Peiyao Wang,Quefeng Li,Dinggang Shen et al.
Peiyao Wang et al.
In modern scientific research, data heterogeneity is commonly observed owing to the abundance of complex data. We propose a factor regression model for data with heterogeneous subpopulations. The proposed model can be represented as a decom...
Use of random integration to test equality of high dimensional covariance matrices [0.03%]
随机积分在检验高维协方差阵的相等性中的应用
Yunlu Jiang,Canhong Wen,Yukang Jiang et al.
Yunlu Jiang et al.
Testing the equality of two covariance matrices is a fundamental problem in statistics, and especially challenging when the data are high-dimensional. Through a novel use of random integration, we can test the equality of high-dimensional c...
Globally Adaptive Longitudinal Quantile Regression with High Dimensional Compositional Covariates [0.03%]
高维组成协变量的全局适应纵向分位数回归
Huijuan Ma,Qi Zheng,Zhumin Zhang et al.
Huijuan Ma et al.
In this work, we propose a longitudinal quantile regression framework that enables a robust characterization of heterogeneous covariate-response associations in the presence of high-dimensional compositional covariates and repeated measurem...
An Efficient Greedy Search Algorithm for High-dimensional Linear Discriminant Analysis [0.03%]
高维线性判别分析的高效贪婪搜索算法
Hannan Yang,D Y Lin,Quefeng Li
Hannan Yang
High-dimensional classification is an important statistical problem that has applications in many areas. One widely used classifier is the Linear Discriminant Analysis (LDA). In recent years, many regularized LDA classifiers have been propo...
Marginal Bayesian Posterior Inference using Recurrent Neural Networks with Application to Sequential Models [0.03%]
利用递归神经网络进行边缘后验推理及其在序列模型中的应用
Thayer Fisher,Alex Luedtke,Marco Carone et al.
Thayer Fisher et al.
In Bayesian data analysis, it is often important to evaluate quantiles of the posterior distribution of a parameter of interest (e.g., to form posterior intervals). In multi-dimensional problems, when non-conjugate priors are used, this is ...
ACCURATE CONSTRUCTION OF LONG RANGE HAPLOTYPE IN UNRELATED INDIVIDUALS [0.03%]
在非亲缘个体中精确构建长程基因型hapsnomes
Nicholas A Johnson,Stephanie J London,Isabelle Romieu et al.
Nicholas A Johnson et al.
Haplotype, or the sequence of alleles along a single chromosome, has important applications in phenotype-genotype association studies, as well as in population genetics analyses. Because haplotype cannot be experimentally assayed in diploid...
Sieve estimation of a class of partially linear transformation models with interval-censored competing risks data [0.03%]
区间删失竞争风险数据下部分线性变换模型的筛选估计
Xuewen Lu,Yan Wang,Dipankar Bandyopadhyay et al.
Xuewen Lu et al.
In this paper, we consider a class of partially linear transformation models with interval-censored competing risks data. Under a semiparametric generalized odds rate specification for the cause-specific cumulative incidence function, we ob...
PENALIZED REGRESSION FOR MULTIPLE TYPES OF MANY FEATURES WITH MISSING DATA [0.03%]
具有缺失值的多重类型大数据集的惩罚回归方法研究
Kin Yau Wong,Donglin Zeng,D Y Lin
Kin Yau Wong
Recent technological advances have made it possible to measure multiple types of many features in biomedical studies. However, some data types or features may not be measured for all study subjects because of cost or other constraints. We u...
Interval estimation for operating characteristic of continuous biomarkers with controlled sensitivity or specificity [0.03%]
控制灵敏度或特异性的连续生物标志物的Operating Characteristic性质的区间估计
Yijian Huang,Isaac Parakati,Dattatraya H Patil et al.
Yijian Huang et al.
The receiver operating characteristic (ROC) curve provides a comprehensive performance assessment of a continuous biomarker over the full threshold spectrum. Nevertheless, a medical test often dictates to operate at a certain high level of ...
An Online Projection Estimator for Nonparametric Regression in Reproducing Kernel Hilbert Spaces [0.03%]
基于再生核希尔伯特空间的非参数回归在线投影估计器
Tianyu Zhang,Noah Simon
Tianyu Zhang
The goal of nonparametric regression is to recover an underlying regression function from noisy observations, under the assumption that the regression function belongs to a prespecified infinite-dimensional function space. In the online set...