2019

最新文章

• Computer model calibration with confidence and consistency AI翻译
  作者：Plumlee, Matthew
  作者单位：Northwestern University
  摘要：The paper proposes and examines a calibration method for inexact models. The method produces a confidence set on the parameters that includes the best parameter with a desired probability under any sample size. Additionally, this confidence set is shown to be consistent in that it excludes suboptimal parameters in large sample environments. The method works and the results hold with few assumptions; the ideas are maintained even with discrete input spaces or parameter spaces. Computation of th...
• Non-parametric cure rate estimation under insufficient follow-up by using extremes AI翻译
  作者：Escobar-Bach, Mikael; Van Keilegom, Ingrid
  作者单位：KU Leuven
  摘要：An important research topic in survival analysis is related to the modelling and estimation of the cure rate, i.e. the proportion of subjects who will never experience the event of interest. However, most estimation methods proposed so far in the literature do not handle the case of insufficient follow-up, i.e. when the right end point of the support of the censoring time is strictly less than that of the survival time of the susceptible subjects, and consequently these estimators overestimate...
• Scalable importance tempering and Bayesian variable selection AI翻译
  作者：Zanella, Giacomo; Roberts, Gareth
  作者单位：Bocconi University; University of Warwick
  摘要：We propose a Monte Carlo algorithm to sample from high dimensional probability distributions that combines Markov chain Monte Carlo and importance sampling. We provide a careful theoretical analysis, including guarantees on robustness to high dimensionality, explicit comparison with standard Markov chain Monte Carlo methods and illustrations of the potential improvements in efficiency. Simple and concrete intuition is provided for when the novel scheme is expected to outperform standard scheme...
• Covariate-assisted ranking and screening for large-scale two-sample inference AI翻译
  作者：Cai, T. Tony; Sun, Wenguang; Wang, Weinan
  作者单位：University of Pennsylvania; University of Southern California
  摘要：Two-sample multiple testing has a wide range of applications. The conventional practice first reduces the original observations to a vector of p-values and then chooses a cut-off to adjust for multiplicity. However, this data reduction step could cause significant loss of information and thus lead to suboptimal testing procedures. We introduce a new framework for two-sample multiple testing by incorporating a carefully constructed auxiliary variable in inference to improve the power. A data-dr...
• Robust causal inference with continuous instruments using the local instrumental variable curve AI翻译
  作者：Kennedy, Edward H.; Lorch, Scott; Small, Dylan S.
  作者单位：Carnegie Mellon University; University of Pennsylvania
  摘要：Instrumental variables are commonly used to estimate effects of a treatment afflicted by unmeasured confounding, and in practice instruments are often continuous (e.g. measures of distance, or treatment preference). However, available methods for continuous instruments have important limitations: they either require restrictive parametric assumptions for identification, or else rely on modelling both the outcome and the treatment process well (and require modelling effect modification by all a...
• A general framework for quantile estimation with incomplete data AI翻译
  作者：Han, Peisong; Kong, Linglong; Zhao, Jiwei; Zhou, Xingcai
  作者单位：University of Michigan System; University of Michigan; University of Alberta; State University of New York (SUNY) System; University at Buffalo, SUNY; Nanjing Audit University
  摘要：Quantile estimation has attracted significant research interest in recent years. However, there has been only a limited literature on quantile estimation in the presence of incomplete data. We propose a general framework to address this problem. Our framework combines the two widely adopted approaches for missing data analysis, the imputation approach and the inverse probability weighting approach, via the empirical likelihood method. The method proposed is capable of dealing with many differe...
• Report of the Editors2018 AI翻译
  作者：Dunson, David; Wood, Simon
• On the design of experiments with ordered treatments AI翻译
  作者：Singh, Satya Prakash; Davidov, Ori
  作者单位：University of Haifa
  摘要：There are many situations where one expects an ordering among K > 2 experimental groups or treatments. Although there is a large body of literature dealing with the analysis under order restrictions, surprisingly, very little work has been done in the context of the design of experiments. Here, a principled approach to the design of experiments with ordered treatments is provided. In particular we propose two classes of designs which are optimal for testing different types of hypotheses. The t...
• Clustering functional data into groups by using projections AI翻译
  作者：Delaigle, Aurore; Hall, Peter; Tung Pham
  作者单位：University of Melbourne
  摘要：We show that, in the functional data context, by appropriately exploiting the functional nature of the data, it is possible to cluster the observations asymptotically perfectly. We demonstrate that this level of performance can sometimes be achieved by the k-means algorithm as long as the data are projected on a carefully chosen finite dimensional space. In general, the notion of an ideal cluster is not clearly defined. We derive our results in the setting where the data come from two populati...
• Construction of row-column factorial designs AI翻译
  作者：Godolphin, J. D.
  作者单位：University of Surrey
  摘要：The arrangement of 2(n)-factorials in row-column designs to estimate main effects and two-factor interactions is investigated. Single-replicate constructions are given which enable estimation of all main effects and maximize the number of estimable two-factor interactions. Constructions and guidance are given for multireplicate designs in single arrays and in multiple arrays. Consideration is given to constructions for 2(n-t) fractional factorials.