Fast tensorial JADE [0.03%]
快速张量JADE算法
Joni Virta,Niko Lietzén,Pauliina Ilmonen et al.
Joni Virta et al.
We propose a novel method for tensorial-independent component analysis. Our approach is based on TJADE and k-JADE, two recently proposed generalizations of the classical JADE algorithm. Our novel method achieves the consistency and the limi...
Hard thresholding regression [0.03%]
硬阈值回归
Qiang Sun,Bai Jiang,Hongtu Zhu et al.
Qiang Sun et al.
In this paper, we propose the hard thresholding regression (HTR) for estimating high-dimensional sparse linear regression models. HTR uses a two-stage convex algorithm to approximate the ℓ 0-penalized regression: The first stage calculates...
Combined multiple testing of multivariate survival times by censored empirical likelihood [0.03%]
多元生存时间的组合多重检验通过删失经验似然法
Judith H Parkinson
Judith H Parkinson
In each study testing the survival experience of one or more populations, one must not only choose an appropriate class of tests, but further an appropriate weight function. As the optimal choice depends on the true shape of the hazard rati...
GMM nonparametric correction methods for logistic regression with error contaminated covariates and partially observed instrumental variables [0.03%]
具有误差污染的协变量和部分观测器械变量的逻辑回归的GMM非参数校正方法
Xiao Song,Ching-Yun Wang
Xiao Song
We consider logistic regression with covariate measurement error. Most existing approaches require certain replicates of the error-contaminated covariates, which may not be available in the data. We propose generalized methods of moments (G...
Regression analysis of longitudinal data with outcome-dependent sampling and informative censoring [0.03%]
具有基于结果取样的纵向数据的回归分析及信息缺失 censorship 机制下的回归分析
Weining Shen,Suyu Liu,Yong Chen et al.
Weining Shen et al.
We consider regression analysis of longitudinal data in the presence of outcome-dependent observation times and informative censoring. Existing approaches commonly require correct specification of the joint distribution of the longitudinal ...
An Additive-Multiplicative Mean Model for Panel Count Data with Dependent Observation and Dropout Processes [0.03%]
具有相关检测和脱落过程的面板计数数据的加法-乘法均值模型
Guanglei Yu,Yang Li,Liang Zhu et al.
Guanglei Yu et al.
This paper discusses regression analysis of panel count data with dependent observation and dropout processes. For the problem, a general mean model is presented that can allow both additive and multiplicative effects of covariates on the u...
Tong Tong Wu,Gang Li,Chengyong Tang
Tong Tong Wu
The linear regression model for right censored data, also known as the accelerated failure time model using the logarithm of survival time as the response variable, is a useful alternative to the Cox proportional hazards model. Empirical li...
Learning from a lot: Empirical Bayes for high-dimensional model-based prediction [0.03%]
化繁为简：基于模型的预测的经验证据方法研究
Mark A van de Wiel,Dennis E Te Beest,Magnus M Münch
Mark A van de Wiel
Empirical Bayes is a versatile approach to "learn from a lot" in two ways: first, from a large number of variables and, second, from a potentially large amount of prior information, for example, stored in public repositories. We review appl...
A quasi-score statistic for homogeneity testing against covariate-varying heterogeneity [0.03%]
一种用于对数线性联立方程组的拟评分同质性检验统计量
David Todem,Wei-Wen Hsu,Jason P Fine
David Todem
In statistical modeling, it is often of interest to evaluate non-negative quantities that capture heterogeneity in the population such as variances, mixing proportions and dispersion parameters. In instances of covariate-dependent heterogen...
Tuning Parameter Selection in Cox Proportional Hazards Model with a Diverging Number of Parameters [0.03%]
Cox比例风险模型中发散参数个数的调谐参数选择问题研究
Ai Ni,Jianwen Cai
Ai Ni
Regularized variable selection is a powerful tool for identifying the true regression model from a large number of candidates by applying penalties to the objective functions. The penalty functions typically involve a tuning parameter that ...