Fadoua Balabdaoui,Hanna Jankowski,Marios Pavlides et al.
Fadoua Balabdaoui et al.
We establish limit theory for the Grenander estimator of a monotone density near zero. In particular we consider the situation when the true density f(0) is unbounded at zero, with different rates of growth to infinity. In the course of our...
An Approach to Constructing Nested Space-Filling Designs for Multi-Fidelity Computer Experiments [0.03%]
构造多 fidelity 计算实验的嵌套空间填充设计的方法
Ben Haaland,Peter Z G Qian
Ben Haaland
Multi-fidelity computer experiments are widely used in many engineering and scientific fields. Nested space-filling designs (NSFDs) are suitable for conducting such experiments. Two classes of NSFDs are currently available. One class is bas...
Surface Estimation, Variable Selection, and the Nonparametric Oracle Property [0.03%]
表面估计，变量选择与非参数oracle性质
Curtis B Storlie,Howard D Bondell,Brian J Reich et al.
Curtis B Storlie et al.
Variable selection for multivariate nonparametric regression is an important, yet challenging, problem due, in part, to the infinite dimensionality of the function space. An ideal selection procedure should be automatic, stable, easy to use...
Jianqing Fan,Jinchi Lv
Jianqing Fan
High dimensional statistical problems arise from diverse fields of scientific research and technological development. Variable selection plays a pivotal role in contemporary statistical learning and scientific discoveries. The traditional i...
Statistical Modelling of Brain Morphological Measures Within Family Pedigrees [0.03%]
基于家族系谱的脑形态测度的统计模型
Hongtu Zhu,Yimei Li,Niansheng Tang et al.
Hongtu Zhu et al.
Large, family-based imaging studies can provide a better understanding of the interactions of environmental and genetic influences on brain structure and function. The interpretation of imaging data from large family studies, however, has b...
Jianxin Yin,Zhi Geng,Runze Li et al.
Jianxin Yin et al.
There has been considerable attention on estimation of conditional variance function in the literature. We propose here a nonparametric model for conditional covariance matrix. A kernel estimator is developed accordingly, its asymptotic bia...
An Information Matrix Prior for Bayesian Analysis in Generalized Linear Models with High Dimensional Data [0.03%]
高维数据广义线性模型的贝叶斯分析的信息矩阵先验
Mayetri Gupta,Joseph G Ibrahim
Mayetri Gupta
An important challenge in analyzing high dimensional data in regression settings is that of facing a situation in which the number of covariates p in the model greatly exceeds the sample size n (sometimes termed the "p > n" problem). In thi...
A GENERAL ASYMPTOTIC THEORY FOR MAXIMUM LIKELIHOOD ESTIMATION IN SEMIPARAMETRIC REGRESSION MODELS WITH CENSORED DATA [0.03%]
乘积极限估计下的半参数回归模型的经验过程及其在区间删失数据场合的渐近理论研究
Donglin Zeng,D Y Lin
Donglin Zeng
We establish a general asymptotic theory for nonparametric maximum likelihood estimation in semiparametric regression models with right censored data. We identify a set of regularity conditions under which the nonparametric maximum likeliho...
SEMIPARAMETRIC REGRESSION WITH TIME-DEPENDENT COEFFICIENTS FOR FAILURE TIME DATA ANALYSIS [0.03%]
具有时间依赖系数的失败风险数据半参数回归分析方法研究
Zhangsheng Yu,Xihong Lin
Zhangsheng Yu
We propose a working independent profile likelihood method for the semiparametric time-varying coefficient model with correlation. Kernel likelihood is used to estimate time-varying coefficient. Profile likelihood for the parametric coeffic...
EFFICIENT ESTIMATION FOR AN ACCELERATED FAILURE TIME MODEL WITH A CURE FRACTION [0.03%]
带有治愈比例的加速失效时间模型的高效估计方法
Wenbin Lu
Wenbin Lu
We study the accelerated failure time model with a cure fraction via kernel-based nonparametric maximum likelihood estimation. An EM algorithm is developed to calculate the estimates for both the regression parameters and the unknown error ...