Mini-batch Estimation for Deep Cox Models: Statistical Foundations and Practical Guidance [0.03%]
深度Cox模型的小批量估计：统计基础与实践指南
Lang Zeng,Weijing Tang,Zhao Ren et al.
Lang Zeng et al.
The stochastic gradient descent (SGD) algorithm has been widely used to optimize deep Cox neural network (Cox-NN) by updating model parameters using mini-batches of data. We show that SGD aims to optimize the average of mini-batch partial-l...
Jiawei Li,Jonathan Hunter Huggins
Jiawei Li
Assessing how well a Bayesian model generalizes to unobserved data is essential, yet existing general-purpose model checks are either not properly calibrated (as in posterior predictive checks) or fail to be sufficiently general for practic...
Yingqi Gao,Wenlu Xu,Jin J Zhou et al.
Yingqi Gao et al.
As data marketplaces become increasingly central to the digital economy, it is crucial to design efficient pricing mechanisms that optimize revenue while ensuring fair and adaptive pricing. We introduce the Maximum Auction-to-Posted Price (...
Sparse Gaussianized Canonical Correlation Analysis with Applications to Portfolio Analysis [0.03%]
稀疏高斯化典型相关分析及其在投资组合分析中的应用
Di He,Hui Zou
Di He
Canonical correlation analysis (CCA) is an important statistical technique that explores the linear relationships between two sets of variables. In this article, we propose a new generalization of CCA named sparse Gaussianized CCA (SGCCA) f...
Theory for Identification and Inference with Synthetic Controls: A Proximal Causal Inference Framework [0.03%]
合成控制法的识别与推断理论：一个近因因果推理框架
Xu Shi,Kendrick Qijun Li,Myeonghun Yu et al.
Xu Shi et al.
Synthetic control (SC) methods are commonly used to estimate the treatment effect on a single treated unit in panel data settings. An SC is a weighted average of control units built to match the treated unit, with weights typically estimate...
Adaptive Debiased Lasso in High-Dimensional Generalized Linear Models with Streaming Data [0.03%]
高维广义线性模型中带数据流的自适应消偏LASSO方法研究
Ruijian Han,Lan Luo,Yuanhang Luo et al.
Ruijian Han et al.
Online statistical inference facilitates real-time analysis of sequentially collected data, making it different from traditional methods that rely on static datasets. This article introduces a novel approach to online inference in high-dime...
Constructing Genetic Risk Scores: Robust Bayesian Approach through Projected Summary Statistics and Flexible Shrinkage [0.03%]
基于投影汇总统计和灵活收缩的稳健贝叶斯基因风险评分构建方法
Yuzheng Dun,Nilanjan Chatterjee,Jin Jin et al.
Yuzheng Dun et al.
Polygenic risk scores (PRS) developed from genome-wide association studies (GWAS) can be used for risk stratification by quantifying the genetic contribution to disease, and many clinical applications have been proposed. Bayesian methods ar...
Localized Sparse Principal Component Analysis of Multivariate Time Series in the Frequency Domain [0.03%]
基于频率的多变量时间序列局部稀疏主成分分析
Jamshid Namdari,Amita Manatunga,Fabio Ferrarelli et al.
Jamshid Namdari et al.
Principal component analysis has been a main tool in multivariate analysis for estimating a low dimensional linear subspace that explains most of the variability in the data. However, in high-dimensional regimes, naive estimates of the prin...
An AI-powered Bayesian Generative Modeling Approach for Causal Inference in Observational Studies [0.03%]
一种基于AI的贝叶斯生成模型方法，用于观察性研究中的因果推断
Qiao Liu,Wing Hung Wong
Qiao Liu
Causal inference in observational studies with high-dimensional covariates presents significant challenges. We introduce CausalBGM, an AI-powered Bayesian generative modeling approach that captures the causal relationship among covariates, ...
Instrumental Variable Estimation of Marginal Structural Mean Models for Time-Varying Treatment [0.03%]
时间变异治疗的边际结构均值模型的工具变量估计法
Haben Michael,Yifan Cui,Scott A Lorch et al.
Haben Michael et al.
Robins introduced Marginal Structural Models (MSMs), a general class of counterfactual models for the joint effects of time-varying treatment regimes in complex longitudinal studies subject to time-varying confounding. In his work, identifi...