FTFLM_Final33variables

Figshare2021-05-19 更新2026-04-28 收录

探索性因子分析

变量选择

数据链接：

https://figshare.com/articles/dataset/FTFLM_Final33variables/14618091 数据链接链接失效反馈

官方服务：

资源简介：

Final 33 variables data, after dropping variables which are not significantly loading on the factors in the Exploratory Factor analysis.

创建时间：

2021-05-19

Variable Selection for Global Fréchet Regression

回归分析

变量选择

Global Fréchet regression is an extension of linear regression to cover more general types of responses, such as distributions, networks, and manifolds, which are becoming more prevalent. In such mode

DataCite Commons2025-04-29 更新70

Variable Selection for Global Fréchet Regression

Fréchet回归

变量选择

NIAID Data Ecosystem50

Analysis of Internal Structure of the Myokinetic Psychodiagnosis - PMK

心理诊断工具验证

探索性因子分析

Abstract The aim was to investigate the internal structure of Myokinetic Psychodiagnosis using exploratory factor analysis. The sample consisted of 300 subjects, 18-61 years of both genders from the

NIAID Data Ecosystem30

The regression coefficients from a stepwise multiple regression of ED against the numbers of new and shared TEs in each region.

逐步回归

变量选择

New terms were included (or excluded) from the model such that all factors in the model were nominally significant at p< = 0.05.

NIAID Data Ecosystem50

Can’t ridge regression perform variable selection?

变量选择

惩罚回归模型

Ridge regression was introduced to deal with the instability issue of the ordinary least squares estimate due to multicollinearity. It essentially penalizes the least squares loss by applying a ridge

Taylor & Francis Group2021-05-04 更新40

FTFLM_Final33variables

资源简介：

相关数据集