-
作者：Skrondal, A.; Rabe-Hesketh, S.
作者单位：Norwegian Institute of Public Health (NIPH); University of California System; University of California Berkeley
摘要：We consider estimation of mixed-effects logistic regression models for longitudinal data when missing outcomes are not missing at random. A typology of missingness mechanisms is presented that includes missingness dependent on observed or missing current outcomes, observed or missing lagged outcomes and subject-specific effects. When data are not missing at random, consistent estimation by maximum marginal likelihood generally requires correct parametric modelling of the missingness mechanism,...
-
作者：Zhang, Xinyu; Zou, Guohua; Liang, Hua
作者单位：Chinese Academy of Sciences; Academy of Mathematics & System Sciences, CAS; George Washington University
摘要：This article studies model averaging for linear mixed-effects models. We establish an unbiased estimator of the squared risk for the model averaging, and use the estimator as a criterion for choosing weights. The resulting model average estimator is proved to be asymptotically optimal under some regularity conditions. Simulation experiments show it is superior or comparable to estimators based on the final models selected by the commonly-used methods and some existing averaging procedures. The...
-
作者：Feng, Changyong; Wang, Hongyue; Chen, Tian; Tu, Xin M.
作者单位：University of Rochester
摘要：Exact forms of Taylor expansion for vector-valued functions have been incorrectly used in many statistical publications. We offer two methods to correct this error.
-
作者：Kundu, S.; Dunson, D. B.
作者单位：Texas A&M University System; Texas A&M University College Station; Duke University
摘要：Although discrete mixture modelling has formed the backbone of the literature on Bayesian density estimation, there are some well-known disadvantages. As an alternative to discrete mixtures, we propose a class of priors based on random nonlinear functions of a uniform latent variable with an additive residual. The induced prior for the density is shown to have desirable properties, including ease of centring on an initial guess, large support, posterior consistency and straightforward computat...
-
作者：Wang, Mei-Cheng; Huang, Chiung-Yu
作者单位：Johns Hopkins University; Johns Hopkins Bloomberg School of Public Health; Johns Hopkins University; Johns Hopkins Medicine
摘要：This paper proposes a unified framework to characterize the rate function of a recurrent event process through shape and size parameters. In contrast to the intensity function, which is the event occurrence rate conditional on the event history, the rate function is the occurrence rate unconditional on the event history, and thus it can be interpreted as a population-averaged count of events in unit time. In this paper, shape and size parameters are introduced and used to characterize the asso...
-
作者：Vansteelandt, S.; Martinussen, T.; Tchetgen, E. J.
作者单位：Ghent University; University of Copenhagen; Harvard University
摘要：We consider additive hazard models (Aalen, 1989) for the effect of a randomized treatment on a survival outcome, adjusting for auxiliary baseline covariates. We demonstrate that the Aalen least-squares estimator of the treatment effect parameter is asymptotically unbiased, even when the hazard's dependence on time or on the auxiliary covariates is misspecified, and even away from the null hypothesis of no treatment effect. We furthermore show that adjustment for auxiliary baseline covariates d...
-
作者：Vogel, D.; Tyler, D. E.
作者单位：Ruhr University Bochum; Rutgers University System; Rutgers University New Brunswick
摘要：Robust estimators of the restricted covariance matrices associated with elliptical graphical models are studied. General asymptotic results, which apply to both decomposable and nondecomposable graphical models, are presented for robust plug-in type estimators. These extend results previously established only for the decomposable case. Furthermore, a class of graphical M-estimators for the restricted covariance matrices is introduced and compared with the corresponding plug-in M-estimators. Th...
-
作者：Boisbunon, A.; Maruyama, Y.
作者单位：University of Tokyo
摘要：This work treats the problem of estimating the predictive density of a random vector when both the mean vector and the variance are unknown. We prove that the density of reference in this context is inadmissible under the Kullback-Leibler loss in a nonasymptotic framework. Our result holds even when the dimension of the vector is strictly lower than three, which is surprising compared to the known variance setting. Finally, we discuss the relationship between the prediction and the estimation ...
-
作者：Kuroki, Manabu; Pearl, Judea
作者单位：Research Organization of Information & Systems (ROIS); Institute of Statistical Mathematics (ISM) - Japan; University of California System; University of California Los Angeles
摘要：This paper highlights several areas where graphical techniques can be harnessed to address the problem of measurement errors in causal inference. In particular, it discusses the control of unmeasured confounders in parametric and nonparametric models and the computational problem of obtaining bias-free effect estimates in such models. We derive new conditions under which causal effects can be restored by observing proxy variables of unmeasured confounders with/without external studies.
-
作者：Petrone, S.; Rousseau, J.; Scricciolo, C.
作者单位：Bocconi University; Institut Polytechnique de Paris; ENSAE Paris
摘要：Bayesian inference is attractive due to its internal coherence and for often having good frequentist properties. However, eliciting an honest prior may be difficult, and common practice is to take an empirical Bayes approach using an estimate of the prior hyperparameters. Although not rigorous, the underlying idea is that, for a sufficiently large sample size, empirical Bayes methods should lead to similar inferential answers as a proper Bayesian inference. However, precise mathematical result...
