Journal of the Royal Statistical Society: Series B

最新文章

• Lack-of-fit tests for quantile regression models AI翻译
  作者：Dong, Chen; Li, Guodong; Feng, Xingdong
  作者单位：Shanghai University of Finance & Economics; University of Hong Kong
  摘要：The paper novelly transforms lack-of-fit tests for parametric quantile regression models into checking the equality of two conditional distributions of covariates. Accordingly, by applying some successful two-sample test statistics in the literature, two tests are constructed to check the lack of fit for low and high dimensional quantile regression models. The low dimensional test works well when the number of covariates is moderate, whereas the high dimensional test can maintain the power whe...
• Characterization of c-, L- and φk-optimal designs for a class of non-linear multiple-regression models AI翻译
  作者：Schmidt, Dennis
  作者单位：Otto von Guericke University
  摘要：Optimal designs for multiple-regression models are determined. We consider a general class of non-linear models including proportional hazards models with different censoring schemes, the Poisson and the negative binomial model. For these models we provide a complete characterization of c-optimal designs for all vectors c in the case of a single covariate. For multiple regression with an arbitrary number of covariates, c-optimal designs for certain vectors c are derived analytically. Using som...
• Hypoelliptic diffusions: filtering and inference from complete and partial observations AI翻译
  作者：Ditlevsen, Susanne; Samson, Adeline
  作者单位：University of Copenhagen; Communaute Universite Grenoble Alpes; Universite Grenoble Alpes (UGA); Centre National de la Recherche Scientifique (CNRS)
  摘要：The statistical problem of parameter estimation in partially observed hypoelliptic diffusion processes is naturally occurring in many applications. However, because of the noise structure, where the noise components of the different co-ordinates of the multi-dimensional process operate on different timescales, standard inference tools are ill conditioned. We propose to use a higher order scheme to approximate the likelihood, such that the different timescales are appropriately accounted for. W...
• On cross-validation for sparse reduced rank regression AI翻译
  作者：She, Yiyuan; Hoang Tran
  作者单位：State University System of Florida; Florida State University
  摘要：In high dimensional data analysis, regularization methods pursuing sparsity and/or low rank have received much attention recently. To provide a proper amount of shrinkage, it is typical to use a grid search and a model comparison criterion to find the optimal regularization parameters. However, we show that fixing the parameters across all folds may result in an inconsistency issue, and it is more appropriate to cross-validate projection-selection patterns to obtain the best coefficient estima...
• Dynamic shrinkage processes AI翻译
  作者：Kowal, Daniel R.; Matteson, David S.; Ruppert, David
  作者单位：Rice University; Cornell University
  摘要：We propose a novel class of dynamic shrinkage processes for Bayesian time series and regression analysis. Building on a global-local framework of prior construction, in which continuous scale mixtures of Gaussian distributions are employed for both desirable shrinkage properties and computational tractability, we model dependence between the local scale parameters. The resulting processes inherit the desirable shrinkage behaviour of popular global-local priors, such as the horseshoe prior, but...
• MALMEM: model averaging in linear measurement error models AI翻译
  作者：Zhang, Xinyu; Ma, Yanyuan; Carroll, Raymond J.
  作者单位：Chinese Academy of Sciences; University of Science & Technology of China, CAS; Chinese Academy of Sciences; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; Texas A&M University System; Texas A&M University College Station; University of Technology Sydney
  摘要：We develop model averaging estimation in the linear regression model where some covariates are subject to measurement error. The absence of the true covariates in this framework makes the calculation of the standard residual-based loss function impossible. We take advantage of the explicit form of the parameter estimators and construct a weight choice criterion. It is asymptotically equivalent to the unknown model average estimator minimizing the loss function. When the true model is not inclu...
• On choosing mixture components via non-local priors AI翻译
  作者：Fuquene, Jairo; Steel, Mark; Rossell, David
  作者单位：University of Warwick; Pompeu Fabra University
  摘要：Choosing the number of mixture components remains an elusive challenge. Model selection criteria can be either overly liberal or conservative and return poorly separated components of limited practical use. We formalize non-local priors (NLPs) for mixtures and show how they lead to well-separated components with non-negligible weight, interpretable as distinct subpopulations. We also propose an estimator for posterior model probabilities under local priors and NLPs, showing that Bayes factors ...
• Approximate Bayesian computation with the Wasserstein distance AI翻译
  作者：Bernton, Espen; Jacob, Pierre E.; Gerber, Mathieu; Robert, Christian P.
  作者单位：Harvard University; University of Bristol; Universite PSL; Universite Paris-Dauphine; University of Warwick
  摘要：A growing number of generative statistical models do not permit the numerical evaluation of their likelihood functions. Approximate Bayesian computation has become a popular approach to overcome this issue, in which one simulates synthetic data sets given parameters and compares summaries of these data sets with the corresponding observed values. We propose to avoid the use of summaries and the ensuing loss of information by instead using the Wasserstein distance between the empirical distribu...
• Moment conditions and Bayesian non-parametrics AI翻译
  作者：Bornn, Luke; Shephard, Neil; Solgi, Reza
  作者单位：Simon Fraser University; Harvard University
  摘要：Models phrased through moment conditions are central to much of modern inference. Here these moment conditions are embedded within a non-parametric Bayesian set-up. Handling such a model is not probabilistically straightforward as the posterior has support on a manifold. We solve the relevant issues, building new probability and computational tools by using Hausdorff measures to analyse them on real and simulated data. These new methods, which involve simulating on a manifold, can be applied w...
• Deterministic parallel analysis: an improved method for selecting factors and principal components AI翻译
  作者：Dobriban, Edgar; Owen, Art B.
  作者单位：University of Pennsylvania
  摘要：Factor analysis and principal component analysis are used in many application areas. The first step, choosing the number of components, remains a serious challenge. Our work proposes improved methods for this important problem. One of the most popular state of the art methods is parallel analysis (PA), which compares the observed factor strengths with simulated strengths under a noise-only model. The paper proposes improvements to PA. We first derandomize it, proposing deterministic PA, which ...