Yueying Wang,Guannan Wang,Brandon Klinedinst et al.
Yueying Wang et al.
The use of complex three-dimensional (3D) objects is growing in various applications as data collection techniques continue to evolve. Identifying and locating significant effects within these objects is essential for making informed decisi...
Fei Jiang,Lu Tian,Jian Kang et al.
Fei Jiang et al.
Classical regression generally assumes that all subjects follow a common model with the same set of parameters. With ever advancing capabilities of modern technologies to collect more subjects and more covariates, it has become increasingly...
SEMIPARAMETRIC REVERSED MEAN MODEL FOR RECURRENT EVENT PROCESS WITH INFORMATIVE TERMINAL EVENT [0.03%]
具有信息终止事件的复发事件过程的半参数逆向平均模型
Wen Su,Li Liu,Guosheng Yin et al.
Wen Su et al.
We study semiparametric regression for a recurrent event process with an informative terminal event, where observations are taken only at discrete time points, rather than continuously over time. To account for the effect of a terminal even...
NONPARAMETRIC ESTIMATION AND TESTING FOR PANEL COUNT DATA WITH INFORMATIVE TERMINAL EVENT [0.03%]
具有终止事件的板块计数数据的非参数估计和检验
Xiangbin Hu,Li Liu,Ying Zhang et al.
Xiangbin Hu et al.
Informative terminal events often occur in the long term recurrent event follow-up studies. To reflect their effects on recurrent event processes explicitly, we propose a reversed nonparametric mean model for panel count data with a termina...
A New Paradigm for Generative Adversarial Networks based on Randomized Decision Rules [0.03%]
基于随机决策规则的生成对抗网络的新范式
Sehwan Kim,Qifan Song,Faming Liang
Sehwan Kim
The Generative Adversarial Network (GAN) was recently introduced in the literature as a novel machine learning method for training generative models. It has many applications in statistics such as nonparametric clustering and nonparametric ...
Statistical Inference for High-Dimensional Vector Autoregression with Measurement Error [0.03%]
含测量误差的高维向量自回归模型的统计推断
Xiang Lyu,Jian Kang,Lexin Li
Xiang Lyu
High-dimensional vector autoregression with measurement error is frequently encountered in a large variety of scientific and business applications. In this article, we study statistical inference of the transition matrix under this model. W...
Fixed-Domain Asymptotics Under Vecchia's Approximation of Spatial Process Likelihoods [0.03%]
Vecchia近似下的固定域渐近性质
Lu Zhang,Wenpin Tang,Sudipto Banerjee
Lu Zhang
Statistical modeling for massive spatial data sets has generated a substantial literature on scalable spatial processes based upon Vecchia's approximation. Vecchia's approximation for Gaussian process models enables fast evaluation of the l...
PARTIALLY FUNCTIONAL LINEAR QUANTILE REGRESSION WITH MEASUREMENT ERRORS [0.03%]
带测量误差的条件部分功能线性分位数回归模型估计方法研究
Mengli Zhang,Lan Xue,Carmen D Tekwe et al.
Mengli Zhang et al.
Ignoring measurement errors in conventional regression analyses can lead to biased estimation and inference results. Reducing such bias is challenging when the error-prone covariate is a functional curve. In this paper, we propose a new cor...
Jaffer M Zaidi,Tyler J VanderWeele
Jaffer M Zaidi
The sufficient cause model is extended from binary to categorical and ordinal outcomes to formalize the concept of sufficient cause interaction and synergism in this setting. This extension allows us to derive counterfactual and empirical c...
ESTIMATION FOR EXTREME CONDITIONAL QUANTILES OF FUNCTIONAL QUANTILE REGRESSION [0.03%]
函数型分位数回归的极端条件下的分位点估计方法研究
Hanbing Zhu,Riquan Zhang,Yehua Li et al.
Hanbing Zhu et al.
Quantile regression as an alternative to modeling the conditional mean function provides a comprehensive picture of the relationship between a response and covariates. It is particularly attractive in applications focused on the upper or lo...