An improved two-stage binary relevance method for multilabel classification [0.03%]
一种改进的二阶段二元相关多标签分类方法
Ziyue Chen,Qing Wang
Ziyue Chen
Multilabel classification concerns a family of unconventional classification problems, where each instance may be associated with multiple labels simultaneously. One of the traditional methods for multilabel classification is the binary rel...
Classification of multivariate functional data with an application to ADHD fMRI data [0.03%]
一种多变量功能数据分析方法及其在注意缺陷多动障碍功能性磁共振图像数据分类中的应用研究
Yeji Seong,Iris Ivy Gauran,Hyunsung Kim et al.
Yeji Seong et al.
The classification of resting-state functional magnetic resonance imaging (rs-fMRI) data presents unique challenges in the detection and diagnosis of neuropsychiatric disorders such as Attention-Deficit/Hyperactivity Disorder (ADHD). Tradit...
Assessing the performance of longitudinal T-lymphocytes as biomarkers of immune recovery in HIV-infected children with or without TB co-infection [0.03%]
评估HIV感染儿童是否合并结核感染的纵向T淋巴细胞作为免疫恢复生物标志物的作用
Musie Ghebremichael
Musie Ghebremichael
In this paper, the receiver operating characteristic (ROC) curve was used to investigate the performance of longitudinal CD4+ T cell counts as a biomarker of disease recovery in HIV-TB co-infected children from sub-Saharan Africa. According...
Tianci Qian
Tianci Qian
This paper introduces a novel regularization framework for the Markowitz mean-variance portfolio optimization under long-only constraints. A sufficient condition that explains the sparsity of long-only optimal portfolios is derived, showing...
Homogeneity of multinomial populations when data are classified into a large number of groups [0.03%]
大分类数据的多项式总体的同质性研究
M V Alba-Fernández,M D Jiménez-Gamero,F J Ariza-López
M V Alba-Fernández
Suppose that we are interested in the comparison of two independent categorical variables. Suppose also that the population is divided into subpopulations or groups. Notice that the distribution of the target variable may vary across subpop...
Inference for dependent competing risks model under m-cycle minimum ranked set sampling [0.03%]
m-周期最小秩置信抽样下的相依竞争风险模型推断
Jiaxin Zhang,Wenhao Gui
Jiaxin Zhang
Minimum ranked set sampling offers an effective approach for collecting failure time data while optimizing testing resources. This paper examines dependent competing risks model within the context of m-cycle minimum ranked set sampling data...
Adjusted profile likelihood inference for the scale parameter of the Gumbel distribution [0.03%]
Gumbel分布尺度参数的修正概似推断
Ayman Baklizi
Ayman Baklizi
We consider inference about the scale parameter of the Gumbel distribution. The maximum likelihood estimator of the scale parameter based on the profile likelihood is biased. This is because the profile likelihood function is not a genuine ...
Distribution-valued data graphical model estimation based on M-LDQ feature embedding [0.03%]
基于M-LDQ特征嵌入的分布值数据图模型估计方法
Qiying Wu,Huiwen Wang,Shan Lu
Qiying Wu
Understanding and modeling distribution-valued data, an important form of symbolic data, has garnered significant attention in statistics because of its effectiveness in handling large datasets. Conventional statistical inference methods ar...
An integer-valued spatial autoregressive model with application to COVID-19 counts [0.03%]
一种整数值空间自回归模型及其在COVID-19计数上的应用
Kai Yang,Mingming Jia,Xiaogang Dong
Kai Yang
In order to effectively capture the spatial dependencies of integer-valued count data, this paper introduces an integer-valued spatial autoregressive model based on negative binomial thinning operator. The model properties are studied in de...
Yasir Atalan,Selim Yaman,Jeff Gill
Yasir Atalan
This work first describes Bayesian Partially-Protected Lasso (BPL), which combines the power of Bayesian Lasso with the ability to protect key theoretical explanatory variables from shrinkage to a zero effect in the model. This approach all...