Extended fiducial inference for individual treatment effects via deep neural networks [0.03%]
基于深度神经网络的个体化治疗效应的扩展可信推断方法研究
Sehwan Kim,Faming Liang
Sehwan Kim
Individual treatment effect estimation has gained significant attention in recent data science literature. This work introduces the Double Neural Network (Double-NN) method to address this problem within the framework of extended fiducial i...
Qiang Heng,Kenneth Lange
Qiang Heng
This paper presents a nonparametric bootstrap method for estimating the proportions of inliers and outliers in robust regression models. Our approach is based on the concept of stability, providing robustness against distributional assumpti...
Online Bayesian changepoint detection for network Poisson processes with community structure [0.03%]
具有社区结构的网络泊松过程的在线贝叶斯变点检测方法
Joshua Corneck,Edward A K Cohen,James S Martin et al.
Joshua Corneck et al.
Network point processes often exhibit latent structure that govern the behaviour of the sub-processes. It is not always reasonable to assume that this latent structure is static, and detecting when and how this driving structure changes is ...
A new p-value based multiple testing procedure for generalized linear models [0.03%]
基于p值广义线性模型下多重假设检验的新方法
Joseph Rilling,Cheng Yong Tang
Joseph Rilling
This study introduces a novel p-value-based multiple testing approach tailored for generalized linear models. Despite the crucial role of generalized linear models in statistics, existing methodologies face obstacles arising from the hetero...
Estimation and model selection for finite mixtures of Tukey's g- &- h distributions [0.03%]
有限混合Tukey's g-&-h分布的估计和模型选择
Tingting Zhan,Misung Yi,Amy R Peck et al.
Tingting Zhan et al.
A finite mixture of distributions is a popular statistical model, which is especially meaningful when the population of interest may include distinct subpopulations. This work is motivated by analysis of protein expression levels quantified...
Lorenzo Rimella,Chris Jewell,Paul Fearnhead
Lorenzo Rimella
Inference for high-dimensional hidden Markov models is challenging due to the exponential-in-dimension computational cost of calculating the likelihood. To address this issue, we introduce an innovative composite likelihood approach called ...
Using prior-data conflict to tune Bayesian regularized regression models [0.03%]
利用先验数据冲突来调整贝叶斯正则化回归模型
Timofei Biziaev,Karen Kopciuk,Thierry Chekouo
Timofei Biziaev
In high-dimensional regression models, variable selection becomes challenging from a computational and theoretical perspective. Bayesian regularized regression via shrinkage priors like the Laplace or spike-and-slab prior are effective meth...
Enhancing Cure Rate Analysis Through Integration of Machine Learning Models: A Comparative Study [0.03%]
通过集成机器学习模型增强治愈率分析：一项比较研究
Wisdom Aselisewine,Suvra Pal
Wisdom Aselisewine
Cure rate models have been thoroughly investigated across various domains, encompassing medicine, reliability, and finance. The merging of machine learning (ML) with cure models is emerging as a promising strategy to improve predictive accu...
funBIalign: a hierachical algorithm for functional motif discovery based on mean squared residue scores [0.03%]
基于平均残差平方分数的分层算法 funBIAlign 功能基序发现算法
Jacopo Di Iorio,Marzia A Cremona,Francesca Chiaromonte
Jacopo Di Iorio
Motif discovery is gaining increasing attention in the domain of functional data analysis. Functional motifs are typical "shapes" or "patterns" that recur multiple times in different portions of a single curve and/or in misaligned portions ...
A Bayesian multilevel model for populations of networks using exponential-family random graphs [0.03%]
基于指数随机图的网络人群的贝叶斯多层次模型
Brieuc Lehmann,Simon White
Brieuc Lehmann
The collection of data on populations of networks is becoming increasingly common, where each data point can be seen as a realisation of a network-valued random variable. Moreover, each data point may be accompanied by some additional covar...