Missing Information Principle: A Unified Approach for General Truncated and Censored Survival Data Problems [0.03%]
缺失信息原理：截断和删失生存数据问题的统一处理方法
Yifei Sun,Jing Qin,Chiung-Yu Huang
Yifei Sun
It is well known that truncated survival data are subject to sampling bias, where the sampling weight depends on the underlying truncation time distribution. Recently, there has been a rising interest in developing methods to better exploit...
Jaehong Jeong,Mikyoung Jun,Marc G Genton
Jaehong Jeong
Statistical models used in geophysical, environmental, and climate science applications must reflect the curvature of the spatial domain in global data. Over the past few decades, statisticians have developed covariance models that capture ...
Modeling and inference for infectious disease dynamics: a likelihood-based approach [0.03%]
基于likelihood的传染病动力学建模与推理方法研究
Carles Bretó
Carles Bretó
Likelihood-based statistical inference has been considered in most scientific fields involving stochastic modeling. This includes infectious disease dynamics, where scientific understanding can help capture biological processes in so-called...
Shaun R Seaman,Stijn Vansteelandt
Shaun R Seaman
Most methods for handling incomplete data can be broadly classified as inverse probability weighting (IPW) strategies or imputation strategies. The former model the occurrence of incomplete data; the latter, the distribution of the missing ...
Pantelis Samartsidis,Silvia Montagna,Thomas E Nichols et al.
Pantelis Samartsidis et al.
Neuroimaging meta-analysis is an area of growing interest in statistics. The special characteristics of neuroimaging data render classical meta-analysis methods inapplicable and therefore new methods have been developed. We review existing ...
Mitigating Bias in Generalized Linear Mixed Models: The Case for Bayesian Nonparametrics [0.03%]
通用线性混合模型中偏差的缓解：贝叶斯非参数方法的优势
Joseph Antonelli,Lorenzo Trippa,Sebastien Haneuse
Joseph Antonelli
Generalized linear mixed models are a common statistical tool for the analysis of clustered or longitudinal data where correlation is accounted for through cluster-specific random effects. In practice, the distribution of the random effects...
Christopher C Drovandi,Christopher Holmes,James M McGree et al.
Christopher C Drovandi et al.
Big Datasets are endemic, but are often notoriously difficult to analyse because of their size, heterogeneity and quality. The purpose of this paper is to open a discourse on the potential for modern decision theoretic optimal experimental ...
On negative outcome control of unobserved confounding as a generalization of difference-in-differences [0.03%]
关于未观察到的混杂因素的负面结果控制作为差异估计的一般化方法
Tamar Sofer,David B Richardson,Elena Colicino et al.
Tamar Sofer et al.
The difference-in-differences (DID) approach is a well known strategy for estimating the effect of an exposure in the presence of unobserved confounding. The approach is most commonly used when pre-and post-exposure outcome measurements are...
Michael Lawrence,Martin Morgan
Michael Lawrence
This paper reviews strategies for solving problems encountered when analyzing large genomic data sets and describes the implementation of those strategies in R by packages from the Bioconductor project. We treat the scalable processing, sum...
Multi-armed Bandit Models for the Optimal Design of Clinical Trials: Benefits and Challenges [0.03%]
临床试验最优设计的多臂乐队模型：好处和挑战
Sofía S Villar,Jack Bowden,James Wason
Sofía S Villar
Multi-armed bandit problems (MABPs) are a special type of optimal control problem well suited to model resource allocation under uncertainty in a wide variety of contexts. Since the first publication of the optimal solution of the classic M...