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Firth multivariate logistic model resulting from a stepdown procedure.

NIAID Data Ecosystem2026-03-11 收录

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https://figshare.com/articles/dataset/Firth_multivariate_logistic_model_resulting_from_a_stepdown_procedure_/12047706 数据链接链接失效反馈

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Firth multivariate logistic model resulting from a stepdown procedure.

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创建时间：

2020-03-30

变量选择

贝叶斯模型

In the context of a Gaussian multiple regression model, we address the problem of variable selection when in the list of potential predictors there are factors, that is, categorical variables. We adop

DataCite Commons2021-04-20 更新120

A Scalable Empirical Bayes Approach to Variable Selection in Generalized Linear Models

统计建模

变量选择

A new empirical Bayes approach to variable selection in the context of generalized linear models is developed. The proposed algorithm scales to situations in which the number of putative explanatory v

DataCite Commons2021-09-29 更新60

Can’t Ridge Regression Perform Variable Selection?

回归分析

变量选择

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

DataCite Commons2021-05-04 更新20

Operator-Induced Structural Variable Selection for Identifying Materials Genes

材料信息学

变量选择

In the emerging field of materials informatics, a fundamental task is to identify physicochemically meaningful descriptors, or materials genes, which are engineered from primary features and a set of

DataCite Commons2024-02-23 更新110

Logistic Regression coefficients and odd ratios for the non-PCR set.

回归分析

变量选择

This table shows the coefficients of the logistic regression model that results from backward variable selection with AIC, applied on the non-PCR sets. The interpretation of coefficients, P-values, od

NIAID Data Ecosystem20

Firth multivariate logistic model resulting from a stepdown procedure.

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