Biometrika

最新文章

• Structural restrictions in local causal discovery: identifying direct causes of a target variable AI翻译
  作者：Bodik, Juraj; Chavez-Demoulin, Valerie
  作者单位：University of Lausanne
  摘要：We consider the problem of learning a set of direct causes of a target variable from an observational joint distribution. Learning directed acyclic graphs that represent the causal structure is a fundamental problem in science. Several results are known when the full directed acyclic graph is identifiable from the distribution, such as when a nonlinear Gaussian data-generating process is assumed. Here, we are interested only in identifying the direct causes of one target variable (local causal...
• Functional principal component analysis forsparse censored data AI翻译
  作者：Murphy, Caitrin; Laber, Eric; Merwin, Rhonda; Reich, Brian; Koerner, Jake
  作者单位：Duke University; Duke University; North Carolina State University
  摘要：Functional principal component analysis is a key tool in the study of functional data, driving both exploratory analyses and feature construction for use in formal modelling and testing procedures. However, existing methods do not apply when functional observations are censored; for example, when the measurement instrument only supports recordings within a prespecified interval, thereby truncating values outside this range to the nearest boundary. A na & iuml;ve application of existing methods...
• Nonsense associations in Markov random fields with pairwise dependence AI翻译
  作者：Bhattacharya, Sohom; Mukherjee, Rajarshi; Ogburn, Elizabeth L.
  作者单位：State University System of Florida; University of Florida; Harvard University; Harvard T.H. Chan School of Public Health; Johns Hopkins University; Johns Hopkins Bloomberg School of Public Health
  摘要：identified the issue of ' nonsense correlations' in time series data, where dependence within each of two random vectors causes overdispersion, i.e., variance inflation, for measures of dependence between the two. Since then much has been written about nonsense correlations, but nearly all of it confined to the time series literature. In this paper we provide the first, to our knowledge, rigorous study of this phenomenon for other forms of (positive) dependence, specifically for Markov random ...
• An omitted variable bias framework for sensitivity analysis of instrumental variables AI翻译
  作者：Cinelli, Carlos; Hazlett, Chad
  作者单位：University of Washington; University of Washington Seattle; University of California System; University of California Los Angeles
  摘要：We develop an omitted variable bias framework for sensitivity analysis of instrumental variable estimates that naturally handles multiple side effects (violations of the exclusion restriction assumption) and confounders (violations of the ignorability of the instrument assumption) of the instrument, exploits expert knowledge to bound sensitivity parameters and can be easily implemented with standard software. Specifically, we introduce sensitivity statistics for routine reporting, such as (ext...
• Transfer learning for piecewise-constant mean estimation: optimality, l1 and l0 penalization AI翻译
  作者：Wang, F.; Yu, Y.
  作者单位：University of Warwick
  摘要：We study transfer learning for estimating piecewise-constant signals when source data, which may be relevant but disparate, are available in addition to target data. We first investigate transfer learning estimators that respectively employ l(0) and l(1) penalties for unisource data scenarios and then generalize these estimators to accommodate multisources. To further reduce estimation errors, especially when some sources significantly differ from the target, we introduce an informative source...
• Comparing causal parameters with many treatments and positivity violations AI翻译
  作者：McClean, A.; Li, Y.; Bae, S.; McAdams DeMarco, M.; Diaz, I; Wu, W.
  作者单位：New York University; New York University; Johns Hopkins University; Johns Hopkins Bloomberg School of Public Health
  摘要：Comparing outcomes across treatments is essential in medicine and public policy. To do so, researchers typically estimate a set of parameters, possibly counterfactual, each targeting adifferent treatment. Treatment-specific means are commonly used, but their identification requires a positivity assumption: every subject has a nonzero probability of receiving each treatment. This assumption is often implausible, especially when treatment can take many values. Causal parameters based on dynamic ...
• Bias correction of quadratic spectral estimators AI翻译
  作者：Astfalck, Lachlan C.; Sykulski, Adam M.; Cripps, Edward J.
  作者单位：University of Western Australia; Imperial College London
  摘要：The three cardinal, statistically consistent families of nonparametric estimators for the power spectral density of a time series are the lag-window, multitaper and Welch estimators. However, when estimating power spectral densities from a finite sample, each can be subject to nonignorable bias. Astfalck et al. (2024) developed a method that offers significant bias reduction for finite samples for Welch's estimator, which this article extends to the larger family of quadratic estimators, thus ...
• Multivariate Gaussian cumulative distribution functions as the marginal likelihood of their dual Bayesian probit models AI翻译
  作者：Fasano, Augusto; Denti, Francesco
  作者单位：University of Turin; University of Padua
  摘要：The computation of multivariate Gaussian cumulative distribution functions is a key step in many statistical procedures, often representing a crucial computational bottleneck. Over the past few decades, efficient algorithms have been proposed to address this problem, mainly using Monte Carlo solutions. This work highlights a connection between the multivariate Gaussian cumulative distribution function and the marginal likelihood of a tailored dual Bayesian probit model. Consequently, any metho...
• Convergence and optimality of the EM algorithm under multi-component Gaussian mixture models AI翻译
  作者：Bing, Xin; Kong, Dehan; Li, Bingqing
  作者单位：University of Toronto; University of Toronto
  摘要：Gaussian mixture models are fundamental statistical tools for modelling heterogeneous data. Due to the nonconcavity of the likelihood function, the expectation-maximization (EM) algorithm is widely used for parameter estimation of each Gaussian component. Existing analyses of the EM algorithm's convergence to the true parameter focus on either the two-component case or multi-component settings with known mixing probabilities and isotropic covariance matrices. In this work, we study the converg...
• Doubly robust and efficient calibration of prediction sets for right-censored time-to-event outcomes AI翻译
  作者：Farina, Rebecca; Tchetgen Tchetgen, Eric; Kuchibhotla, Arun Kumar
  作者单位：Carnegie Mellon University; University of Pennsylvania; Carnegie Mellon University
  摘要：Our objective is to construct well-calibrated prediction sets for a time-to-event outcome subject to right censoring with guaranteed coverage. Inspired by modern conformal inference, our approach avoids the need for a well-specified parametric or semiparametric survival model. Unlike existing conformal methods for survival data, which assume Type-I censoring with fully observed censoring times, we consider the more common right-censoring setting in which only the censoring time or the event ti...