A Hybrid Method for Density Power Divergence Minimization with Application to Robust Univariate Location and Scale Estimation [0.03%]
一种用于密度幂散度最小化的混合方法及其在稳健单变量位置和尺度估计中的应用
Andrews T Anum,Michael Pokojovy
Andrews T Anum
We develop a new globally convergent optimization method for solving a constrained minimization problem underlying the minimum density power divergence estimator for univariate Gaussian data in the presence of outliers. Our hybrid procedure...
A few theoretical results for Laplace and arctan penalized ordinary least squares linear regression estimators [0.03%]
Laplace和反正切带罚则的普通最小二乘法线性回归估计量的若干理论结果
Majnu John,Sujit Vettam
Majnu John
Two new nonconvex penalty functions - Laplace and arctan - were recently introduced in the literature to obtain sparse models for high-dimensional statistical problems. In this paper, we study the theoretical properties of Laplace and arcta...
D-optimal designs for two-variable logistic regression model with restricted design space [0.03%]
具有受限设计空间的两个变量逻辑回归模型的D-最优设计
Yi Zhai,Chengci Wang,Hui-Yi Lin et al.
Yi Zhai et al.
The problem of constructing locally D-optimal designs for two-variable logistic model with no interaction has been studied in many literature. In Kabera, Haines, and Ndlovu (2015), the model is restricted to have positive slopes and negativ...
A fortune cookie problem: A test for nominal data whether two samples are from the same population of equally likely elements [0.03%]
一个关于名义数据的抽样来自相同总体的假设检验问题
Jiangtao Gou,Karen Ruth,Stanley Basickes et al.
Jiangtao Gou et al.
This article considers a way to test the hypothesis that two collections of objects are from the same uniform distribution of such objects. The exact p-value is calculated based on the distribution for the observed overlaps. In addition, an...
Han Yu,Alan D Hutson
Han Yu
In this work, we show that Spearman's correlation coefficient test about H0:ρs=0 found in most statistical software is theoretically incorrect and performs poorly when bivariate normality assumptions are not met or the sample size is s...
Inference for sparse linear regression based on the leave-one-covariate-out solution path [0.03%]
基于留一预测变量法的稀疏线性回归推断路径
Xiangyang Cao,Karl Gregory,Dewei Wang
Xiangyang Cao
We propose a new measure of variable importance in high-dimensional regression based on the change in the LASSO solution path when one covariate is left out. The proposed procedure provides a novel way to calculate variable importance and c...
Estimating Time-Varying Treatment Switching Effect Using Accelerated Failure Time Model with Application to Vascular Access for Hemodialysis [0.03%]
基于加速失效时间模型估计动态转换治疗效应及其在血液透析动静脉内瘘中的应用研究
Fang-I Chu,Yuedong Wang
Fang-I Chu
Vascular access for hemodialysis is of paramount importance. Although studies have found that central venous catheter (CVC) is often associated with poor outcomes and switching to arteriovenous fistula (AVF) and arteriovenous grafts (AVG) i...
A note on semiparametric efficient generalization of causal effects from randomized trials to target populations [0.03%]
关于从随机试验推广因果效应到目标人群的半参数有效方法的注记
Fan Li,Hwanhee Hong,Elizabeth A Stuart
Fan Li
When effect modifiers influence the decision to participate in randomized trials, generalizing causal effect estimates to an external target population requires the knowledge of two scores - the propensity score for receiving treatment and ...
Power for balanced linear mixed models with complex missing data processes [0.03%]
具有复杂缺失数据过程的平衡线性混合模型的检验力
Kevin P Josey,Brandy M Ringham,Anna E Barón et al.
Kevin P Josey et al.
When designing repeated measures studies, both the amount and the pattern of missing outcome data can affect power. The chance that an observation is missing may vary across measurements, and missingness may be correlated across measurement...
Semiparametric copula-based regression modeling of semi-competing risks data [0.03%]
半竞争风险数据的半参数基于copula回归建模
Hong Zhu,Yu Lan,Jing Ning et al.
Hong Zhu et al.
Semi-competing risks data often arise in medical studies where the terminal event (e.g., death) censors the non-terminal event (e.g., cancer recurrence), but the non-terminal event does not prevent the subsequent occurrence of the terminal ...