-
作者：Wen, Zihao; Dowe, David L.
作者单位：Monash University; South China Agricultural University
-
作者：Ying, Andrew
摘要：Many epidemiological and clinical studies aim to analyse a time-to-event endpoint. A common complication is right censoring. In some cases, right censoring occurs when subjects are still surviving after the study terminates or move out of the study area. In such cases, right censoring is typically treated as independent or noninformative. This assumption can be further relaxed to conditional independent censoring by leveraging possibly time-varying covariate information, if available, and assu...
-
作者：Schmidt-Hieber, Johannes
作者单位：University of Twente
-
作者：Kang, Seungwoo; Oh, Hee-Seok
作者单位：Seoul National University (SNU)
摘要：We propose a new method for dimension reduction of high-dimensional spherical data based on the nonlinear projection of sphere-valued data to a randomly chosen subsphere. The proposed method, spherical random projection, leads to a probabilistic lower-dimensional mapping of spherical data into a subsphere of the original. In this paper, we investigate some properties of spherical random projection, including expectation preservation and distance concentration, from which we derive an analogue ...
-
作者：Wadsworth, Jennifer L.; Campbell, Ryan
作者单位：Lancaster University; Lancaster University
摘要：A geometric representation for multivariate extremes, based on the shapes of scaled sample clouds in light-tailed margins and their so-called limit sets, has recently been shown to connect several existing extremal dependence concepts. However, these results are purely probabilistic, and the geometric approach itself has not been fully exploited for statistical inference. We outline a method for parametric estimation of the limit set shape, which includes a useful non-/semi-parametric estimate...
-
作者：Yang, Yachong; Kuchibhotla, Arun Kumar; Tchetgen, Eric Tchetgen
作者单位：University of Pennsylvania; Carnegie Mellon University; University of Pennsylvania
摘要：Conformal prediction has received tremendous attention in recent years and has offered new solutions to problems in missing data and causal inference; yet these advances have not leveraged modern semi-parametric efficiency theory for more efficient uncertainty quantification. We consider the problem of obtaining well-calibrated prediction regions that can data adaptively account for a shift in the distribution of covariates between training and test data. Under a covariate shift assumption ana...
-
作者：Grunwald, Peter
作者单位：Leiden University; Leiden University - Excl LUMC
-
作者：Dawid, Philip
作者单位：University of Cambridge
摘要：The prior distribution is the usual starting point for Bayesian uncertainty. In this paper, we present a different perspective that focuses on missing observations as the source of statistical uncertainty, with the parameter of interest being known precisely given the entire population. We argue that the foundation of Bayesian inference is to assign a distribution on missing observations conditional on what has been observed. In the i.i.d. setting with an observed sample of size n, the Bayesia...
-
作者：Mena, Ramses H.
作者单位：Universidad Nacional Autonoma de Mexico; Universidad Nacional Autonoma de Mexico
-
作者：Tallman, Emily; West, Mike
作者单位：Duke University; Duke University
摘要：Decision-guided perspectives on model uncertainty expand traditional statistical thinking about managing, comparing, and combining inferences from sets of models. Bayesian predictive decision synthesis (BPDS) advances conceptual and theoretical foundations, and defines new methodology that explicitly integrates decision-analytic outcomes into the evaluation, comparison, and potential combination of candidate models. BPDS extends recent theoretical and practical advances based on both Bayesian ...
