Heteroscedastic nonlinear regression models based on scale mixtures of skew-normal distributions [0.03%]
基于尺度混合的偏斜正态分布的异方差非线性回归模型
Victor H Lachos,Dipankar Bandyopadhyay,Aldo M Garay
Victor H Lachos
An extension of some standard likelihood based procedures to heteroscedastic nonlinear regression models under scale mixtures of skew-normal (SMSN) distributions is developed. We derive a simple EM-type algorithm for iteratively computing m...
Testing for Constant Nonparametric Effects in General Semiparametric Regression Models with Interactions [0.03%]
在一般半参数回归模型中检验恒定的非参数效应及相互作用
Jiawei Wei,Raymond J Carroll,Arnab Maity
Jiawei Wei
We consider the problem of testing for a constant nonparametric effect in a general semi-parametric regression model when there is the potential for interaction between the parametrically and nonparametrically modeled variables. The work wa...
An Integrative Pathway-based Clinical-genomic Model for Cancer Survival Prediction [0.03%]
一种基于整合途径的临床基因组模型用于癌症生存预测
Xi Chen,Lily Wang,Hemant Ishwaran
Xi Chen
Prediction models that use gene expression levels are now being proposed for personalized treatment of cancer, but building accurate models that are easy to interpret remains a challenge. In this paper, we describe an integrative clinical-g...
A consistent local linear estimator of the covariate adjusted correlation coefficient [0.03%]
自变量调整的相关系数的局部线性估计量
Danh V Nguyen,Damla Sentürk
Danh V Nguyen
Consider the correlation between two random variables (X, Y), both not directly observed. One only observes X̃ = φ(1)(U)X + φ(2)(U) and Ỹ = ψ(1)(U)Y + ψ(2)(U), where all four functions {φ(l)(·),ψ(l)(·), l = 1, 2} are unknown/unspe...
R M Pfeiffer,E Petracci
R M Pfeiffer
We present a simple influence function based approach to compute the variances of estimates of absolute risk and functions of absolute risk. We apply this approach to criteria that assess the impact of changes in the risk factor distributio...
Propensity score modelling in observational studies using dimension reduction methods [0.03%]
利用降维方法进行倾向评分模型在观察性研究中的应用
Debashis Ghosh
Debashis Ghosh
Conditional independence assumptions are very important in causal inference modelling as well as in dimension reduction methodologies. These are two very strikingly different statistical literatures, and we study links between the two in th...
Finding Quantitative Trait Loci Genes with Collaborative Targeted Maximum Likelihood Learning [0.03%]
具有合作靶向最大可能学习的定量性状位基因的定位
Hui Wang,Sherri Rose,Mark J van der Laan
Hui Wang
Quantitative trait loci mapping is focused on identifying the positions and effect of genes underlying an an observed trait. We present a collaborative targeted maximum likelihood estimator in a semi-parametric model using a newly proposed ...
Higher Order Inference On A Treatment Effect Under Low Regularity Conditions [0.03%]
低正则性条件下的处理效应的高阶推断
Lingling Li,Eric Tchetgen Tchetgen,Aad van der Vaart et al.
Lingling Li et al.
We describe a novel approach to nonparametric point and interval estimation of a treatment effect in the presence of many continuous confounders. We show the problem can be reduced to that of point and interval estimation of the expected co...
Effect partitioning under interference in two-stage randomized vaccine trials [0.03%]
两阶段随机化疫苗试验中的干扰效应分解
Tyler J Vanderweele,Eric J Tchetgen Tchetgen
Tyler J Vanderweele
In the presence of interference, the exposure of one individual may affect the outcomes of others. We provide new effect partitioning results under interferences that express the overall effect as a sum of (i) the indirect (or spillover) ef...
Nikolay Balov
Nikolay Balov
In this paper we address the problem of learning discrete Bayesian networks from noisy data. Considered is a graphical model based on mixture of Gaussian distributions with categorical mixing structure coming from a discrete Bayesian networ...