Biometrika

最新文章

• Covariance-based analyses of biological pathways AI翻译
  作者：Danaher, P.; Paul, D.; Wang, P.
  作者单位：NanoString Technologies; University of California System; University of California Davis; Icahn School of Medicine at Mount Sinai
  摘要：The use of high-throughput data to study the changing behaviour of biological pathways has focused mainly on examining the changes in the means of pathway genes. In this paper, we propose instead to test for changes in the co-regulated and unregulated variability of pathway genes. We assume that the eigenvalues of previously defined pathways capture biologically relevant quantities, and we develop a test for biologically meaningful changes in the eigenvalues between classes. This test reflects...
• Using covariate-specific disease prevalence information to increase the power of case-control studies AI翻译
  作者：Qin, Jing; Zhang, Han; Li, Pengfei; Albanes, Demetrius; Yu, Kai
  作者单位：National Institutes of Health (NIH) - USA; NIH National Institute of Allergy & Infectious Diseases (NIAID); National Institutes of Health (NIH) - USA; NIH National Cancer Institute (NCI); NIH National Cancer Institute- Division of Cancer Epidemiology & Genetics; University of Waterloo
  摘要：Public registration databases and large cohort studies provide vital information on disease prevalence at various levels of a risk factor. This auxiliary information can be helpful in conducting statistical inference in a new study. We aim to develop a statistical procedure that improves the efficiency of the logistic regression model for a case-control study by utilizing auxiliary information on covariate-specific disease prevalence via a series of unbiased estimating equations. We adopt empi...
• Bayesian inference for partially observed stochastic differential equations driven by fractional Brownian motion AI翻译
  作者：Beskos, A.; Dureau, J.; Kalogeropoulos, K.
  作者单位：University of London; University College London; University of London; London School Economics & Political Science
  摘要：We consider continuous-time diffusion models driven by fractional Brownian motion. Observations are assumed to possess a nontrivial likelihood given the latent path. Due to the non-Markovian and high-dimensional nature of the latent path, estimating posterior expectations is computationally challenging. We present a reparameterization framework based on the Davies and Harte method for sampling stationary Gaussian processes and use it to construct a Markov chain Monte Carlo algorithm that allow...
• Entropy testing for nonlinear serial dependence in time series AI翻译
  作者：Giannerini, Simone; Maasoumi, Esfandiar; Dagum, Estela Bee
  作者单位：University of Bologna; Emory University
  摘要：We propose tests for nonlinear serial dependence in time series under the null hypothesis of general linear dependence, in contrast to the more widely studied null hypothesis of independence. The approach is based on combining an entropy dependence metric, which possesses many desirable properties and is used as a test statistic, with a suitable extension of surrogate data methods, a class of Monte Carlo distribution-free tests for nonlinearity, and a smoothed sieve bootstrap scheme. We show h...
• Bayesian sensitivity analysis with the Fisher-Rao metric AI翻译
  作者：Kurtek, Sebastian; Bharath, Karthik
  作者单位：University System of Ohio; Ohio State University; University of Nottingham
  摘要：We propose a geometric framework to assess sensitivity of Bayesian procedures to modelling assumptions based on the nonparametric Fisher-Rao metric. While the framework is general, the focus of this article is on assessing local and global robustness in Bayesian procedures with respect to perturbations of the likelihood and prior, and on the identification of influential observations. The approach is based on a square-root representation of densities, which enables analytical computation of ge...
• Effective degrees of freedom: a flawed metaphor AI翻译
  作者：Janson, Lucas; Fithian, William; Hastie, Trevor J.
  作者单位：Stanford University
  摘要：To most applied statisticians, a fitting procedure's degrees of freedom is synonymous with its model complexity, or its capacity for overfitting to data. In particular, the degrees of freedom is often used to parameterize the bias-variance trade-off in model selection. We argue that, on the contrary, model complexity and degrees of freedom may correspond very poorly. We exhibit and theoretically explore various fitting procedures for which the degrees of freedom is not monotonic in the model c...
• Efficient implementation of Markov chain Monte Carlo when using an unbiased likelihood estimator AI翻译
  作者：Doucet, A.; Pitt, M. K.; Deligiannidis, G.; Kohn, R.
  作者单位：University of Oxford; University of Warwick; University of New South Wales Sydney
  摘要：When an unbiased estimator of the likelihood is used within a Metropolis-Hastings chain, it is necessary to trade off the number of Monte Carlo samples used to construct this estimator against the asymptotic variances of the averages computed under this chain. Using many Monte Carlo samples will typically result in Metropolis-Hastings averages with lower asymptotic variances than the corresponding averages that use fewer samples; however, the computing time required to construct the likelihood...
• A tractable and interpretable four-parameter family of unimodal distributions on the circle AI翻译
  作者：Kato, Shogo; Jones, M. C.
  作者单位：Research Organization of Information & Systems (ROIS); Institute of Statistical Mathematics (ISM) - Japan; Open University - UK
  摘要：This article presents a class of four-parameter distributions for circular data that are unimodal, possess simple characteristic and density functions and a tractable distribution function, can be interpretably parameterized directly in terms of their trigonometric moments, afford a very wide range of skewness and kurtosis, envelop numerous interesting submodels including the wrapped Cauchy and cardioid distributions, allow straightforward parameter estimation by both method of moments and max...
• Locally optimal designs for errors-in-variables models AI翻译
  作者：Konstantinou, M.; Dette, H.
  作者单位：Ruhr University Bochum
  摘要：We consider the construction of optimal designs for nonlinear regression models when there are measurement errors in the covariates. Corresponding approximate design theory is developed for maximum likelihood and least-squares estimation, with the latter leading to nonconcave optimization problems. Analytical characterizations of the locally D-optimal saturated designs are provided for the Michaelis-Menten, E-max and exponential regression models. Through concrete applications, we illustrate h...
• A validated information criterion to determine the structural dimension in dimension reduction models AI翻译
  作者：Ma, Yanyuan; Zhang, Xinyu
  作者单位：University of South Carolina System; University of South Carolina Columbia; Capital University of Economics & Business
  摘要：A crucial component of performing sufficient dimension reduction is to determine the structural dimension of the reduction model. We propose a novel information criterion-based method for this purpose, a special feature of which is that when examining the goodness-of-fit of the current model, one needs to perform model evaluation by using an enlarged candidate model. Although the procedure does not require estimation under the enlarged model of dimension k + 1, the decision as to how well the ...