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作者：Guedon, T.; Baey, C.; Kuhn, E.
作者单位：Universite Paris Saclay; INRAE; Universite de Lille; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute for Mathematical Sciences (INSMI)
摘要：We examine the problem of variance component testing in general mixed effects models using the likelihood ratio test. We account for the presence of nuisance parameters, ie, the fact that some untested variances might also be equal to zero. Two main issues arise in this context, leading to a nonregular setting. First, under the null hypothesis, the true parameter value lies on the boundary of the parameter space. Moreover, due to the presence of nuisance parameters, the exact locations of thes...
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作者：Hojsgaard, S.; Lauritzen, S.
作者单位：Aalborg University; University of Copenhagen
摘要：In Gaussian graphical models, the likelihood equations must typically be solved iteratively. This paper investigates two algorithms: a version of iterative proportional scaling, which avoids inversion of large matrices, and an algorithm based on convex duality and operating on the covariance matrix by neighbourhood coordinate descent, which corresponds to the graphical lasso with zero penalty. For large, sparse graphs, the iterative proportional scaling algorithm appears feasible and has simpl...
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作者：Yang, Cheng-Han; Cheng, Yu-Jen
作者单位：University of Texas System; University of Texas Health Science Center Houston; National Tsing Hua University
摘要：We propose a model-free variable screening method for the optimal treatment regime with high-dimensional survival data. The proposed screening method provides a unified framework to select the active variables in a prespecified target population, including the treated group as a special case. Based on this framework, the optimal treatment regime is exactly the optimal classifier that minimizes a weighted misclassification error rate, with weights associated with survival outcome variables, the...
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作者：Stensrud, M. J.; Laurendeau, J. D.; Sarvet, A. L.
作者单位：Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne
摘要：We consider optimal regimes for algorithm-assisted human decision-making. Such regimes are decision functions of measured pre-treatment variables and, by leveraging natural treatment values, enjoy a superoptimality property whereby they are guaranteed to outperform conventional optimal regimes. When there is unmeasured confounding, the benefit of using superoptimal regimes can be considerable. When there is no unmeasured confounding, superoptimal regimes are identical to conventional optimal r...
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作者：Zhao, Anqi; Ding, Peng; Li, Fan
作者单位：Duke University; University of California System; University of California Berkeley; Duke University
摘要：Covariate adjustment can improve precision in analysing randomized experiments. With fully observed data, regression adjustment and propensity score weighting are asymptotically equivalent in improving efficiency over unadjusted analysis. When some outcomes are missing, we consider combining these two adjustment methods with inverse probability of observation weighting for handling missing outcomes, and show that the equivalence between the two methods breaks down. Regression adjustment no lon...
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作者：Zhang, Jeffrey; Small, Dylan S.; Heng, Siyu
作者单位：University of Pennsylvania; New York University
摘要：Matching is one of the most widely used study designs for adjusting for measured confounders in observational studies. However, unmeasured confounding may exist and cannot be removed by matching. Therefore, a sensitivity analysis is typically needed to assess a causal conclusion's sensitivity to unmeasured confounding. Sensitivity analysis frameworks for binary exposures have been well established for various matching designs and are commonly used in various studies. However, unlike the binary...
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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 ...
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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...
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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...
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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...
