论文部分内容阅读
During the past decade,shrinkage priors have received much attention in Bayesian analysis of high-dimensional data.
NEARLY OPTIMAL BAYESIAN SHRINKAGE FOR HIGH DIMENSIONAL REGRESSION
【摘 要】
：
During the past decade,shrinkage priors have received much attention in Bayesian analysis of high-dimensional data.
【机 构】
：
Purdue University,IN 47906,USA University of Flor
【出 处】
：
上海交通大学
【发表日期】
：
2016年4期
其他文献
With rapid accumulation of public gene expression data,meta-analysis to combine multiple transcriptomic studies for biomarker detection improves detection accuracy and robustness.
A solid tumor sample consists of mixed populations of cells,normal cells and cancer cells.Heterogeneity among cancer cells is further explained by multiple distinct tumor subclones.
In oncology clinical development,randomized phase Ⅱ studies have been increasingly used in recent years for a variety of reasons.
Nonparametric Kernel-Based Estimation for Time-Varying Coefficient Models with Multivariate Integrat
This paper studies nonlinear cointegration models in which the structural coefficients may evolve smoothly over time,and considers time-varying coefficient functions estimated by nonparametric kernel
Large administrative data from healthcare claims and electronic health records provide significant opportunities for health services,drug safety,and other health research,due to large sample sizes and
Continuous-time Markov chains are widely used as models for stochastic counting processes in molecular evolution.
In many applications where both predictors and responses are collected across geographical regions over time,the impacts of predictors to responses are often not static but time-varying.
Semi-competing risks data arise when two types of events,non-terminal and terminal,may be observed.When the terminal event occurs first,it censors the non-terminal event.
Logistic Regression Augmented Community Detection with Application in Identifying Autism-Related Gen
When searching for gene pathways leading to specific disease outcomes,we propose to take advantage of additional information on gene characteristics to differentiate genes of interests from irrelevant