Intrinsic Regression Models for Positive-Definite Matrices With Applications to Diffusion Tensor Imaging [0.03%]
各向同性回归模型及其在扩散张量成像中的应用
Hongtu Zhu,Yasheng Chen,Joseph G Ibrahim et al.
Hongtu Zhu et al.
The aim of this paper is to develop an intrinsic regression model for the analysis of positive-definite matrices as responses in a Riemannian manifold and their association with a set of covariates, such as age and gender, in a Euclidean sp...
A Class of Transformed Mean Residual Life Models With Censored Survival Data [0.03%]
具有一类变换的平均剩余寿命模型及其在删失生存数据中的应用
Liuquan Sun,Zhigang Zhang
Liuquan Sun
The mean residual life function is an attractive alternative to the survival function or the hazard function of a survival time in practice. It provides the remaining life expectancy of a subject surviving up to time t. In this study, we pr...
Joint Models for the Association of Longitudinal Binary and Continuous Processes With Application to a Smoking Cessation Trial [0.03%]
纵向二元和连续过程关联的联合模型及其在戒烟试验中的应用
Xuefeng Liu,Michael J Daniels,Bess Marcus
Xuefeng Liu
Joint models for the association of a longitudinal binary and a longitudinal continuous process are proposed for situations in which their association is of direct interest. The models are parameterized such that the dependence between the ...
Edsel A Peña,Elizabeth H Slate
Edsel A Peña
An easy-to-implement global procedure for testing the four assumptions of the linear model is proposed. The test can be viewed as a Neyman smooth test and it only relies on the standardized residual vector. If the global procedure indicates...
Joint modeling of self-rated health and changes in physical functioning [0.03%]
自我报告健康状况及身体功能变化的联合建模
Rebecca A Hubbard,Lurdes Y T Inoue,Paula Diehr
Rebecca A Hubbard
Self-rated health is an important indicator of future morbidity and mortality. Past research has indicated that self-rated health is related to both levels of and changes in physical functioning. However, no previous study has jointly model...
Junhui Wang,Xiaotong Shen,Wei Pan
Junhui Wang
Hierarchical classification is critical to knowledge management and exploration, as in gene function prediction and document categorization. In hierarchical classification, an input is classified according to a structured hierarchy. In a si...
Carolyn M Rutter,Diana L Miglioretti,James E Savarino
Carolyn M Rutter
Microsimulation models that describe disease processes synthesize information from multiple sources and can be used to estimate the effects of screening and treatment on cancer incidence and mortality at a population level. These models are...
Nonparametric Signal Extraction and Measurement Error in the Analysis of Electroencephalographic Activity During Sleep [0.03%]
非参数信号抽取及测量误差在睡眠期间脑电图活动分析中的作用
Ciprian M Crainiceanu,Brian S Caffo,Chong-Zhi Di et al.
Ciprian M Crainiceanu et al.
We introduce methods for signal and associated variability estimation based on hierarchical nonparametric smoothing with application to the Sleep Heart Health Study (SHHS). SHHS is the largest electroencephalographic (EEG) collection of sle...
Variable Selection in Nonparametric Varying-Coefficient Models for Analysis of Repeated Measurements [0.03%]
重复测量分析的非参数变系数模型变量选择
Lifeng Wang,Hongzhe Li,Jianhua Z Huang
Lifeng Wang
Nonparametric varying-coefficient models are commonly used for analysis of data measured repeatedly over time, including longitudinal and functional responses data. While many procedures have been developed for estimating the varying-coeffi...
Hua Liang,Runze Li
Hua Liang
This article focuses on variable selection for partially linear models when the covariates are measured with additive errors. We propose two classes of variable selection procedures, penalized least squares and penalized quantile regression...