Regression analysis of incomplete data from event history studies with the proportional rates model [0.03%]
比例率模型下的事件史研究中不完全数据的回归分析
Guanglei Yu,Liang Zhu,Jianguo Sun et al.
Guanglei Yu et al.
This paper discusses regression analysis of a type of incomplete mixed data arising from event history studies with the proportional rates model. By mixed data, we mean that each study subject may be observed continuously during the whole s...
Quantile regression in linear mixed models: a stochastic approximation EM approach [0.03%]
线性混合模型中的分位数回归：一种随机逼近EM方法
Christian E Galarza,Victor H Lachos,Dipankar Bandyopadhyay
Christian E Galarza
This paper develops a likelihood-based approach to analyze quantile regression (QR) models for continuous longitudinal data via the asymmetric Laplace distribution (ALD). Compared to the conventional mean regression approach, QR can charact...
LCN: a random graph mixture model for community detection in functional brain networks [0.03%]
基于功能脑网络社区检测的随机图混合模型(LCN)
Christopher Bryant,Hongtu Zhu,Mihye Ahn et al.
Christopher Bryant et al.
The aim of this article is to develop a Bayesian random graph mixture model (RGMM) to detect the latent class network (LCN) structure of brain connectivity networks and estimate the parameters governing this structure. The use of conjugate ...
A Bayesian approach to identify genes and gene-level SNP aggregates in a genetic analysis of cancer data [0.03%]
在癌症数据遗传分析中识别基因及基因水平SNP集合的贝叶斯方法
Francesco C Stingo,Michael D Swartz,Marina Vannucci
Francesco C Stingo
Complex diseases, such as cancer, arise from complex etiologies consisting of multiple single-nucleotide polymorphisms (SNPs), each contributing a small amount to the overall risk of disease. Thus, many researchers have gone beyond single-S...
Semiparametric Random Effects Models for Longitudinal Data with Informative Observation Times [0.03%]
具有信息观察时间的纵向数据的半参数随机效应模型
Yang Li,Yanqing Sun
Yang Li
Longitudinal data frequently arise in many fields such as medical follow-up studies focusing on specific longitudinal responses. In such situations, the responses are recorded only at discrete observation times. Most existing approaches for...
Linear mixed models for multiple outcomes using extended multivariate skew-t distributions [0.03%]
多重目标的线性混合模型使用扩展多变量skew-t分布
Binbing Yu,A James O&#x;Malley,Pulak Ghosh
Binbing Yu
Multivariate outcomes with heavy skewness and thick tails often arise from clustered experiments or longitudinal studies. Linear mixed models with multivariate skew-t (MST) distributions for the random effects and the error terms is a popul...
Genome-wide association test of multiple continuous traits using imputed SNPs [0.03%]
基于完整基因型的多重性状全基因组关联分析
Baolin Wu,James S Pankow
Baolin Wu
More and more large cohort studies have conducted or are conducting genome-wide association studies (GWAS) to reveal the genetic components of many complex human diseases. These large cohort studies often collected a broad array of correlat...
Single-gene negative binomial regression models for RNA-Seq data with higher-order asymptotic inference [0.03%]
用于RNA序数据的单基因负二项回归模型及其高阶渐近推断方法
Yanming Di
Yanming Di
We consider negative binomial (NB) regression models for RNA-Seq read counts and investigate an approach where such NB regression models are fitted to individual genes separately and, in particular, the NB dispersion parameter is estimated ...
Kun Chen
Kun Chen
Reduced-rank methods are very popular in high-dimensional multivariate analysis for conducting simultaneous dimension reduction and model estimation. However, the commonly-used reduced-rank methods are not robust, as the underlying reduced-...
Chun Wang,Ming-Hui Chen,Elizabeth Schifano et al.
Chun Wang et al.
Big data are data on a massive scale in terms of volume, intensity, and complexity that exceed the capacity of standard analytic tools. They present opportunities as well as challenges to statisticians. The role of computational statisticia...