2023

最新文章

• Xiaoyue Niu's contribution to the Discussion of 'Vintage Factor Analysis with Varimax Performs Statistical Inference' by Rohe & Zeng AI翻译
  作者：Niu, Xiaoyue
  作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
• Priyantha Wijayatunga's contribution to the Discussion of 'Martingale Posterior Distributions' by Fong, Holmes, and Walker AI翻译
  作者：Wijayatunga, Priyantha
  作者单位：Umea University
• Quasi-Newton updating for large-scale distributed learning AI翻译
  作者：Wu, Shuyuan; Huang, Danyang; Wang, Hansheng
  作者单位：Shanghai University of Finance & Economics; Renmin University of China; Peking University
  摘要：Distributed computing is critically important for modern statistical analysis. Herein, we develop a distributed quasi-Newton (DQN) framework with excellent statistical, computation, and communication efficiency. In the DQN method, no Hessian matrix inversion or communication is needed. This considerably reduces the computation and communication complexity of the proposed method. Notably, related existing methods only analyse numerical convergence and require a diverging number of iterations to...
• Bayesian likelihood-based regression for estimation of optimal dynamic treatment regimes AI翻译
  作者：Yu, Weichang; Bondell, Howard D.
  作者单位：University of Melbourne
  摘要：Clinicians often make sequences of treatment decisions that can be framed as dynamic treatment regimes. In this paper, we propose a Bayesian likelihood-based dynamic treatment regime model that incorporates regression specifications to yield interpretable relationships between covariates and stage-wise outcomes. We define a set of probabilistically-coherent properties for dynamic treatment regime processes and present the theoretical advantages that are consequential to these properties. We ju...
• Simultaneous directional inference AI翻译
  作者：Heller, Ruth; Solari, Aldo
  作者单位：Tel Aviv University; University of Milano-Bicocca; Universita Ca Foscari Venezia; Tel Aviv University
  摘要：We consider the problem of inference on the signs of n > 1 parameters. We aim to provide 1 - alpha post hoc confidence bounds on the number of positive and negative (or non-positive) parameters, with a simultaneous guarantee, for all subsets of parameters. We suggest to start by using the data to select the direction of the hypothesis test for each parameter; then, adjust the p-values of the one-sided hypotheses for the selection, and use the adjusted p-values for simultaneous inference on the...
• Parameterizing and simulating from causal models AI翻译
  作者：Evans, Robin J.; Didelez, Vanessa
  作者单位：University of Oxford; Leibniz Association; Leibniz Institute for Prevention Research & Epidemiology (BIPS); University of Bremen; University of Bremen
  摘要：Many statistical problems in causal inference involve a probability distribution other than the one from which data are actually observed; as an additional complication, the object of interest is often a marginal quantity of this other probability distribution. This creates many practical complications for statistical inference, even where the problem is non-parametrically identified. In particular, it is difficult to perform likelihood-based inference, or even to simulate from the model in a ...
• Testing homogeneity: the trouble with sparse functional data AI翻译
  作者：Zhu, Changbo; Wang, Jane-Ling
  作者单位：University of Notre Dame; University of California System; University of California Davis
  摘要：Testing the homogeneity between two samples of functional data is an important task. While this is feasible for intensely measured functional data, we explain why it is challenging for sparsely measured functional data and show what can be done for such data. In particular, we show that testing the marginal homogeneity based on point-wise distributions is feasible under some mild constraints and propose a new two-sample statistic that works well with both intensively and sparsely measured func...
• Cluster extent inference revisited: quantification and localisation of brain activity AI翻译
  作者：Goeman, Jelle J.; Gorecki, Pawel; Monajemi, Ramin; Chen, Xu; Nichols, Thomas E.; Weeda, Wouter
  作者单位：Leiden University; Leiden University Medical Center (LUMC); Leiden University - Excl LUMC; University of Warsaw; University of Oxford; University of Oxford; Leiden University - Excl LUMC; Leiden University
  摘要：Cluster inference based on spatial extent thresholding is a popular analysis method multiple testing in spatial data, and is frequently used for finding activated brain areas in neuroimaging. However, the method has several well-known issues. While powerful for finding regions with some activation, the method as currently defined does not allow any further quantification or localisation of signal. In this paper, we repair this gap. We show that cluster-extent inference can be used (1) to infer...
• On the causal interpretation of randomised interventional indirect effects AI翻译
  作者：Miles, Caleb H.
  摘要：Identification of standard mediated effects such as the natural indirect effect relies on heavy causal assumptions. By circumventing such assumptions, so-called randomised interventional indirect effects have gained popularity in the mediation literature. Here, I introduce properties one might demand of an indirect effect measure in order for it to have a true mediational interpretation. For instance, the sharp null criterion requires an indirect effect measure to be null whenever no individua...
• A model where the least trimmed squares estimator is maximum likelihood AI翻译
  作者：Berenguer-Rico, Vanessa; Johansen, Soren; Nielsen, Bent
  作者单位：University of Oxford; University of Copenhagen; University of Oxford
  摘要：The least trimmed squares (LTS) estimator is a popular robust regression estimator. It finds a subsample of h 'good' observations among n observations and applies least squares on that subsample. We formulate a model in which this estimator is maximum likelihood. The model has 'outliers' of a new type, where the outlying observations are drawn from a distribution with values outside the realized range of h 'good', normal observations. The LTS estimator is found to be h(1/2) consistent and asym...