-
作者：Zhang, J.; Xue, F.; Xu, Q.; Lee, J.; Qu, A.
作者单位：University of California System; University of California Irvine; Purdue University System; Purdue University; University of California System; University of California Irvine; University of California System; University of California Irvine
摘要：Mobile health has emerged as a major success for tracking individual health status, due to the popularity and power of smartphones and wearable devices. This has also brought great challenges in handling heterogeneous, multi-resolution data that arise ubiquitously in mobile health due to irregular multivariate measurements collected from individuals. In this paper, we propose an individualized dynamic latent factor model for irregular multi-resolution time series data to interpolate unsampled ...
-
作者：Wang, J. Y.; Ye, Z. S.; Chen, Y.
作者单位：National University of Singapore; University of Pennsylvania
摘要：Likelihood-based inference under nonconvex constraints on model parameters has become increasingly common in biomedical research. In this paper, we establish large-sample properties of the maximum likelihood estimator when the true parameter value lies at the boundary of a nonconvex parameter space. We further derive the asymptotic distribution of the likelihood ratio test statistic under nonconvex constraints on model parameters. A general Monte Carlo procedure for generating the limiting dis...
-
作者：Castelletti, F.; Peluso, S.
作者单位：Catholic University of the Sacred Heart; University of Milano-Bicocca
摘要：Directed acyclic graphs provide an effective framework for learning causal relationships among variables given multivariate observations. Under pure observational data, directed acyclic graphs encoding the same conditional independencies cannot be distinguished and are collected into Markov equivalence classes. In many contexts, however, observational measurements are supplemented by interventional data that improve directed acyclic graph identifiability and enhance causal effect estimation. W...
-
作者：He, Yihui; Han, Fang
作者单位：Peking University; University of Washington; University of Washington Seattle
摘要：This paper re-examines the work of on propensity score matching for average treatment effect estimation. We explore the asymptotic behaviour of these estimators when the number of nearest neighbours, M, grows with the sample size. It is shown, while not surprising, but technically nontrivial, that the modified estimators can improve upon the original fixed M-estimators in terms of efficiency. Additionally, we demonstrate the potential to attain the semiparametric efficiency lower bound when th...
-
作者：Cape, J.
作者单位：University of Wisconsin System; University of Wisconsin Madison
摘要：Varimax factor rotations, while popular among practitioners in psychology and statistics since being introduced by , have historically been viewed with skepticism and suspicion by some theoreticians and mathematical statisticians. Now, work by provides new, fundamental insight: varimax rotations provably perform statistical estimation in certain classes of latent variable models when paired with spectral-based matrix truncations for dimensionality reduction. We build on this new-found understa...
-
作者：Kallus, Nathan; Uehara, Masatoshi
作者单位：Cornell University; Cornell University
摘要：We study the efficient off-policy evaluation of natural stochastic policies, which are defined in terms of deviations from the unknown behaviour policy. This is a departure from the literature on off-policy evaluation that largely considers the evaluation of explicitly specified policies. Crucially, off-line reinforcement learning with natural stochastic policies can help alleviate issues of weak overlap, lead to policies that build upon current practice and improve policies' implementability ...
-
作者：Politis, Dimitris
作者单位：University of California System; University of California San Diego
摘要：Subsampling has seen a resurgence in the big data era where the standard, full-resample size bootstrap can be infeasible to compute. Nevertheless, even choosing a single random subsample of size b can be computationally challenging with both b and the sample size n being very large. This paper shows how a set of appropriately chosen, nonrandom subsamples can be used to conduct effective, and computationally feasible, subsampling distribution estimation. Furthermore, the same set of subsamples ...
-
作者：Ghassami, Amiremad; Yang, Alan; Shpitser, Ilya; Tchetgen, Eric Tchetgen
作者单位：Boston University; Stanford University; Johns Hopkins University; University of Pennsylvania
摘要：Proximal causal inference was recently proposed as a framework to identify causal effects from observational data in the presence of hidden confounders for which proxies are available. In this paper, we extend the proximal causal inference approach to settings where identification of causal effects hinges upon a set of mediators that are not observed, yet error prone proxies of the hidden mediators are measured. Specifically, (i) we establish causal hidden mediation analysis, which extends cla...
-
作者：Zhu, Changbo; Yao, Junwen; Wang, Jane-Ling
作者单位：University of Notre Dame; University of California System; University of California Davis
摘要：With the advance of science and technology, more and more data are collected in the form of functions. A fundamental question for a pair of random functions is to test whether they are independent. This problem becomes quite challenging when the random trajectories are sampled irregularly and sparsely for each subject. In other words, each random function is only sampled at a few time-points, and these time-points vary with subjects. Furthermore, the observed data may contain noise. To the bes...
-
作者：Bai, Lujia; Wu, Weichi
作者单位：Tsinghua University; Tsinghua University
摘要：Long-run covariance matrix estimation is the building block of time series inference. The corresponding difference-based estimator, which avoids detrending, has attracted considerable interest due to its robustness to both smooth and abrupt structural breaks and its competitive finite sample performance. However, existing methods mainly focus on estimators for the univariate process, while their direct and multivariate extensions for most linear models are asymptotically biased. We propose a n...
