3期

最新文章

• Joint sufficient dimension reduction and estimation of conditional and average treatment effects AI翻译
  作者：Huang, Ming-Yueh; Chan, Kwun Chuen Gary
  作者单位：University of Washington; University of Washington Seattle
  摘要：The estimation of treatment effects based on observational data usually involves multiple confounders, and dimension reduction is often desirable and sometimes inevitable. We first clarify the definition of a central subspace that is relevant for the efficient estimation of average treatment effects. A criterion is then proposed to simultaneously estimate the structural dimension, the basis matrix of the joint central subspace, and the optimal bandwidth for estimating the conditional treatment...
• Multiple robustness in factorized likelihood models AI翻译
  作者：Molina, J.; Rotnitzky, A.; Sued, M.; Robins, J. M.
  作者单位：University of Buenos Aires; Universidad Torcuato Di Tella; Harvard University; Harvard T.H. Chan School of Public Health
  摘要：We consider inference under a nonparametric or semiparametric model with likelihood that factorizes as the product of two or more variation-independent factors. We are interested in a finitedimensional parameter that depends on only one of the likelihood factors and whose estimation requires the auxiliary estimation of one or several nuisance functions. We investigate general structures conducive to the construction of so-called multiply robust estimating functions, whose computation requires ...
• Identification and estimation of causal effects with outcomes truncated by death AI翻译
  作者：Wang, Linbo; Zhou, Xiao-Hua; Richardson, Thomas S.
  作者单位：Harvard University; Harvard T.H. Chan School of Public Health; University of Washington; University of Washington Seattle; University of Washington; University of Washington Seattle
  摘要：It is common in medical studies that the outcome of interest is truncated by death, meaning that a subject has died before the outcome could be measured. In this case, restricted analysis among survivors may be subject to selection bias. Hence, it is of interest to estimate the survivor average causal effect, defined as the average causal effect among the subgroup consisting of subjects who would survive under either exposure. In this paper, we consider the identification and estimation proble...
• Optimal Bayes classifiers for functional data and density ratios AI翻译
  作者：Dai, Xiongtao; Mueller, Hans-Georg; Yao, Fang
  作者单位：University of California System; University of California Davis; Peking University; Peking University
  摘要：Bayes classifiers for functional data pose a challenge. One difficulty is that probability density functions do not exist for functional data, so the classical Bayes classifier using density quotients needs to be modified. We propose to use density ratios of projections onto a sequence of eigenfunctions that are common to the groups to be classified. The density ratios are then factorized into density ratios of individual projection scores, reducing the classification problem to obtaining a se...
• Uncertainty quantification under group sparsity AI翻译
  作者：Zhou, Qing; Min, Seunghyun
  作者单位：University of California System; University of California Los Angeles
  摘要：Quantifying the uncertainty in penalized regression under group sparsity is an important open question. We establish, under a high-dimensional scaling, the asymptotic validity of a modified parametric bootstrap method for the group lasso, assuming a Gaussian error model and mild conditions on the design matrix and the true coefficients. Simulation of bootstrap samples provides simultaneous inferences on large groups of coefficients. Through extensive numerical comparisons, we demonstrate that ...
• Simple, scalable and accurate posterior interval estimation AI翻译
  作者：Li, Cheng; Srivastava, Sanvesh; Dunson, David B.
  作者单位：National University of Singapore; University of Iowa; Duke University
  摘要：Standard posterior sampling algorithms, such as Markov chain Monte Carlo procedures, face major challenges in scaling up to massive datasets. We propose a simple and general posterior interval estimation algorithm to rapidly and accurately estimate quantiles of the posterior distributions for one-dimensional functionals. Our algorithm runs Markov chain Monte Carlo in parallel for subsets of the data, and then averages quantiles estimated from each subset. We provide strong theoretical guarante...
• Maximum likelihood estimation for semiparametric regression models with multivariate interval-censored data AI翻译
  作者：Zeng, Donglin; Gao, Fei; Lin, D. Y.
  作者单位：University of North Carolina; University of North Carolina Chapel Hill
  摘要：Interval-censored multivariate failure time data arise when there are multiple types of failure or there is clustering of study subjects and each failure time is known only to lie in a certain interval. We investigate the effects of possibly time-dependent covariates on multivariate failure times by considering a broad class of semiparametric transformation models with random effects, and we study nonparametric maximum likelihood estimation under general interval-censoring schemes. We show tha...
• Robust reduced-rank regression AI翻译
  作者：She, Y.; Chen, K.
  作者单位：State University System of Florida; Florida State University; University of Connecticut
  摘要：In high-dimensional multivariate regression problems, enforcing low rank in the coefficient matrix offers effective dimension reduction, which greatly facilitates parameter estimation and model interpretation. However, commonly used reduced-rank methods are sensitive to data corruption, as the low-rank dependence structure between response variables and predictors is easily distorted by outliers. We propose a robust reduced-rank regression approach for joint modelling and outlier detection. Th...
• Non-strange weird resampling for complex survival data AI翻译
  作者：Dobler, D.; Beyersmann, J.; Pauly, M.
  作者单位：Ulm University
  摘要：This paper introduces a new data-dependent multiplier bootstrap for nonparametric analysis of survival data, possibly subject to competing risks. The new procedure includes the general wild bootstrap and the weird bootstrap as special cases. The data may be subject to independent right-censoring and left-truncation. The asymptotic correctness of the proposed resampling procedure is proven under standard assumptions. Simulation results on time-simultaneous inference suggest that the weird boots...
• Interleaved lattice-based minimax distance designs AI翻译
  作者：He, Xu
  作者单位：Chinese Academy of Sciences; Academy of Mathematics & System Sciences, CAS
  摘要：We propose a new method for constructing minimax distance designs, which are useful for computer experiments. To circumvent computational difficulties, we consider designs with an interleaved lattice structure, a newly defined class of lattice that has repeated or alternated layers based on any single dimension. Such designs have boundary adaptation and low-thickness properties. From our numerical results, the proposed designs are by far the best minimax distance designs for moderate or large ...