Central double cross-validation for estimating parameters in regression models
The ridge regression, lasso, elastic net, forward stagewise regression and the least angle regression require a solution path and tuning parameter, λ, to estimate the coefficient vector. Therefore, it is crucial to find the ideal λ. Cross-validation (CV) is the most widely utilized method for choosi...
Saved in:
Main Author: | Chye, Rou Shi |
---|---|
Format: | Thesis |
Language: | English |
Published: |
2016
|
Subjects: | |
Online Access: | http://eprints.utm.my/id/eprint/80959/2/ChyeRouShiMFS2016.pdf http://eprints.utm.my/id/eprint/80959/ http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:120286 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Generalized Cross-Validation for Simultaneous Optimization of Tuning Parameters in Ridge Regression
by: Roozbeh, Mahdi, et al.
Published: (2020) -
Parameter estimation of stochastic differential equation : Bayesian regression
by: Abd.Rahman, Haliza, et al.
Published: (2010) -
New approaches in estimating linear regression model parameters in the presence of multicollinearity and outliers
by: Al-Mash, Mohammad Sabry Abo
Published: (2017) -
Comparing least-squares and goal programming estimates of linear regression parameter.
by: Ahmad, Maizah Hura, et al.
Published: (2005) -
Estimating tree crown model with multiple regression
by: Chang, Chung Weng
Published: (2008)