Causal effect estimation for competing risk data in randomized trial: adjusting covariates to gain efficiency [0.03%]
调整协变量以提高效率的随机对照试验竞争风险数据因果效应估计方法研究
Youngjoo Cho,Cheng Zheng,Lihong Qi et al.
Youngjoo Cho et al.
The double-blinded randomized trial is considered the gold standard to estimate the average causal effect (ACE). The naive estimator without adjusting any covariate is consistent. However, incorporating the covariates that are strong predic...
Bayesian doubly robust estimation of causal effects for clustered observational data [0.03%]
贝叶斯双重稳健因果效应估计及其在聚类观测数据中的应用
Qi Zhou,Haonan He,Jie Zhao et al.
Qi Zhou et al.
Observational data often exhibit clustered structure, which leads to inaccurate estimates of exposure effect if such structure is ignored. To overcome the challenges of modelling the complex confounder effects in clustered data, we propose ...
A Batsidis,G Tzavelas,P Economou
A Batsidis
The present research deals with statistical inference for the expectation of a function of a random vector based on biased samples. After highlighting with the help of a motivating example the need for conducting this study, using the conce...
The slashed Lomax distribution: new properties and Mellin-type statistical measures for inference [0.03%]
具有新属性的切点Lomax分布及Mellin型统计测量推断方法
Jaine de Moura Carvalho,Frank Gomes-Silva,Josimar M Vasconcelos et al.
Jaine de Moura Carvalho et al.
Several continuous distributions have been proposed recently to provide more flexibility in modeling lifetime data. Among these, the Slashed class of models, particularly the Slashed Lomax ( SL ) distribution, has gained special attention....
Adapting and evaluating deep-pseudo neural network for survival data with time-varying covariates [0.03%]
具有时变协变量的生存数据的深度伪神经网络的适应和评估
Albert Whata,Justine B Nasejje,Najmeh Nakhaei Rad et al.
Albert Whata et al.
The Extended Cox model provides an alternative to the proportional hazard Cox model for modelling data including time-varying covariates. Incorporating time-varying covariates is particularly beneficial when dealing with survival data, as i...
Semiparametric model averaging prediction in nested case-control studies [0.03%]
嵌套性列联表研究中的半参数模型平均预测方法研究
Mengyu Li,Xiaoguang Wang
Mengyu Li
Survival predictions for patients are becoming increasingly important in clinical practice as they play a crucial role in aiding healthcare professionals to make more informed diagnoses and treatment decisions. The nested case-control desig...
Statistical inference for dependent competing risks data under adaptive Type-II progressive hybrid censoring [0.03%]
适应性II型渐进杂交删失下相依竞争风险的统计推断
Subhankar Dutta,Suchandan Kayal
Subhankar Dutta
In this article, we consider statistical inference based on dependent competing risks data from Marshall-Olkin bivariate Weibull distribution. The maximum likelihood estimates of the unknown model parameters have been computed by using Newt...
Weighted portmanteau statistics for testing for zero autocorrelation in dependent data [0.03%]
相依数据的零自相关性检验的加权组合统计量
N Muriel
N Muriel
Zero autocorrelation test statistics of the portmanteau type are studied under dependence. The asymptotic distribution of statistics formed with weighted averages of the autocorrelation and partial autocorrelation functions is theoretically...
Scalable Bayesian inference for bradley-Terry models with ties: an application to honour based abuse [0.03%]
考虑并列情况的大规模贝叶斯布拉德利-特里模型的推理：基于荣誉的虐待行为应用
Rowland G Seymour,Fabian Hernandez
Rowland G Seymour
Honour-based abuse covers a wide range of family abuse including female genital mutilation and forced marriage. Safeguarding professionals need to identify where abuses are happening in their local community to the best support those at ris...
Lixia Hu,Jinhong You,Qian Huang et al.
Lixia Hu et al.
Time-varying coefficient regression is commonly used in the modeling of nonstationary stochastic processes. In this paper, we consider a time-varying coefficient convolution-type smoothed quantile regression (conquer). The covariates and er...