Prediction intervals and bands with improved coverage for functional data under noisy discrete observation [0.03%]
在噪声离散观测下的函数数据的改进覆盖预测区间和带状图
David Kraus
David Kraus
We revisit the classic situation in functional data analysis in which curves are observed at discrete, possibly sparse and irregular, arguments with observation noise. We focus on the reconstruction of individual curves by prediction interv...
Wisdom Aselisewine,Suvra Pal,Helton Saulo
Wisdom Aselisewine
The mixture cure rate model (MCM) is the most widely used model for the analysis of survival data with a cured subgroup. In this context, the most common strategy to model the cure probability is to assume a generalized linear model with a ...
R Lakshmi,T A Sajesh
R Lakshmi
Identifying outliers in data analysis is a critical task, as outliers can significantly influence the results and conclusions drawn from a dataset. This study explores the use of the Mahalanobis distance metric for detecting outliers in mul...
Graphical representation of survival curves in the presence of time-dependent categorical covariates with application to liver transplantation [0.03%]
肝移植背景下时间依赖性分类协变量存在的生存曲线图形表示法
Abigail R Smith,Nathan P Goodrich,Charlotte A Beil et al.
Abigail R Smith et al.
Graphical representation of survival curves is often used to illustrate associations between exposures and time-to-event outcomes. However, when exposures are time-dependent, calculation of survival probabilities is not straightforward. Our...
Regression-based rectangular tolerance regions as reference regions in laboratory medicine [0.03%]
基于回归的矩形容差区作为实验室医学中的参考区域
Iana Michelle L Garcia,Michael Daniel C Lucagbo
Iana Michelle L Garcia
Reference ranges are invaluable in laboratory medicine, as these are indispensable tools for the interpretation of laboratory test results. When assessing measurements on a single analyte, univariate reference intervals are required. In man...
Robust multi-outcome regression with correlated covariate blocks using fused LAD-lasso [0.03%]
使用融合LAD套索进行相关协变量块的稳健多结果回归分析
Jyrki Möttönen,Tero Lähderanta,Janne Salonen et al.
Jyrki Möttönen et al.
Lasso is a popular and efficient approach to simultaneous estimation and variable selection in high-dimensional regression models. In this paper, a robust fused LAD-lasso method for multiple outcomes is presented that addresses the challeng...
Reliability analysis based on doubly-truncated and interval-censored data [0.03%]
基于双截断和区间删失数据的可靠性分析
Pao-Sheng Shen,Huai-Man Li
Pao-Sheng Shen
Field data provide important information on product reliability. Interval sampling is widely used for collection of field data, which typically report incident cases during a certain time period. Such sampling scheme induces doubly truncate...
Elisa Frutos-Bernal,José Luis Vicente-Villardón
Elisa Frutos-Bernal
Biplots are useful tools because they provide a visual representation of both individuals and variables simultaneously, making it easier to explore relationships and patterns within multidimensional datasets. This paper extends their use to...
Bayesian poisson regression tensor train decomposition model for learning mortality pattern changes during COVID-19 pandemic [0.03%]
用于学习COVID-19大流行期间死亡率模式变化的贝叶斯泊松回归张量列车分解模型
Wei Zhang,Antonietta Mira,Ernst C Wit
Wei Zhang
COVID-19 has led to excess deaths around the world. However, the impact on mortality rates from other causes of death during this time remains unclear. To understand the broader impact of COVID-19 on other causes of death, we analyze Italia...
Yasir Khan,Said Farooq Shah,Syed Muhammad Asim
Yasir Khan
Missing data is a common problem in many domains that rely on data analysis. The k Nearest Neighbors imputation method has been widely used to address this issue, but it has limitations in accurately imputing missing values, especially for ...