Journal of the Royal Statistical Society: Series B

最新文章

• Causal analysis of ordinal treatments and binary outcomes under truncation by death AI翻译
  作者：Wang, Linbo; Richardson, Thomas S.; Zhou, Xiao-Hua
  作者单位：University of Washington; University of Washington Seattle; US Department of Veterans Affairs; Veterans Health Administration (VHA); Vet Affairs Puget Sound Health Care System
  摘要：It is common that, in multiarm randomized trials, the outcome of interest is truncated by death', meaning that it is only observed or well-defined conditioning on an intermediate outcome. In this case, in addition to pairwise contrasts, the joint inference for all treatment arms is also of interest. Under a monotonicity assumption we present methods for both pairwise and joint causal analyses of ordinal treatments and binary outcomes in the presence of truncation by death. We illustrate via ex...
• Regionalization of multiscale spatial processes by using a criterion for spatial aggregation error AI翻译
  作者：Bradley, Jonathan R.; Wikle, Christopher K.; Holan, Scott H.
  作者单位：University of Missouri System; University of Missouri Columbia
  摘要：The modifiable areal unit problem and the ecological fallacy are known problems that occur when modelling multiscale spatial processes. We investigate how these forms of spatial aggregation error can guide a regionalization over a spatial domain of interest. By regionalization' we mean a specification of geographies that define the spatial support for areal data. This topic has been studied vigorously by geographers but has been given less attention by spatial statisticians. Thus, we propose a...
• On estimation of the noise variance in high dimensional probabilistic principal component analysis AI翻译
  作者：Passemier, Damien; Li, Zhaoyuan; Yao, Jianfeng
  作者单位：University of Hong Kong
  摘要：We develop new statistical theory for probabilistic principal component analysis models in high dimensions. The focus is the estimation of the noise variance, which is an important and unresolved issue when the number of variables is large in comparison with the sample size. We first unveil the reasons for an observed downward bias of the maximum likelihood estimator of the noise variance when the data dimension is high. We then propose a bias-corrected estimator by using random-matrix theory ...
• Optimal group testing designs for estimating prevalence with uncertain testing errors AI翻译
  作者：Huang, Shih-Hao; Huang, Mong-Na Lo; Shedden, Kerby; Wong, Weng Kee
  作者单位：National Sun Yat Sen University; Academia Sinica - Taiwan; University of Michigan System; University of Michigan; University of California System; University of California Los Angeles
  摘要：We construct optimal designs for group testing experiments where the goal is to estimate the prevalence of a trait by using a test with uncertain sensitivity and specificity. Using optimal design theory for approximate designs, we show that the most efficient design for simultaneously estimating the prevalence, sensitivity and specificity requires three different group sizes with equal frequencies. However, if estimating prevalence as accurately as possible is the only focus, the optimal strat...
• Estimation of the false discovery proportion with unknown dependence AI翻译
  作者：Fan, Jianqing; Han, Xu
  作者单位：Princeton University; Fudan University; Pennsylvania Commonwealth System of Higher Education (PCSHE); Temple University
  摘要：Large-scale multiple testing with correlated test statistics arises frequently in much scientific research. Incorporating correlation information in approximating the false discovery proportion (FDP) has attracted increasing attention in recent years. When the covariance matrix of test statistics is known, Fan and his colleagues provided an accurate approximation of the FDP under arbitrary dependence structure and some sparsity assumption. However, the covariance matrix is often unknown in man...
• Statistical clustering of temporal networks through a dynamic stochastic block model AI翻译
  作者：Matias, Catherine; Miele, Vincent
  作者单位：Centre National de la Recherche Scientifique (CNRS); Sorbonne Universite; Sorbonne Universite; Universite Paris Cite; Centre National de la Recherche Scientifique (CNRS); Universite Claude Bernard Lyon 1; VetAgro Sup
  摘要：Statistical node clustering in discrete time dynamic networks is an emerging field that raises many challenges. Here, we explore statistical properties and frequentist inference in a model that combines a stochastic block model for its static part with independent Markov chains for the evolution of the nodes groups through time. We model binary data as well as weighted dynamic random graphs ( with discrete or continuous edges values). Our approach, motivated by the importance of controlling fo...
• Linear and conic programming estimators in high dimensional errors-in-variables models AI翻译
  作者：Belloni, Alexandre; Rosenbaum, Mathieu; Tsybakov, Alexandre B.
  作者单位：Duke University; Sorbonne Universite; Institut Polytechnique de Paris; ENSAE Paris
  摘要：We consider the linear regression model with observation error in the design. In this setting, we allow the number of covariates to be much larger than the sample size. Several new estimation methods have been recently introduced for this model. Indeed, the standard lasso estimator or Dantzig selector turns out to become unreliable when only noisy regressors are available, which is quite common in practice. In this work, we propose and analyse a new estimator for the errors-in-variables model....
• Theoretical guarantees for approximate sampling from smooth and log-concave densities AI翻译
  作者：Dalalyan, Arnak S.
  摘要：Sampling from various kinds of distribution is an issue of paramount importance in statistics since it is often the key ingredient for constructing estimators, test procedures or confidence intervals. In many situations, exact sampling from a given distribution is impossible or computationally expensive and, therefore, one needs to resort to approximate sampling strategies. However, there is no well-developed theory providing meaningful non-asymptotic guarantees for the approximate sampling pr...
• Mediation analysis with time varying exposures and mediators AI翻译
  作者：VanderWeele, Tyler J.; Tchetgen, Eric J. Tchetgen
  作者单位：Harvard University; Harvard T.H. Chan School of Public Health
  摘要：We consider causal mediation analysis when exposures and mediators vary over time. We give non-parametric identification results, discuss parametric implementation and also provide a weighting approach to direct and indirect effects based on combining the results of two marginal structural models. We also discuss how our results give rise to a causal interpretation of the effect estimates produced from longitudinal structural equation models. When there are time varying confounders affected by...
• A minimum relative entropy based correlation model between the response and covariates AI翻译
  作者：Bhattacharya, Bhaskar; Al-Talib, Mohammad
  作者单位：Southern Illinois University System; Southern Illinois University; Yarmouk University
  摘要：A semiparametric model is presented utilizing dependence between a response and several covariates. We show that this model is optimum when the marginal distributions of the response and the covariates are known. This model extends the generalized linear model and the proportional likelihood ratio model when the marginal distributions are unknown. New interpretations of known models such as the logistic regression model, density ratio model and selection bias model are obtained in terms of dep...