Model-based clustering of time-dependent observations with common structural changes [0.03%]
基于模型的聚类方法及其在结构共同变化时间序列数据中的应用
Riccardo Corradin,Luca Danese,Wasiur R KhudaBukhsh et al.
Riccardo Corradin et al.
We propose a novel model-based clustering approach for samples of time series. We assume as a unique commonality that two observations belong to the same group if structural changes in their behaviors happen at the same time. We resort to a...
Efficient Likelihood-Based Temporal Changepoint Detection in Spatio-Temporal Processes [0.03%]
基于有效似然的时空过程中时效性变化点检测方法
Gaurav Agarwal,Idris A Eckley,Paul Fearnhead
Gaurav Agarwal
The rapid advancements of scalable methodologies have opened new avenues for analyzing complex spatio-temporal data, which is crucial in understanding dynamic environmental phenomena. This paper introduces a likelihood-based methodology for...
Minimax optimal designs via particle swarm optimization methods [0.03%]
基于粒子群优化方法的最小最大 optimal design 方法研究
Ray-Bing Chen,Shin-Perng Chang,Weichung Wang et al.
Ray-Bing Chen et al.
Particle swarm optimization (PSO) techniques are widely used in applied fields to solve challenging optimization problems but they do not seem to have made an impact in mainstream statistical applications hitherto. PSO methods are popular b...
Outcome-guided spike-and-slab Lasso Biclustering: A Novel Approach for Enhancing Biclustering Techniques for Gene Expression Analysis [0.03%]
一种用于增强基因表达分析双聚类技术的新型结果引导式_spike-and-slab_LASSO_双聚类方法
Luis A Vargas-Mieles,Paul D W Kirk,Chris Wallace
Luis A Vargas-Mieles
Biclustering has gained interest in gene expression data analysis due to its ability to identify groups of samples that exhibit similar behaviour in specific subsets of genes (or vice versa), in contrast to traditional clustering methods th...
Yaeji Lim,Ruijin Lu,Madeleine St Ville et al.
Yaeji Lim et al.
In this paper, we introduce a novel approach that integrates Bayesian additive regression trees (BART) with the composite quantile regression (CQR) framework, creating a robust method for modeling complex relationships between predictors an...
A Neural Network Integrated Accelerated Failure Time-Based Mixture Cure Model [0.03%]
一种神经网络集成的加速失效时间混合愈合模型
Wisdom Aselisewine,Suvra Pal
Wisdom Aselisewine
The mixture cure rate model (MCM) is commonly used for analyzing survival data with a cured subgroup. While the prevailing approach to modeling the probability of cure involves a generalized linear model using a known parametric link functi...
Sida Chen,Danilo Alvares,Marco Palma et al.
Sida Chen et al.
Joint models (JMs) for longitudinal and time-to-event data are an important class of biostatistical models in health and medical research. When the study population consists of heterogeneous subgroups, standard JMs may be inadequate, leadin...
Yoonji Kim,Oksana A Chkrebtii,Sebastian A Kurtek
Yoonji Kim
In many modern applications, discretely-observed data may be naturally understood as a set of functions. Functional data often exhibit two confounded sources of variability: amplitude (y-axis) and phase (x-axis). The extraction of amplitude...
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...