Changing Statistical Significance with the Amount of Information: The Adaptive α Significance Level [0.03%]
根据信息量调整统计显著性：自适应的α显著性水平
María-Eglée Pérez,Luis Raúl Pericchi
María-Eglée Pérez
We put forward an adaptive alpha which changes with the amount of sample information. This calibration may be interpreted as a Bayes/non-Bayes compromise, and leads to statistical consistency. The calibration can also be used to produce con...
Y Goldberg,M R Kosorok
Y Goldberg
We present an asymptotic exponential bound for the deviation of the survival function estimator of the Cox model. We show that the bound holds even when the proportional hazards assumption does not hold. ...
Mark M Meerschaert,Erkan Nane,Yimin Xiao
Mark M Meerschaert
Continuous time random walks impose random waiting times between particle jumps. This paper computes the fractal dimensions of their process limits, which represent particle traces in anomalous diffusion. ...
Partially Linear Varying Coefficient Models Stratified by a Functional Covariate [0.03%]
分段函数型协变量的偏线性变系数模型
Arnab Maity,Jianhua Z Huang
Arnab Maity
We consider the problem of estimation in semiparametric varying coefficient models where the covariate modifying the varying coefficients is functional and is modeled nonparametrically. We develop a kernel-based estimator of the nonparametr...
A causal framework for surrogate endpoints with semi-competing risks data [0.03%]
半竞争风险数据下的替代终点因果推断框架
Debashis Ghosh
Debashis Ghosh
In this note, we address the problem of surrogacy using a causal modelling framework that differs substantially from the potential outcomes model that pervades the biostatistical literature. The framework comes from econometrics and concept...
Julio M Singer,Edward J Stanek rd,Viviana B Lencina et al.
Julio M Singer et al.
We address the problem of selecting the best linear unbiased predictor (BLUP) of the latent value (e.g., serum glucose fasting level) of sample subjects with heteroskedastic measurement errors. Using a simple example, we compare the usual m...
Artin Armagan,David Dunson
Artin Armagan
It is increasingly common to be faced with longitudinal or multi-level data sets that have large numbers of predictors and/or a large sample size. Current methods of fitting and inference for mixed effects models tend to perform poorly in s...
Using Randomization Tests to Preserve Type I Error With Response-Adaptive and Covariate-Adaptive Randomization [0.03%]
使用随机化检验在响应适应性和协变量适应性随机化中保持I型误差
Richard Simon,Noah Robin Simon
Richard Simon
We demonstrate that clinical trials using response adaptive randomized treatment assignment rules are subject to substantial bias if there are time trends in unknown prognostic factors and standard methods of analysis are used. We develop a...
Heteroscedastic nonlinear regression models based on scale mixtures of skew-normal distributions [0.03%]
基于尺度混合的偏斜正态分布的异方差非线性回归模型
Victor H Lachos,Dipankar Bandyopadhyay,Aldo M Garay
Victor H Lachos
An extension of some standard likelihood based procedures to heteroscedastic nonlinear regression models under scale mixtures of skew-normal (SMSN) distributions is developed. We derive a simple EM-type algorithm for iteratively computing m...
Testing for Constant Nonparametric Effects in General Semiparametric Regression Models with Interactions [0.03%]
在一般半参数回归模型中检验恒定的非参数效应及相互作用
Jiawei Wei,Raymond J Carroll,Arnab Maity
Jiawei Wei
We consider the problem of testing for a constant nonparametric effect in a general semi-parametric regression model when there is the potential for interaction between the parametrically and nonparametrically modeled variables. The work wa...