Estimation of a non-parametric variable importance measure of a continuous exposure [0.03%]
连续暴露的非参数变量重要性度量估计方法
Antoine Chambaz,Pierre Neuvial,Mark J van der Laan
Antoine Chambaz
We define a new measure of variable importance of an exposure on a continuous outcome, accounting for potential confounders. The exposure features a reference level x(0) with positive mass and a continuum of other levels. For the purpose of...
Jiawei Bai,Jeff Goldsmith,Brian Caffo et al.
Jiawei Bai et al.
Recent technological advances provide researchers with a way of gathering real-time information on an individual's movement through the use of wearable devices that record acceleration. In this paper, we propose a method for identifying act...
Causal inference in longitudinal studies with history-restricted marginal structural models [0.03%]
基于历史限制边缘结构模型的纵向研究中的因果推断
Romain Neugebauer,Mark J van der Laan,Marshall M Joffe et al.
Romain Neugebauer et al.
A new class of Marginal Structural Models (MSMs), History-Restricted MSMs (HRMSMs), was recently introduced for longitudinal data for the purpose of defining causal parameters which may often be better suited for public health research or a...
Estimation via corrected scores in general semiparametric regression models with error-prone covariates [0.03%]
带误差的协变量下一般半参数回归模型的经验似然推断
Arnab Maity,Tatiyana V Apanasovich
Arnab Maity
This paper considers the problem of estimation in a general semiparametric regression model when error-prone covariates are modeled parametrically while covariates measured without error are modeled nonparametrically. To account for the eff...
Structured penalties for functional linear models-partially empirical eigenvectors for regression [0.03%]
具有结构化惩罚的功效线性模型-回归的局部经验证本征向量
Timothy W Randolph,Jaroslaw Harezlak,Ziding Feng
Timothy W Randolph
One of the challenges with functional data is incorporating geometric structure, or local correlation, into the analysis. This structure is inherent in the output from an increasing number of biomedical technologies, and a functional linear...
Aad van der Vaart,Jon A Wellner
Aad van der Vaart
We derive an upper bound for the mean of the supremum of the empirical process indexed by a class of functions that are known to have variance bounded by a small constant δ. The bound is expressed in the uniform entropy integral of the cla...
Jeff Goldsmith,Matt P Wand,Ciprian Crainiceanu
Jeff Goldsmith
We introduce variational Bayes methods for fast approximate inference in functional regression analysis. Both the standard cross-sectional and the increasingly common longitudinal settings are treated. The methodology allows Bayesian functi...
Sonja Greven,Ciprian Crainiceanu,Brian Caffo et al.
Sonja Greven et al.
We introduce models for the analysis of functional data observed at multiple time points. The dynamic behavior of functional data is decomposed into a time-dependent population average, baseline (or static) subject-specific variability, lon...
Penalized model-based clustering with unconstrained covariance matrices [0.03%]
具有非限制性协方差矩阵的惩罚模型聚类方法
Hui Zhou,Wei Pan,Xiaotong Shen
Hui Zhou
Clustering is one of the most useful tools for high-dimensional analysis, e.g., for microarray data. It becomes challenging in presence of a large number of noise variables, which may mask underlying clustering structures. Therefore, noise ...
Hanna K Jankowski,Jon A Wellner
Hanna K Jankowski
We study and compare three estimators of a discrete monotone distribution: (a) the (raw) empirical estimator; (b) the "method of rearrangements" estimator; and (c) the maximum likelihood estimator. We show that the maximum likelihood estima...