Biometrika

最新文章

• A linear adjustment-based approach to posterior drift in transfer learning AI翻译
  作者：Maity, Subha; Dutta, Diptavo; Terhorst, Jonathan; Sun, Yuekai; Banerjee, Moulinath
  作者单位：University of Michigan System; University of Michigan; National Institutes of Health (NIH) - USA; NIH National Cancer Institute (NCI); NIH National Cancer Institute- Division of Cancer Epidemiology & Genetics
  摘要：We present new models and methods for the posterior drift problem where the regression function in the target domain is modelled as a linear adjustment, on an appropriate scale, of that in the source domain, and study the theoretical properties of our proposed estimators in the binary classification problem. The core idea of our model inherits the simplicity and the usefulness of generalized linear models and accelerated failure time models from the classical statistics literature. Our approac...
• Treatment effect quantiles in stratified randomized experiments and matched observational studies AI翻译
  作者：Su, Yongchang; Li, Xinran
  作者单位：University of Illinois System; University of Illinois Urbana-Champaign
  摘要：Evaluating the treatment effect has become an important topic for many applications. However, most existing literature focuses mainly on average treatment effects. When the individual effects are heavy tailed or have outlier values, not only may the average effect not be appropriate for summarizing treatment effects, but also the conventional inference for it can be sensitive and possibly invalid due to poor large-sample approximations. In this paper we focus on quantiles of individual treatme...
• Network community detection using higher-order structures AI翻译
  作者：Yu, X.; Zhu, J.
  作者单位：University of Michigan System; University of Michigan
  摘要：In many real-world networks, it is often observed that subgraphs or higher-order structures of certain configurations, e.g., triangles and by-fans, are overly abundant compared to standard randomly generated networks (). However, statistical models accounting for this phenomenon are limited, especially when community structure is of interest. This limitation is coupled with a lack of community detection methods that leverage subgraphs or higher-order structures. In this paper, we propose a new...
• Generalized kernel two-sample tests AI翻译
  作者：Song, Hoseung; Chen, Hao
  作者单位：University of California System; University of California Davis
  摘要：Kernel two-sample tests have been widely used for multivariate data to test equality of distributions. However, existing tests based on mapping distributions into a reproducing kernel Hilbert space mainly target specific alternatives and do not work well for some scenarios when the dimension of the data is moderate to high due to the curse of dimensionality. We propose a new test statistic that makes use of a common pattern under moderate and high dimensions and achieves substantial power impr...
• Retrospective causal inference with multiple effect variables AI翻译
  作者：Li, Wei; Lu, Zitong; Jia, Jinzhu; Xie, Min; Geng, Zhi
  作者单位：Renmin University of China; Renmin University of China; City University of Hong Kong; Peking University; Peking University; Beijing Technology & Business University
  摘要：As highlighted in and , deducing the causes of given effects is a more challenging problem than evaluating the effects of causes in causal inference. proposed an approach for deducing causes of a single effect variable based on posterior causal effects. In many applications, there are multiple effect variables, and they can be used simultaneously to more accurately deduce the causes. To retrospectively deduce causes from multiple effects, we propose multivariate posterior total, intervention a...
• Power and sample size calculations for rerandomization AI翻译
  作者：Branson, Zach; Li, Xinran; Ding, Peng
  作者单位：Carnegie Mellon University; University of Illinois System; University of Illinois Urbana-Champaign; University of California System; University of California Berkeley
  摘要：Power analyses are an important aspect of experimental design, because they help determine how experiments are implemented in practice. It is common to specify a desired level of power and compute the sample size necessary to obtain that power. Such calculations are well known for completely randomized experiments, but there can be many benefits to using other experimental designs. For example, it has recently been established that rerandomization, where subjects are randomized until covariate...
• Causal inference with misspecified exposure mappings: separating definitions and assumptions AI翻译
  作者：Savje, F.
  作者单位：Yale University
  摘要：Exposure mappings facilitate investigations of complex causal effects when units interact in experiments. Current methods require experimenters to use the same exposure mappings to define the effect of interest and to impose assumptions on the interference structure. However, the two roles rarely coincide in practice, and experimenters are forced to make the often questionable assumption that their exposures are correctly specified. This paper argues that the two roles exposure mappings curren...
• Radial neighbours for provably accurate scalable approximations of Gaussian processes AI翻译
  作者：Zhu, Yichen; Peruzzi, Michele; Li, Cheng; Dunson, David B.
  作者单位：Bocconi University; University of Michigan System; University of Michigan; National University of Singapore; Duke University
  摘要：In geostatistical problems with massive sample size, Gaussian processes can be approximated using sparse directed acyclic graphs to achieve scalable O(n) computational complexity. In these models, data at each location are typically assumed conditionally dependent on a small set of parents that usually include a subset of the nearest neighbours. These methodologies often exhibit excellent empirical performance, but the lack of theoretical validation leads to unclear guidance in specifying the ...
• Flexible control of the median of the false discovery proportion AI翻译
  作者：Hemerik, Jesse; Solari, Aldo; Goeman, Jelle J.
  作者单位：Erasmus University Rotterdam; Erasmus University Rotterdam - Excl Erasmus MC; Universita Ca Foscari Venezia; Leiden University - Excl LUMC; Leiden University; Leiden University Medical Center (LUMC)
  摘要：We introduce a multiple testing procedure that controls the median of the proportion of false discoveries in a flexible way. The procedure requires only a vector of p-values as input and is comparable to the Benjamini-Hochberg method, which controls the mean of the proportion of false discoveries. Our method allows free choice of one or several values of $ \alpha $ after seeing the data, unlike the Benjamini-Hochberg procedure, which can be very anti-conservative when $ \alpha $ is chosen post...
• Efficient nonparametric estimation of Toeplitz covariance matrices AI翻译
  作者：Klockmann, K.; Krivobokova, T.
  作者单位：University of Vienna
  摘要：A new efficient nonparametric estimator for Toeplitz covariance matrices is proposed. This estimator is based on a data transformation that translates the problem of Toeplitz covariance matrix estimation to the problem of mean estimation in an approximate Gaussian regression. The resulting Toeplitz covariance matrix estimator is positive definite by construction, fully data driven and computationally very fast. Moreover, this estimator is shown to be minimax optimal under the spectral norm for...