Yaohua Rong,Sihai Dave Zhao,Ji Zhu et al.
Yaohua Rong et al.
A key step in pharmacogenomic studies is the development of accurate prediction models for drug response based on individuals' genomic information. Recent interest has centered on semiparametric models based on kernel machine regression, wh...
Bayesian modeling and uncertainty quantification for descriptive social networks [0.03%]
描述性社交网络的贝叶斯模型及不确定性量化
Thomas Nemmers,Anjana Narayan,Sudipto Banerjee
Thomas Nemmers
This article presents a simple and easily implementable Bayesian approach to model and quantify uncertainty in small descriptive social networks. While statistical methods for analyzing networks have seen burgeoning activity over the last d...
Michelle DeVeaux,Michael J Kane,Daniel Zelterman
Michelle DeVeaux
We introduce a discrete distribution suggested by curtailed sampling rules common in early-stage clinical trials. We derive the distribution of the smallest number of independent and identically distributed Bernoulli trials needed to observ...
Doubly regularized estimation and selection in linear mixed-effects models for high-dimensional longitudinal data [0.03%]
高维纵向数据分析中线性混合效应模型的双重正则化估计与变量选择方法研究
Yun Li,Sijian Wang,Peter X-K Song et al.
Yun Li et al.
The linear mixed-effects model (LMM) is widely used in the analysis of clustered or longitudinal data. This paper aims to address analytic challenges arising from estimation and selection in the application of the LMM to high-dimensional lo...
Janet S Kim,Arnab Maity,Ana-Maria Staicu
Janet S Kim
We propose a flexible regression model to study the association between a functional response and multiple functional covariates that are observed on the same domain. Specifically, we relate the mean of the current response to current value...
Double Sparsity Kernel Learning with Automatic Variable Selection and Data Extraction [0.03%]
具备自动变量选择和数据提取功能的双稀疏核学习方法
Jingxiang Chen,Chong Zhang,Michael R Kosorok et al.
Jingxiang Chen et al.
Learning in the Reproducing Kernel Hilbert Space (RKHS) has been widely used in many scientific disciplines. Because a RKHS can be very flexible, it is common to impose a regularization term in the optimization to prevent overfitting. Stand...
A propensity score approach to estimating child restraint effectiveness in preventing mortality [0.03%]
一种用于估计儿童约束系统在预防死亡方面效果的倾向值方法
Michael R Elliott,Dennis R Durbin,Flaura K Winston
Michael R Elliott
Confounding between the child's restraint use and driver behavior can bias restraint effectiveness estimates away from the null if survivable crashes are more common in certain restraint types. Analyzing only fatal crashes may introduce sel...
Two-stage design for phase II oncology trials with relaxed futility stopping [0.03%]
具有宽松无效停止规则的II期肿瘤试验的两阶段设计
Anastasia Ivanova,Allison M Deal
Anastasia Ivanova
Many oncology phase II trials are single arm studies designed to screen novel treatments based on efficacy outcome. Efficacy is often assessed as an ordinal variable based on a level of response of solid tumors with four categories: complet...
Esra Kürüm,John Hughes,Runze Li et al.
Esra Kürüm et al.
We propose a copula-based joint modeling framework for mixed longitudinal responses. Our approach permits all model parameters to vary with time, and thus will enable researchers to reveal dynamic response-predictor relationships and respon...
Bayesian analysis of stochastic volatility-in-mean model with leverage and asymmetrically heavy-tailed error using generalized hyperbolic skew Student's t-distribution [0.03%]
带有杠杆和非对称厚尾误差的随机波动率含均值模型的广义双曲有偏学生氏t分布的贝叶斯分析
William L Leão,Carlos A Abanto-Valle,Ming-Hui Chen
William L Leão
A stochastic volatility-in-mean model with correlated errors using the generalized hyperbolic skew Student-t (GHST) distribution provides a robust alternative to the parameter estimation for daily stock returns in the absence of normality. ...