2010

最新文章

• On the use of non-local prior densities in Bayesian hypothesis tests AI翻译
  作者：Johnson, Valen E.; Rossell, David
  作者单位：University of Texas System; UTMD Anderson Cancer Center; Barcelona Institute of Science & Technology; Institute for Research in Biomedicine - IRB Barcelona
  摘要：We examine philosophical problems and sampling deficiencies that are associated with current Bayesian hypothesis testing methodology, paying particular attention to objective Bayes methodology. Because the prior densities that are used to define alternative hypotheses in many Bayesian tests assign non-negligible probability to regions of the parameter space that are consistent with null hypotheses, resulting tests provide exponential accumulation of evidence in favour of true alternative hypot...
• Signed directed acyclic graphs for causal inference AI翻译
  作者：VanderWeele, Tyler J.; Robins, James M.
  作者单位：Harvard University; Harvard T.H. Chan School of Public Health
  摘要：Formal rules governing signed edges on causal directed acyclic graphs are described and it is shown how these rules can be useful in reasoning about causality. Specifically, the notions of a monotonic effect, a weak monotonic effect and a signed edge are introduced. Results are developed relating these monotonic effects and signed edges to the sign of the causal effect of an intervention in the presence of intermediate variables. The incorporation of signed edges in the directed acyclic graph ...
• Maximum likelihood estimation of a multi-dimensional log-concave density AI翻译
  作者：Cule, Madeleine; Samworth, Richard; Stewart, Michael
  作者单位：University of Cambridge; University of Sydney
  摘要：Let X-1,...,X-n be independent and identically distributed random vectors with a (Lebesgue) density f. We first prove that, with probability 1, there is a unique log-concave maximum likelihood estimator f(n) of f. The use of this estimator is attractive because, unlike kernel density estimation, the method is fully automatic, with no smoothing parameters to choose. Although the existence proof is non-constructive, we can reformulate the issue of computing f(n) in terms of a non-differentiable ...
• Default priors for Bayesian and frequentist inference AI翻译
  作者：Fraser, D. A. S.; Reid, N.; Marras, E.; Yi, G. Y.
  作者单位：University of Toronto; University of Waterloo
  摘要：We investigate the choice of default priors for use with likelihood for Bayesian and frequentist inference. Such a prior is a density or relative density that weights an observed likelihood function, leading to the elimination of parameters that are not of interest and then a density-type assessment for a parameter of interest. For independent responses from a continuous model, we develop a prior for the full parameter that is closely linked to the original Bayes approach and provides an exten...
• Particle Markov chain Monte Carlo methods AI翻译
  作者：Andrieu, Christophe; Doucet, Arnaud; Holenstein, Roman
  作者单位：University of British Columbia; University of Bristol; Research Organization of Information & Systems (ROIS); Institute of Statistical Mathematics (ISM) - Japan
  摘要：Markov chain Monte Carlo and sequential Monte Carlo methods have emerged as the two main tools to sample from high dimensional probability distributions. Although asymptotic convergence of Markov chain Monte Carlo algorithms is ensured under weak assumptions, the performance of these algorithms is unreliable when the proposal distributions that are used to explore the space are poorly chosen and/or if highly correlated variables are updated independently. We show here how it is possible to bui...
• Random-weight particle filtering of continuous time processes AI翻译
  作者：Fearnhead, Paul; Papaspiliopoulos, Omiros; Roberts, Gareth O.; Stuart, Andrew
  作者单位：Pompeu Fabra University; Lancaster University; University of Warwick
  摘要：It is possible to implement importance sampling, and particle filter algorithms, where the importance sampling weight is random. Such random-weight algorithms have been shown to be efficient for inference for a class of diffusion models, as they enable inference without any (time discretization) approximation of the underlying diffusion model. One difficulty of implementing such random-weight algorithms is the requirement to have weights that are positive with probability 1. We show how Wald's...
• Explicit estimating equations for semiparametric generalized linear latent variable models AI翻译
  作者：Ma, Yanyuan; Genton, Marc G.
  作者单位：Texas A&M University System; Texas A&M University College Station
  摘要：We study generalized linear latent variable models without requiring a distributional assumption of the latent variables. Using a geometric approach, we derive consistent semiparametric estimators. We demonstrate that these models have a property which is similar to that of a sufficient complete statistic, which enables us to simplify the estimating procedure and explicitly to formulate the semiparametric estimating equations. We further show that the explicit estimators have the usual root n ...
• On selection of spatial linear models for lattice data AI翻译
  作者：Zhu, Jun; Huang, Hsin-Cheng; Reyes, Perla E.
  作者单位：Colorado State University System; Colorado State University Fort Collins; University of Wisconsin System; University of Wisconsin Madison; Academia Sinica - Taiwan; National Yang Ming Chiao Tung University
  摘要：Spatial linear models are popular for the analysis of data on a spatial lattice, but statistical techniques for selection of covariates and a neighbourhood structure are limited. Here we develop new methodology for simultaneous model selection and parameter estimation via penalized maximum likelihood under a spatial adaptive lasso. A computationally efficient algorithm is devised for obtaining approximate penalized maximum likelihood estimates. Asymptotic properties of penalized maximum likeli...
• Ordering and selecting components in multivariate or functional data linear prediction AI翻译
  作者：Hall, Peter; Yang, You-Jun
  作者单位：University of Melbourne; National Taiwan University
  摘要：The problem of component choice in regression-based prediction has a long history. The main cases where important choices must be made are functional data analysis, and problems in which the explanatory variables are relatively high dimensional vectors. Indeed, principal component analysis has become the basis for methods for functional linear regression. In this context the number of components can also be interpreted as a smoothing parameter, and so the viewpoint is a little different from t...
• Stability selection AI翻译
  作者：Meinshausen, Nicolai; Buehlmann, Peter
  作者单位：University of Oxford; Swiss Federal Institutes of Technology Domain; ETH Zurich
  摘要：Estimation of structure, such as in variable selection, graphical modelling or cluster analysis, is notoriously difficult, especially for high dimensional data. We introduce stability selection. It is based on subsampling in combination with (high dimensional) selection algorithms. As such, the method is extremely general and has a very wide range of applicability. Stability selection provides finite sample control for some error rates of false discoveries and hence a transparent principle to ...