516期

最新文章

• Smoothing Parameter and Model Selection for General Smooth Models Comment AI翻译
  作者：Kneib, Thomas
  作者单位：University of Gottingen
• Sensitivity Analysis for Multiple Comparisons in Matched Observational Studies Through Quadratically Constrained Linear Programming AI翻译
  作者：Fogarty, Colin B.; Small, Dylan S.
  作者单位：University of Pennsylvania; Massachusetts Institute of Technology (MIT); Massachusetts Institute of Technology (MIT)
  摘要：A sensitivity analysis in an observational study assesses the robustness of significant findings to unmeasured confounding. While sensitivity analyses in matched observational studies have been well addressed when there is a single outcome variable, accounting for multiple comparisons through the existing methods yields overly conservative results when there are multiple outcome variables of interest. This stems from the fact that unmeasured confounding cannot affect the probability of assignm...
• Personalized Dose Finding Using Outcome Weighted Learning Comment AI翻译
  作者：Wallace, Michael P.; Moodie, Erica E. M.; Stephens, David A.
  作者单位：McGill University; McGill University
• Rejoinder AI翻译
  作者：Wood, Simon N.; Pya, Natalya; Saefken, Benjamin
  作者单位：University of Bristol; KIMEP University; University of Gottingen
• Optimal Model Averaging Estimation for Generalized Linear Models and Generalized Linear Mixed-Effects Models AI翻译
  作者：Zhang, Xinyu; Yu, Dalei; Zou, Guohua; Liang, Hua
  作者单位：Chinese Academy of Sciences; Academy of Mathematics & System Sciences, CAS; Yunnan University of Finance & Economics
  摘要：Considering model averaging estimation in generalized linear models, we propose a weight choice criterion based on the Kullback-Leibler (KL) loss with a penalty term. This criterion is different from that for continuous observations in principle, but reduces to the Mallows criterion in the situation. We prove that the corresponding model averaging estimator is asymptotically optimal under certain assumptions. We further extend our concern to the generalized linear mixed-effects model framework...
• Statistical Matching Analysis for Complex Survey Data With Applications AI翻译
  作者：Conti, Pier Luigi; Marella, Daniela; Scanu, Mauro
  作者单位：Sapienza University Rome; Roma Tre University
  摘要：The goal of statistical matching is the estimation of a joint distribution having observed only samples from its marginals. The lack of joint observations on the variables of interest is the reason of uncertainty about the joint population distribution function. In the present article, the notion of matching error is introduced, and upper-bounded via an appropriate measure of uncertainty. Then, an estimate of the distribution function for the variables not jointly observed is constructed on th...
• Panel Data Models With Interactive Fixed Effects and Multiple Structural Breaks AI翻译
  作者：Li, Degui; Qian, Junhui; Su, Liangjun
  作者单位：University of York - UK; Shanghai Jiao Tong University; Singapore Management University
  摘要：In this article, we consider estimation of common structural breaks in panel data models with unobservable interactive fixed effects. We introduce a penalized principal component (PPC) estimation procedure with an adaptive group fused LASSO to detect the multiple structural breaks in the models. Under some mild conditions, we show that with probability approaching one the proposed method can correctly determine the unknown number of breaks and consistently estimate the common break dates. Furt...
• Testing the Predictor Effect on a Functional Response AI翻译
  作者：Patilea, Valentin; Sanchez-Sellero, Cesar; Saumard, Matthieu
  作者单位：Ecole Nationale de la Statistique et de l'Analyse de l'Information (ENSAI); Bucharest University of Economic Studies; Universidade de Santiago de Compostela; Pontificia Universidad Catolica de Valparaiso
  摘要：This article examines the problem of nonparametric testing,for the no-effect of a random covariate (or predictor) on a functional response. This means testing whether the conditional expectation of the response given the covariate is almost surely zero or not, without imposing any model relating response and covariate. The covariate could be univariate, multivariate, or functional. Our test statistic is a quadratic form involving univariate nearest neighbor smoothing and the asymptotic critica...
• Calibrating an Ice Sheet Model Using High-Dimensional Binary Spatial Date (vol 111, pg 57, 2016) AI翻译
  作者：Chang, W.; Haran, M.; Applegate, P.; Pollard, D.
  作者单位：University System of Ohio; University of Cincinnati
• Inference for Monotone Functions Under Short- and Long-Range Dependence: Confidence Intervals and New Universal Limits AI翻译
  作者：Bagchi, Pramita; Banerjee, Moulinath; Stoev, Stilian A.
  作者单位：Ruhr University Bochum; University of Michigan System; University of Michigan
  摘要：We introduce new point-wise confidence interval estimates for monotone functions observed with additive, dependent noise. Our methodology applies to both short- and long-range dependence regimes for the errors. The interval estimates are obtained via the method of inversion of certain discrepancy statistics. This approach avoids the estimation of nuisance parameters such as the derivative of the unknown function, which previous methods are forced to deal with. The resulting estimates are there...