Xiaoxi Shen,Chang Jiang,Lyudamila Sakhanenko et al.
Xiaoxi Shen et al.
Neural networks have become one of the most popularly used methods in machine learning and artificial intelligence. Due to the universal approximation theorem (Hornik et al., 1989), a neural network with one hidden layer can approximate any...
Permutation-Based Inference for Function-on-Scalar Regression With an Application in PET Brain Imaging [0.03%]
基于排列的函数回归推论及其在脑PET成像中的应用
Denise Shieh,R Todd Ogden
Denise Shieh
The density of various proteins throughout the human brain can be studied through the use of positron emission tomography (PET) imaging. We report here on data from a study of serotonin transporter (5-HTT) binding. While PET imaging data an...
A Semiparametric Bayesian Approach to Epidemics, with Application to the Spread of the Coronavirus MERS in South Korea in 2015 [0.03%]
半参数贝叶斯流行病传播分析方法及中东呼吸综合征疫情实证研究
Michael Schweinberger,Rashmi P Bomiriya,Sergii Babkin
Michael Schweinberger
We consider incomplete observations of stochastic processes governing the spread of infectious diseases through finite populations by way of contact. We propose a flexible semiparametric modeling framework with at least three advantages. Fi...
Efficient Semiparametric Regression for Longitudinal Data with Regularized Estimation of Error Covariance Function [0.03%]
具有正则化残差协方差函数估计的纵向数据有效半参数回归
Shengji Jia,Chunming Zhang,Hulin Wu
Shengji Jia
Improving estimation efficiency for regression coefficients is an important issue in the analysis of longitudinal data, which involves estimating the covariance matrix of errors. But challenges arise in estimating the covariance matrix of l...
Dewei Wang,Xichen Mou,Xiang Li et al.
Dewei Wang et al.
We propose local polynomial estimators for the conditional mean of a continuous response when only pooled response data are collected under different pooling designs. Asymptotic properties of these estimators are investigated and compared. ...
Variable selection for partially linear proportional hazards model with covariate measurement error [0.03%]
具有协变量测量误差的部分线性比例风险模型的变元选择问题
Xiao Song,Li Wang,Shuangge Ma et al.
Xiao Song et al.
In survival analysis, we may encounter the following three problems: nonlinear covariate effect, variable selection and measurement error. Existing studies only address one or two of these problems. The goal of this study is to fill the kno...
Estimators based on Unconventional Likelihoods with Nonignorable Missing Data and its Application to a Children's Mental Health Study [0.03%]
非传统似然估计方法在儿童心理健康研究中的应用及不可忽略缺失数据问题的解决
Jiwei Zhao,Chi Chen
Jiwei Zhao
Nonignorable missing-data is common in studies where the outcome is relevant to the subject's behavior. Ibrahim et al. (2001) fitted a logistic regression for a binary outcome subject to nonignorable missing data, and they proposed to repla...
Nonparametric tests for transition probabilities in nonhomogeneous Markov processes [0.03%]
非齐次马尔可夫过程中过渡概率的非参数检验方法研究
Giorgos Bakoyannis
Giorgos Bakoyannis
This paper proposes nonparametric two-sample tests for the direct comparison of the probabilities of a particular transition between states of a continuous time non-homogeneous Markov process with a finite state space. The proposed tests ar...
Reducing Bias for Maximum Approximate Conditional Likelihood Estimator with General Missing Data Mechanism [0.03%]
一般缺失数据机制下的近似条件似然估计的偏差修正
Jiwei Zhao
Jiwei Zhao
In missing data analysis, the assumption of the missing data mechanism is crucial. Under different assumptions, different statistical methods have to be developed accordingly; however, in reality this kind of assumption is usually unverifia...
Dehan Kong,Ana-Maria Staicu,Arnab Maity
Dehan Kong
We extend four tests common in classical regression - Wald, score, likelihood ratio and F tests - to functional linear regression, for testing the null hypothesis, that there is no association between a scalar response and a functional cova...