An Easy-to-Implement Hierarchical Standardization for Variable Selection Under Strong Heredity Constraint

For many practical problems, the regression models follow the strong heredity property (also known as the marginality), which means they include parent main effects when a second-order effect is present. Existing methods rely mostly on special penalty functions or algorithms to enforce the strong he...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Journal of statistical theory and practice Ročník 14; číslo 3
Hlavní autori: Chen, Kedong, Li, William, Wang, Sijian
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Cham Springer International Publishing 01.09.2020
Predmet:
Algorithms
Analysis and Advanced Methodologies in the Design of Experiments
Mathematics and Statistics
Original Article
Probability Theory and Stochastic Processes
Statistical Theory and Methods
Statistics
Marginality
Hierarchical standardization
Heredity
Hierarchical structure
Variable selection
ISSN:1559-8608, 1559-8616
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:For many practical problems, the regression models follow the strong heredity property (also known as the marginality), which means they include parent main effects when a second-order effect is present. Existing methods rely mostly on special penalty functions or algorithms to enforce the strong heredity in variable selection. We propose a novel hierarchical standardization procedure to maintain strong heredity in variable selection. Our method is effortless to implement and is applicable to any variable selection method for any type of regression. The performance of the hierarchical standardization is comparable to that of the regular standardization. We also provide robustness checks and real data analysis to illustrate the merits of our method.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1559-8608
1559-8616
DOI:10.1007/s42519-020-00102-x