3期

最新文章

• Globally efficient non-parametric inference of average treatment effects by empirical balancing calibration weighting AI翻译
  作者：Chan, Kwun Chuen Gary; Yam, Sheung Chi Phillip; Zhang, Zheng
  作者单位：University of Washington; University of Washington Seattle; Chinese University of Hong Kong
  摘要：The estimation of average treatment effects based on observational data is extremely important in practice and has been studied by generations of statisticians under different frameworks. Existing globally efficient estimators require non-parametric estimation of a propensity score function, an outcome regression function or both, but their performance can be poor in practical sample sizes. Without explicitly estimating either function, we consider a wide class of calibration weights construct...
• Randomization inference for treatment effect variation AI翻译
  作者：Ding, Peng; Feller, Avi; Miratrix, Luke
  作者单位：Harvard University
  摘要：Applied researchers are increasingly interested in whether and how treatment effects vary in randomized evaluations, especially variation that is not explained by observed covariates. We propose a model-free approach for testing for the presence of such unexplained variation. To use this randomization-based approach, we must address the fact that the average treatment effect, which is generally the object of interest in randomized experiments, actually acts as a nuisance parameter in this sett...
• Of quantiles and expectiles: consistent scoring functions, Choquet representations and forecast rankings AI翻译
  作者：Ehm, Werner; Gneiting, Tilmann; Jordan, Alexander; Krueger, Fabian
  作者单位：Heidelberg Institute for Theoretical Studies; Helmholtz Association; Karlsruhe Institute of Technology
  摘要：In the practice of point prediction, it is desirable that forecasters receive a directive in the form of a statistical functional. For example, forecasters might be asked to report the mean or a quantile of their predictive distributions. When evaluating and comparing competing forecasts, it is then critical that the scoring function used for these purposes be consistent for the functional at hand, in the sense that the expected score is minimized when following the directive. We show that any...
• Drift estimation in sparse sequential dynamic imaging, with application to nanoscale fluorescence microscopy AI翻译
  作者：Hartmann, Alexander; Huckemann, Stephan; Dannemann, Joern; Laitenberger, Oskar; Geisler, Claudia; Egner, Alexander; Munk, Axel
  作者单位：University of Gottingen; Max Planck Society
  摘要：A major challenge in many modern superresolution fluorescence microscopy techniques at the nanoscale lies in the correct alignment of long sequences of sparse but spatially and temporally highly resolved images. This is caused by the temporal drift of the protein structure, e.g. due to temporal thermal inhomogeneity of the object of interest or its supporting area during the observation process. We develop a simple semiparametric model for drift correction in single-marker switching microscopy...
• Estimating multivariate volatility models equation by equation AI翻译
  作者：Francq, Christian; Zakoian, Jean-Michel
  作者单位：Institut Polytechnique de Paris; ENSAE Paris; Universite de Lille
  摘要：The paper investigates the estimation of a wide class of multivariate volatility models. Instead of estimating an m-multivariate volatility model, a much simpler and numerically efficient method consists in estimating m univariate generalized auto-regressive conditional heteroscedasticity type models equation by equation in the first step, and a correlation matrix in the second step. Strong consistency and asymptotic normality of the equation-by-equation estimator are established in a very gen...
• High dimensional ordinary least squares projection for screening variables AI翻译
  作者：Wang, Xiangyu; Leng, Chenlei
  作者单位：Duke University; University of Warwick
  摘要：Variable selection is a challenging issue in statistical applications when the number of predictors p far exceeds the number of observations n. In this ultrahigh dimensional setting, the sure independence screening procedure was introduced to reduce the dimensionality significantly by preserving the true model with overwhelming probability, before a refined second-stage analysis. However, the aforementioned sure screening property strongly relies on the assumption that the important variables ...
• Truncated linear models for functional data AI翻译
  作者：Hall, Peter; Hooker, Giles
  作者单位：University of Melbourne; Cornell University
  摘要：A conventional linear model for functional data involves expressing a response variable Y in terms of the explanatory function X(t), via the model Y=a+integral(I)b(t) X(t)dt + error, where a is a scalar, b is an unknown function and I = [0, alpha] is a compact interval. However, in some problems the support of b or X, I-1 say, is a proper and unknown subset of I, and is a quantity of particular practical interest. Motivated by a real data example involving particulate emissions, we develop met...