-
作者：Wang, Linbo; Zhang, Yuexia; Richardson, Thomas S.; Robins, James M.
作者单位：University of Toronto; University of Toronto; University of Washington; University of Washington Seattle; Harvard University; Harvard T.H. Chan School of Public Health
摘要：Instrumental variables are widely used to deal with unmeasured confounding in observational studies and imperfect randomized controlled trials. In these studies, researchers often target the so-called local average treatment effect as it is identifiable under mild conditions. In this paper we consider estimation of the local average treatment effect under the binary instrumental variable model. We discuss the challenges of causal estimation with a binary outcome and show that, surprisingly, it...
-
作者：Zhou, Wenzhuo; Zhu, Ruoqing; Zeng, Donglin
作者单位：University of Illinois System; University of Illinois Urbana-Champaign; University of North Carolina; University of North Carolina Chapel Hill
摘要：Learning an individualized dose rule in personalized medicine is a challenging statistical problem. Existing methods often suffer from the curse of dimensionality, especially when the decision function is estimated nonparametrically. To tackle this problem, we propose a dimension reduction framework that effectively reduces the estimation to an optimization on a lower-dimensional subspace of the covariates. We exploit the fact that the individualized dose rule can be defined in a subspace span...
-
作者：He, Yinqiu; Meng, Bo; Zeng, Zhenghao; Xu, Gongjun
作者单位：University of Michigan System; University of Michigan; Chinese Academy of Sciences; University of Science & Technology of China, CAS
摘要：Wilks' theorem, which offers universal chi-squared approximations for likelihood ratio tests, is widely used in many scientific hypothesis testing problems. For modern datasets with increasing dimension, researchers have found that the conventional Wilks' phenomenon of the likelihood ratio test statistic often fails. Although new approximations have been proposed in high-dimensional settings, there still lacks a clear statistical guideline regarding how to choose between the conventional and n...
-
作者：Ahfock, D. C.; Astle, W. J.; Richardson, S.
作者单位：University of Cambridge; MRC Biostatistics Unit
摘要：Sketching is a probabilistic data compression technique that has been largely developed by the computer science community. Numerical operations on big datasets can be intolerably slow; sketching algorithms address this issue by generating a smaller surrogate dataset. Typically, inference proceeds on the compressed dataset. Sketching algorithms generally use random projections to compress the original dataset, and this stochastic generation process makes them amenable to statistical analysis. W...
-
作者：Lei, Lihua; Ding, Peng
作者单位：Stanford University; University of California System; University of California Berkeley
摘要：Randomized experiments have become important tools in empirical research. In a completely randomized treatment-control experiment, the simple difference in means of the outcome is unbiased for the average treatment effect, and covariate adjustment can further improve the efficiency without assuming a correctly specified outcome model. In modern applications, experimenters often have access to many covariates, motivating the need for a theory of covariate adjustment under the asymptotic regime ...
-
作者：Lei, Lihua; Ramdas, Aaditya; Fithian, William
作者单位：Stanford University; Carnegie Mellon University; University of California System; University of California Berkeley
摘要：We propose a general framework based on selectively traversed accumulation rules for interactive multiple testing with generic structural constraints on the rejection set. It combines accumulation tests from ordered multiple testing with data-carving ideas from post-selection inference, allowing highly flexible adaptation to generic structural information. Our procedure defines an interactive protocol for gradually pruning a candidate rejection set, beginning with the set of all hypotheses and...
-
作者：Griffin, J. E.; Latuszynski, K. G.; Steel, M. F. J.
作者单位：University of London; University College London; University of Warwick
摘要：The availability of datasets with large numbers of variables is rapidly increasing. The effective application of Bayesian variable selection methods for regression with these datasets has proved difficult since available Markov chain Monte Carlo methods do not perform well in typical problem sizes of interest. We propose new adaptive Markov chain Monte Carlo algorithms to address this shortcoming. The adaptive design of these algorithms exploits the observation that in large-p, small-n setting...
-
作者：Sun, Ming; Zeng, Donglin; Wang, Yuanjia
作者单位：Columbia University; University of North Carolina; University of North Carolina Chapel Hill
摘要：Dynamical systems based on differential equations are useful for modelling the temporal evolution of biomarkers. Such systems can characterize the temporal patterns of biomarkers and inform the detection of interactions between biomarkers. Existing statistical methods for dynamical systems deal mostly with single time-course data based on a linear model or generalized additive model. Hence, they cannot adequately capture the complex interactions between biomarkers; nor can they take into accou...
-
作者：Kosmidis, Ioannis; Firth, David
作者单位：University of Warwick
摘要：Penalization of the likelihood by Jeffreys' invariant prior, or a positive power thereof, is shown to produce finite-valued maximum penalized likelihood estimates in a broad class of binomial generalized linear models. The class of models includes logistic regression, where the Jeffreys-prior penalty is known additionally to reduce the asymptotic bias of the maximum likelihood estimator, and models with other commonly used link functions, such as probit and log-log. Shrinkage towards equiproba...
-
作者：Hiabu, M.; Nielsen, J. P.; Scheike, T. H.
作者单位：University of Sydney; City St Georges, University of London; University of Copenhagen
摘要：We consider an extension of Aalen's additive regression model that allows covariates to have effects that vary on two different time scales. The two time scales considered are equal up to a constant for each individual and vary across individuals, such as follow-up time and age in medical studies or calendar time and age in longitudinal studies. The model was introduced in Scheike (2001), where it was solved using smoothing techniques. We present a new backfitting algorithm for estimating the ...
