Biometrika

最新文章

• Discussion of 'Statistical inference for streamed longitudinal data' AI翻译
  作者：Song, Peter X-K; Zhou, Ling
  作者单位：University of Michigan System; University of Michigan; Southwestern University of Finance & Economics - China; Southwestern University of Finance & Economics - China
• Honest calibration assessment for binary outcome predictions AI翻译
  作者：Dimitriadis, Timo; Dumbgen, Lutz; Henzi, Alexander; Puke, Marius; Ziegel, Johanna
  作者单位：Ruprecht Karls University Heidelberg; University of Bern; Swiss Federal Institutes of Technology Domain; ETH Zurich; University Hohenheim
  摘要：Probability predictions from binary regressions or machine learning methods ought to be calibrated: if an event is predicted to occur with probability x, it should materialize with approximately that frequency, which means that the so-called calibration curvep(middot) should equal the identity, i.e., p(x) = x for all x in the unit interval. We propose honest calibration assessment based on novel confidence bands for the calibration curve, which are valid subject to only the natural assumption ...
• Marginal proportional hazards models for multivariate interval-censored data AI翻译
  作者：Xu, Yangjianchen; Zeng, Donglin; Lin, D. Y.
  作者单位：University of North Carolina; University of North Carolina Chapel Hill
  摘要：Multivariate interval-censored data arise when there are multiple types of events or clusters of study subjects, such that the event times are potentially correlated and when each event is only known to occur over a particular time interval. We formulate the effects of potentially time-varying covariates on the multivariate event times through marginal proportional hazards models while leaving the dependence structures of the related event times unspecified. We construct the nonparametric pseu...
• Localized conformal prediction: a generalized inference framework for conformal prediction AI翻译
  作者：Guan, Leying
  作者单位：Yale University
  摘要：We propose a new inference framework called localized conformal prediction. It generalizes the framework of conformal prediction by offering a single-test-sample adaptive construction that emphasizes a local region around this test sample, and can be combined with different conformal scores. The proposed framework enjoys an assumption-free finite sample marginal coverage guarantee, and it also offers additional local coverage guarantees under suitable assumptions. We demonstrate how to change ...
• Median regularity and honest inference AI翻译
  作者：Kuchibhotla, Arun Kumar; Balakrishnan, Sivaraman; Wasserman, Larry
  作者单位：Carnegie Mellon University
  摘要：We introduce a new notion of regularity of an estimator called median regularity. We prove that uniformly valid honest inference for a functional is possible if and only if there exists a median regular estimator of that functional. To the best of our knowledge, such a notion of regularity that is necessary for uniformly valid inference is unavailable in the literature.
• Dependent censoring based on parametric copulas AI翻译
  作者：Czado, C.; Van Keilegom, I
  作者单位：Technical University of Munich; KU Leuven
  摘要：Consider a survival time T that is subject to random right censoring, and suppose that T is stochastically dependent on the censoring time C. We are interested in the marginal distribution of T. This situation is often encountered in practice. Consider, for example, the case where T is a patient's time to death from a certain disease. Then the censoring time C could be the time until the patient leaves the study or the time until death from another cause. If the reason for leaving the study is...
• Splitting strategies for post-selection inference AI翻译
  作者：Rasines, D. Garcia; Young, G. A.
  作者单位：Imperial College London
  摘要：We consider the problem of providing valid inference for a selected parameter in a sparse regression setting. It is well known that classical regression tools can be unreliable in this context because of the bias generated in the selection step. Many approaches have been proposed in recent years to ensure inferential validity. In this article we consider a simple alternative to data splitting based on randomizing the response vector, which allows for higher selection and inferential power than...
• Thresholded graphical lasso adjusts for latent variables AI翻译
  作者：Wang, Minjie; Allen, Genevera, I
  作者单位：University of Minnesota System; University of Minnesota Twin Cities; Rice University
  摘要：Structural learning of Gaussian graphical models in the presence of latent variables has long been a challenging problem. Chandrasekaran et al. (2012) proposed a convex program for estimating a sparse graph plus a low-rank term that adjusts for latent variables; however, this approach poses challenges from both computational and statistical perspectives. We propose an alternative, simple solution: apply a hard-thresholding operator to existing graph selection methods. Conceptually simple and c...
• Minimax designs for causal effects in temporal experiments with treatment habituation AI翻译
  作者：Basse, Guillaume W.; Ding, Yi; Toulis, Panos
  作者单位：Stanford University; Massachusetts Institute of Technology (MIT); University of Chicago
  摘要：In many modern settings, such as an online marketplace, randomized experiments need to be executed over multiple time periods. In such temporal experiments, it has been observed that the effects of an intervention on an experimental unit may be large when the unit is first exposed to it, but then it attenuates after repeated exposures. This is typically due to units' habituation to the intervention, or some other form of learning, such as when users gradually start to ignore repeated mails sen...
• Optimal minimax random designs for weighted least squares estimators AI翻译
  作者：Azriel, D.
  作者单位：Technion Israel Institute of Technology
  摘要：This work studies an experimental design problem where the values of a predictor variable, denoted by x, are to be determined with the goal of estimating a function m(x), which is observed with noise. A linear model is fitted to m(x), but it is not assumed that the model is correctly specified. It follows that the quantity of interest is the best linear approximation of m(x), which is denoted by l(x). It is shown that in this framework the ordinary least squares estimator typically leads to an...