻译
  作者：Petersen, Alexander; Mueller, Hans-Georg
  作者单位：University of California System; University of California Santa Barbara; University of California System; University of California Davis
  摘要：A common feature of methods for analysing samples of probability density functions is that they respect the geometry inherent to the space of densities. Once a metric is specified for this space, the Frechet mean is typically used to quantify and visualize the average density of the sample. For one-dimensional densities, the Wasserstein metric is popular due to its theoretical appeal and interpretive value as an optimal transport metric, leading to the Wasserstein-Frechet mean or barycentre as...
• Miscellanea Calibrating general posterior credible regions AI翻译
  作者：Syring, Nicholas; Martin, Ryan
  作者单位：University of Illinois System; University of Illinois Chicago; University of Illinois Chicago Hospital; North Carolina State University
  摘要：Calibration of credible regions derived from under- or misspecified models is an important and challenging problem. In this paper, we introduce a scalar tuning parameter that controls the posterior distribution spread, and develop a Monte Carlo algorithm that sets this parameter so that the corresponding credible region achieves the nominal frequentist coverage probability.
• Discussion of Gene hunting with hidden Markov model knockoffs' AI翻译
  作者：Bottolo, L.; Richardson, S.
  作者单位：University of Cambridge; MRC Biostatistics Unit; University of Cambridge
• On causal discovery with an equal-variance assumption AI翻译
  作者：Chen, Wenyu; Drton, Mathias; Wang, Y. Samuel
  作者单位：University of Washington; University of Washington Seattle; Technical University of Munich; University of Chicago
  摘要：Prior work has shown that causal structure can be uniquely identified from observational data when these follow a structural equation model whose error terms have equal variance. We show that this fact is implied by an ordering among conditional variances. We demonstrate that ordering estimates of these variances yields a simple yet state-of-the-art method for causal structure learning that is readily extendable to high-dimensional problems.