Bayesian Signal Matching for Transfer Learning in ERP-Based Brain Computer Interface [0.03%]
基于ERP的脑计算机接口中转移学习的贝叶斯信号匹配方法研究
Tianwen Ma,Jane E Huggins,Jian Kang
Tianwen Ma
An Event-Related Potential (ERP)-based Brain-Computer Interface (BCI) Speller System assists people with disabilities to communicate by decoding electroencephalogram (EEG) signals. A P300-ERP embedded in EEG signals arises in response to a ...
Nonparametric Causal Inference for Optogenetics: Sequential Excursion Effects for Dynamic Regimes [0.03%]
非参因果推理在光遗传学中的应用：动态方案下的序列效应
Gabriel Loewinger,Alexander W Levis,Francisco Pereira
Gabriel Loewinger
Optogenetics is a powerful neuroscience technique for studying how neural circuit manipulation affects behavior. Standard analysis conventions discard information and severely limit the scope of the causal questions that can be probed. To a...
SPARCC: Semi-Parametric Robust Estimation in a Right-Censored Covariate Model [0.03%]
半参 robust 估计在右删失协变量模型中的应用
Seong-Ho Lee,Brian D Richardson,Yanyuan Ma et al.
Seong-Ho Lee et al.
In Huntington disease research, a current goal is to understand how symptoms change prior to a clinical diagnosis. Statistically, achieving this goal entails modeling symptom severity as a function of the covariate 'time of diagnosis,' whic...
Inference for Deep Neural Network Estimators in Generalized Nonparametric Models [0.03%]
广义非参数模型中深度神经网络估计器的推断方法
Xuran Meng,Yi Li
Xuran Meng
While deep neural networks (DNNs) are used for prediction, inference on DNN-estimated subject-specific means for categorical or exponential family outcomes remains underexplored. We address this by proposing a DNN estimator under generalize...
Winner's Curse Free Robust Mendelian Randomization with Summary Data [0.03%]
基于汇总数据的无 winner's curse 效应的稳健孟德尔随机化方法
Zhongming Xie,Wanheng Zhang,Jingshen Wang et al.
Zhongming Xie et al.
In the past decade, the increased availability of genome-wide association studies summary data has popularized Mendelian Randomization (MR) for conducting causal inference. MR analyses, incorporating genetic variants as instrumental variabl...
Bayesian Nonparametric Common Atoms Regression for Generating Synthetic Controls in Clinical Trials [0.03%]
贝叶斯非参数公共原子回归在临床试验中生成合成控制的运用
Noirrit Kiran Chandra,Abhra Sarkar,John F de Groot et al.
Noirrit Kiran Chandra et al.
The availability of electronic health records (EHR) has opened opportunities to supplement increasingly expensive and difficult to carry out randomized controlled trials (RCT) with evidence from readily available real world data. In this ar...
Doudou Zhou,Yufeng Zhang,Aaron Sonabend-W et al.
Doudou Zhou et al.
Evidence-based or data-driven dynamic treatment regimes are essential for personalized medicine, which can benefit from offline reinforcement learning (RL). Although massive healthcare data are available across medical institutions, they ar...
John Kornak,Karl Young,Eric Friedman et al.
John Kornak et al.
Bayesian image analysis has been instrumental for over 40 years in addressing challenges such as image noise reduction, de-blurring, feature enhancement, and object detection. Despite its success, modeling spatial dependencies inherent to t...
Xinyu Tian,Xiaotong Shen
Xinyu Tian
Reliable machine learning and statistical analysis rely on diverse, well-distributed training data. However, real-world datasets are often limited in size and exhibit underrepresentation across key subpopulations, leading to biased predicti...
Boosting AI-Generated Biomedical Images with Confidence through Advanced Statistical Inference [0.03%]
基于高级统计推理提升AI生成的生物医学图像的信心度
Zhiling Gu,Shan Yu,Guannan Wang et al.
Zhiling Gu et al.
Generative artificial intelligence (AI) has transformed the biomedical imaging field through image synthesis, addressing challenges of data availability, privacy, and diversity in biomedical research. This paper proposes a novel nonparametr...