Integrative rank-based regression for multi-source high-dimensional data with multi-type responses [0.03%]
多源高维数据的多元响应整合秩回归方法研究
Fuzhi Xu,Shuangge Ma,Qingzhao Zhang
Fuzhi Xu
Practical scenarios often present instances where the types of responses are different between multi-source different datasets, reflecting distinct attributes or characteristics. In this paper, an integrative rank-based regression is propos...
An integrated change point detection and online monitoring approach for the ratio of two variables using clustering-based control charts [0.03%]
基于聚类控制图的两个变量的比例的整体变化点检测及在线监测方法
Adel Ahmadi Nadi,Ali Yeganeh,Sandile Charles Shongwe et al.
Adel Ahmadi Nadi et al.
Online monitoring of the ratio of two random characteristics rather than monitoring their individual behaviors has many applications. For this aim, there are various control charts, known as RZ charts in the literature, e.g. Shewhart, memor...
Upper quantile-based CUSUM-type control chart for detecting small changes in image data [0.03%]
基于上分位数的cusum型控制图在检测图像数据小变化中的应用
Anik Roy,Partha Sarathi Mukherjee
Anik Roy
Image monitoring is an important research problem that has wide applications in various fields, including manufacturing industries, satellite imaging, medical diagnostics, and so forth. Traditional image monitoring control charts perform ra...
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...