A statistical test to reject the structural interpretation of a latent factor model [0.03%]
一个拒绝潜在因素模型结构解释的统计检验方法
Tyler J VanderWeele,Stijn Vansteelandt
Tyler J VanderWeele
Factor analysis is often used to assess whether a single univariate latent variable is sufficient to explain most of the covariance among a set of indicators for some underlying construct. When evidence suggests that a single factor is adeq...
Arthur P Guillaumin,Adam M Sykulski,Sofia C Olhede et al.
Arthur P Guillaumin et al.
We provide a computationally and statistically efficient method for estimating the parameters of a stochastic covariance model observed on a regular spatial grid in any number of dimensions. Our proposed method, which we call the Debiased S...
Fast increased fidelity samplers for approximate Bayesian Gaussian process regression [0.03%]
快速提高保真度采样器用于近似贝叶斯高斯过程回归
Kelly R Moran,Matthew W Wheeler
Kelly R Moran
Gaussian processes (GPs) are common components in Bayesian non-parametric models having a rich methodological literature and strong theoretical grounding. The use of exact GPs in Bayesian models is limited to problems containing several tho...
Wei Zhao,Limin Peng,John Hanfelt
Wei Zhao
Recurrent events data frequently arise in chronic disease studies, providing rich information on disease progression. The concept of latent class offers a sensible perspective to characterize complex population heterogeneity in recurrent ev...
Sean Jewell,Paul Fearnhead,Daniela Witten
Sean Jewell
While many methods are available to detect structural changes in a time series, few procedures are available to quantify the uncertainty of these estimates post-detection. In this work, we fill this gap by proposing a new framework to test ...
Efficient Evaluation of Prediction Rules in Semi-Supervised Settings under Stratified Sampling [0.03%]
分层抽样下半监督设置中有效评估预测规则
Jessica Gronsbell,Molei Liu,Lu Tian et al.
Jessica Gronsbell et al.
In many contemporary applications, large amounts of unlabeled data are readily available while labeled examples are limited. There has been substantial interest in semi-supervised learning (SSL) which aims to leverage unlabeled data to impr...
Qingyuan Zhao,Dylan S Small,Ashkan Ertefaie
Qingyuan Zhao
Effect modification occurs when the effect of the treatment on an outcome varies according to the level of other covariates and often has important implications in decision-making. When there are tens or hundreds of covariates, it becomes n...