Huijuan Ma,Wei Zhao,John Hanfelt et al.
Huijuan Ma et al.
Chronic disease studies often collect data on biological and clinical markers at follow-up visits to monitor disease progression. Viewing such longitudinal measurements governed by latent continuous trajectories, we develop a new dynamic re...
Comparison of Longitudinal Trajectories Using a High-dimensional Partial Linear Semiparametric Mixed-Effects Model [0.03%]
基于高维半参数混合效应模型的纵向数据轨迹比较分析方法研究
Sami Leon,Tong Tong Wu
Sami Leon
In longitudinal research, it is essential to compare sets of trajectories, commonly seen as changes over time in different treatment or patient groups. This paper presents a partial linear semiparametric mixed-effects model (PLSMM) for the ...
Lu Zhang,Wenpin Tang,Sudipto Banerjee
Lu Zhang
We develop Bayesian predictive stacking for geostatistical models, where the primary inferential objective is to provide inference on the latent spatial random field and conduct spatial predictions at arbitrary locations. We exploit analyti...
Yubai Yuan,Yijiao Zhang,Babak Shahbaba et al.
Yubai Yuan et al.
Detecting dynamic patterns shared across heterogeneous datasets is a critical yet challenging task in many scientific domains, particularly within the biomedical sciences. Systematic heterogeneity inherent in diverse data sources can signif...
Lorenzo Cappello,Amandine Véber,Julia A Palacios
Lorenzo Cappello
Molecular sequence variation at a locus informs about the evolutionary history of the sample and past population size dynamics. The Kingman coalescent is used in a generative model of molecular sequence variation to infer evolutionary param...
Jin-Hong Du,Zhenghao Zeng,Edward H Kennedy et al.
Jin-Hong Du et al.
With the evolution of single-cell RNA sequencing techniques into a standard approach in genomics, it has become possible to conduct cohort-level causal inferences based on single-cell-level measurements. However, the individual gene express...
A Latent Variable Model for Individual Degree Measures in Respondent-Driven Sampling [0.03%]
响应驱动采样中个体度量的潜在变量模型
Yibo Wang,Sunghee Lee,Michael R Elliott
Yibo Wang
Respondent-driven sampling (RDS) is widely used to collect data from hidden populations in social and biomedical science. Although RDS may provide comprehensive coverage of the target hidden population through social network recruitment, it...
Sijia Li,Peter B Gilbert,Rui Duan et al.
Sijia Li et al.
We introduce a new data fusion method that utilizes multiple data sources to estimate a smooth, finite-dimensional parameter. Most existing methods only make use of fully aligned data sources that share common conditional distributions of o...
Ashesh Rambachan,Jonathan Roth
Ashesh Rambachan
Design-based frameworks of uncertainty are frequently used in settings where the treatment is (conditionally) randomly assigned. This article develops a design-based framework suitable for analyzing quasi-experimental settings in the social...
Miheer Dewaskar,Christopher Tosh,Jeremias Knoblauch et al.
Miheer Dewaskar et al.
Likelihood-based inferences have been remarkably successful in wide-spanning application areas. However, even after due diligence in selecting a good model for the data at hand, there is inevitably some amount of model misspecification: out...