A class of transformed joint quantile time series models with applications to health studies [0.03%]
一类变换联合分位数时间序列模型及其在健康研究中的应用
Fahimeh Tourani-Farani,Zeynab Aghabazaz,Iraj Kazemi
Fahimeh Tourani-Farani
Extensions of quantile regression modeling for time series analysis are extensively employed in medical and health studies. This study introduces a specific class of transformed quantile-dispersion regression models for non-stationary time ...
Approximate Bayesian inference in a model for self-generated gradient collective cell movement [0.03%]
自生梯度集体细胞运动模型中的近似贝叶斯推理
Jon Devlin,Agnieszka Borowska,Dirk Husmeier et al.
Jon Devlin et al.
In this article we explore parameter inference in a novel hybrid discrete-continuum model describing the movement of a population of cells in response to a self-generated chemotactic gradient. The model employs a drift-diffusion stochastic ...
Likelihood Inference for Unified Transformation Cure Model with Interval Censored Data [0.03%]
区间删失数据下统一变换模型的似然推断方法
Jodi Treszoks,Suvra Pal
Jodi Treszoks
In this paper, we extend the unified class of Box-Cox transformation (BCT) cure rate models to accommodate interval-censored data. The probability of cure is modeled using a general covariate structure, whereas the survival distribution of ...
Peter C Austin
Peter C Austin
In time-to-event analyses, a competing risk is an event whose occurrence precludes the occurrence of the event of interest. Settings with competing risks occur frequently in clinical research. Missing data, which is a common problem in rese...
An Extended Langevinized Ensemble Kalman Filter for non-Gaussian Dynamic Systems [0.03%]
一种扩展的朗之万化集合卡尔曼滤波方法及其在非高斯动态系统中的应用
Peiyi Zhang,Tianning Dong,Faming Liang
Peiyi Zhang
State estimation for large-scale non-Gaussian dynamic systems remains an unresolved issue, given nonscalability of the existing particle filter algorithms. To address this issue, this paper extends the Langevinized ensemble Kalman filter (L...
A New Approach to Modeling the Cure Rate in the Presence of Interval Censored Data [0.03%]
间断检测数据下建模治愈率的新方法
Suvra Pal,Yingwei Peng,Wisdom Aselisewine
Suvra Pal
We consider interval censored data with a cured subgroup that arises from longitudinal followup studies with a heterogeneous population where a certain proportion of subjects is not susceptible to the event of interest. We propose a two com...
Chung Chang,R Todd Ogden,Yakuan Chen
Chung Chang
In recent years, several methods have been proposed to deal with functional data classification problems (e.g., one-dimensional curves or two- or three-dimensional images). One popular general approach is based on the kernel-based method, p...
Joint Bayesian longitudinal models for mixed outcome types and associated model selection techniques [0.03%]
混合结果类型的联合贝叶斯纵向模型及其相关的模型选择技术
Nicholas Seedorff,Grant Brown,Breanna Scorza et al.
Nicholas Seedorff et al.
Motivated by data measuring progression of leishmaniosis in a cohort of US dogs, we develop a Bayesian longitudinal model with autoregressive errors to jointly analyze ordinal and continuous outcomes. Multivariate methods can borrow strengt...
Armando Tapia,Silvestre L González,Jose R Vergara et al.
Armando Tapia et al.
The interest of this article is to better understand the effects of different public policy alternatives to handle the COVID-19 pandemic. In this work we use the susceptible, infected, recovered (SIR) model to find which of these policies h...
Spatio-temporal clustering analysis using generalized lasso with an application to reveal the spread of Covid-19 cases in Japan [0.03%]
基于广义LASSO的时空聚类分析及其在日本COVID-19传播中的应用
Septian Rahardiantoro,Wataru Sakamoto
Septian Rahardiantoro
This study addressed the issue of determining multiple potential clusters with regularization approaches for the purpose of spatio-temporal clustering. The generalized lasso framework has flexibility to incorporate adjacencies between objec...