Journal of the American Statistical Association

最新文章

• Identification, Semiparametric Efficiency, and Quadruply Robust Estimation in Mediation Analysis with Treatment-Induced Confounding AI翻译
  作者：Xia, Fan; Chan, Kwun Chuen Gary
  作者单位：University of Washington; University of Washington Seattle; University of Washington; University of Washington Seattle
  摘要：Natural mediation effects are often of interest when the goal is to understand a causal mechanism. However, most existing methods and their identification assumptions preclude treatment-induced confounders often present in practice. To address this fundamental limitation, we provide a set of assumptions that identify the natural direct effect in the presence of treatment-induced confounders. Even when some of those assumptions are violated, the estimand still has an interventional direct effec...
• Nonparametric Bounds for Causal Effects in Imperfect Randomized Experiments AI翻译
  作者：Gabriel, Erin E.; Sjolander, Arvid; Sachs, Michael C.
  作者单位：Karolinska Institutet
  摘要：Nonignorable missingness and noncompliance can occur even in well-designed randomized experiments, making the intervention effect that the experiment was designed to estimate nonidentifiable. Nonparametric causal bounds provide a way to narrow the range of possible values for a nonidentifiable causal effect with minimal assumptions. We derive novel bounds for the causal risk difference for a binary outcome and intervention in randomized experiments with nonignorable missingness that is caused ...
• Estimation of the Number of Spiked Eigenvalues in a Covariance Matrix by Bulk Eigenvalue Matching Analysis AI翻译
  作者：Ke, Zheng Tracy; Ma, Yucong; Lin, Xihong
  作者单位：Harvard University; Harvard University; Harvard T.H. Chan School of Public Health
  摘要：The spiked covariance model has gained increasing popularity in high-dimensional data analysis. A fundamental problem is determination of the number of spiked eigenvalues, K. For estimation of K, most attention has focused on the use of top eigenvalues of sample covariance matrix, and there is little investigation into proper ways of using bulk eigenvalues to estimate K. We propose a principled approach to incorporating bulk eigenvalues in the estimation of K. Our method imposes a working mode...
• Mixed-Response State-Space Model for Analyzing Multi-Dimensional Digital Phenotypes AI翻译
  作者：Xu, Tianchen; Chen, Yuan; Zeng, Donglin; Wang, Yuanjia
  作者单位：Columbia University; Memorial Sloan Kettering Cancer Center; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina School of Medicine
  摘要：Digital technologies (e.g., mobile phones) can be used to obtain objective, frequent, and real-world digital phenotypes from individuals. However, modeling these data poses substantial challenges since observational data are subject to confounding and various sources of variabilities. For example, signals on patients' underlying health status and treatment effects are mixed with variation due to the living environment and measurement noises. The digital phenotype data thus shows extensive vari...
• Transformation-Invariant Learning of Optimal Individualized Decision Rules with Time-to-Event Outcomes AI翻译
  作者：Zhou, Yu; Wang, Lan; Song, Rui; Zhao, Tuoyi
  作者单位：University of Miami; North Carolina State University
  摘要：In many important applications of precision medicine, the outcome of interest is time to an event (e.g., death, relapse of disease) and the primary goal is to identify the optimal individualized decision rule (IDR) to prolong survival time. Existing work in this area have been mostly focused on estimating the optimal IDR to maximize the restricted mean survival time in the population. We propose a new robust framework for estimating an optimal static or dynamic IDR with time-to-event outcomes ...
• On Robustness of Individualized Decision Rules AI翻译
  作者：Qi, Zhengling; Pang, Jong-Shi; Liu, Yufeng
  作者单位：George Washington University; University of Southern California; University of North Carolina; University of North Carolina Chapel Hill
  摘要：With the emergence of precision medicine, estimating optimal individualized decision rules (IDRs) has attracted tremendous attention in many scientific areas. Most existing literature has focused on finding optimal IDRs that can maximize the expected outcome for each individual. Motivated by complex individualized decision making procedures and the popular conditional value at risk (CVaR) measure, we propose a new robust criterion to estimate optimal IDRs in order to control the average lower ...
• Tukey's Depth for Object Data AI翻译
  作者：Dai, Xiongtao; Lopez-Pintado, Sara
  作者单位：Iowa State University; Northeastern University
  摘要：We develop a novel exploratory tool for non-Euclidean object data based on data depth, extending celebrated Tukey's depth for Euclidean data. The proposed metric halfspace depth, applicable to data objects in a general metric space, assigns to data points depth values that characterize the centrality of these points with respect to the distribution and provides an interpretable center-outward ranking. Desirable theoretical properties that generalize standard depth properties postulated for Euc...
• Modeling the Extremes of Bivariate Mixture Distributions With Application to Oceanographic Data AI翻译
  作者：Tendijck, Stan; Eastoe, Emma; Tawn, Jonathan; Randell, David; Jonathan, Philip
  作者单位：Lancaster University; Royal Dutch Shell; Royal Dutch Shell
  摘要：There currently exist a variety of statistical methods for modeling bivariate extremes. However, when the dependence between variables is driven by more than one latent process, these methods are likely to fail to give reliable inferences. We consider situations in which the observed dependence at extreme levels is a mixture of a possibly unknown number of much simpler bivariate distributions. For such structures, we demonstrate the limitations of existing methods and propose two new methods: ...
• Saddlepoint Approximations for Spatial Panel Data Models AI翻译
  作者：Jiang, Chaonan; La Vecchia, Davide; Ronchetti, Elvezio; Scaillet, Olivier
  作者单位：University of Geneva; University of Geneva; University of Geneva; University of Geneva
  摘要：We develop new higher-order asymptotic techniques for the Gaussian maximum likelihood estimator in a spatial panel data model, with fixed effects, time-varying covariates, and spatially correlated errors. Our saddlepoint density and tail area approximation feature relative error of order O(1/(n(T-1))) with n being the cross-sectional dimension and T the time-series dimension. The main theoretical tool is the tilted-Edgeworth technique in a nonidentically distributed setting. The density approx...
• Variable Selection for Global Frechet Regression AI翻译
  作者：Tucker, Danielle C.; Wu, Yichao; Mueller, Hans-Georg
  作者单位：University of Illinois System; University of Illinois Chicago; University of Illinois Chicago Hospital; University of California System; University of California Davis
  摘要：Global Frechet regression is an extension of linear regression to cover more general types of responses, such as distributions, networks, and manifolds, which are becoming more prevalent. In such models, predictors are Euclidean while responses are metric space valued. Predictor selection is of major relevance for regression modeling in the presence of multiple predictors but has not yet been addressed for Frechet regression. Due to the metric space-valued nature of the responses, Frechet regr...