Artin Armagan,David B Dunson,Jaeyong Lee
Artin Armagan
We propose a generalized double Pareto prior for Bayesian shrinkage estimation and inferences in linear models. The prior can be obtained via a scale mixture of Laplace or normal distributions, forming a bridge between the Laplace and Norma...
Functional Linear Model with Zero-value Coefficient Function at Sub-regions [0.03%]
在部分区域系数函数为零的分段线性回归模型
Jianhui Zhou,Nae-Yuh Wang,Naisyin Wang
Jianhui Zhou
We propose a shrinkage method to estimate the coefficient function in a functional linear regression model when the value of the coefficient function is zero within certain sub-regions. Besides identifying the null region in which the coeff...
ANALYSIS ON CENSORED QUANTILE RESIDUAL LIFE MODEL VIA SPLINE SMOOTHING [0.03%]
基于样条估计的思想研究删失下的分位数剩余寿命模型
Yanyuan Ma,Ying Wei
Yanyuan Ma
We propose a general class of quantile residual life models, where a specific quantile of the residual life time, conditional on an individual has survived up to time t, is a function of certain covariates with their coefficients varying ov...
VARIABLE SELECTION AND ESTIMATION IN HIGH-DIMENSIONAL VARYING-COEFFICIENT MODELS [0.03%]
高维变系数模型中的变量筛选与估计
Fengrong Wei,Jian Huang,Hongzhe Li
Fengrong Wei
Nonparametric varying coefficient models are useful for studying the time-dependent effects of variables. Many procedures have been developed for estimation and variable selection in such models. However, existing work has focused on the ca...
David B Dunson
David B Dunson
Mixtures provide a useful approach for relaxing parametric assumptions. Discrete mixture models induce clusters, typically with the same cluster allocation for each parameter in multivariate cases. As a more flexible approach that facilitat...
SEMIPARAMETRIC ESTIMATION OF CONDITIONAL HETEROSCEDASTICITY VIA SINGLE-INDEX MODELING [0.03%]
单指数模型的半参数估计及其条件波动应用研究
Liping Zhu,Yuexiao Dong,Runze Li
Liping Zhu
We consider a single-index structure to study heteroscedasticity in regression with high-dimensional predictors. A general class of estimating equations is introduced, the resulting estimators remain consistent even when the structure of th...
CENTER-ADJUSTED INFERENCE FOR A NONPARAMETRIC BAYESIAN RANDOM EFFECT DISTRIBUTION [0.03%]
中心对齐的推断方法在非参数贝叶斯随机效应分布中的应用
Yisheng Li,Peter Müller,Xihong Lin
Yisheng Li
Dirichlet process (DP) priors are a popular choice for semiparametric Bayesian random effect models. The fact that the DP prior implies a non-zero mean for the random effect distribution creates an identifiability problem that complicates t...
Montserrat Fuentes,Brian Reich
Montserrat Fuentes
In this paper we develop a nonparametric multivariate spatial model that avoids specifying a Gaussian distribution for spatial random effects. Our nonparametric model extends the stick-breaking (SB) prior of Sethuraman (1994), which is freq...
Sequential Analysis of the Cox Model under Response Dependent Allocation [0.03%]
基于响应相关分配的Cox模型的序贯分析方法研究
Xiaolong Luo,Gongjun Xu,Zhiliang Ying
Xiaolong Luo
Sellke and Siegmund (1983) developed the Brownian approximation to the Cox partial likelihood score as a process of calendar time, laying the foundation for group sequential analysis of survival studies. We extend their results to cover sit...
ANALYSIS OF MULTIVARIATE FAILURE TIME DATA USING MARGINAL PROPORTIONAL HAZARDS MODEL [0.03%]
Marginal比例风险模型的多变量失效时间数据的分析
Ying Chen,Kani Chen,Zhiliang Ying
Ying Chen
The marginal proportional hazards model is an important tool in the analysis of multivariate failure time data in the presence of censoring. We propose a method of estimation via the linear combinations of martingale residuals. The estimati...