Comment: Feature Screening and Variable Selection via Iterative Ridge Regression

Technometrics ◽

10.1080/00401706.2020.1801256 ◽

2020 ◽

Vol 62 (4) ◽

pp. 434-437

Author(s):

Jianqing Fan ◽

Runze Li

Keyword(s):

Variable Selection ◽

Ridge Regression ◽

Feature Screening

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Variable Selection and Feature Screening

Macroeconomic Forecasting in the Era of Big Data - Advanced Studies in Theoretical and Applied Econometrics ◽

10.1007/978-3-030-31150-6_10 ◽

2019 ◽

pp. 293-326

Author(s):

Wanjun Liu ◽

Runze Li

Keyword(s):

Variable Selection ◽

Feature Screening

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Feature screening and variable selection for partially linear models with ultrahigh-dimensional longitudinal data

Neurocomputing ◽

10.1016/j.neucom.2015.09.122 ◽

2016 ◽

Vol 195 ◽

pp. 202-210 ◽

Author(s):

Jingyuan Liu

Keyword(s):

Variable Selection ◽

Longitudinal Data ◽

Linear Models ◽

Partially Linear Models ◽

Feature Screening ◽

Partially Linear ◽

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Can’t Ridge Regression Perform Variable Selection?

Technometrics ◽

10.1080/00401706.2020.1791254 ◽

2020 ◽

pp. 1-9

Author(s):

Yichao Wu

Keyword(s):

Variable Selection ◽

Ridge Regression

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Variable selection in random calibration of near-infrared instruments: ridge regression and partial least squares regression settings

Journal of Chemometrics ◽

10.1002/cem.787 ◽

2003 ◽

Vol 17 (3) ◽

pp. 174-185 ◽

Author(s):

Arief Gusnanto ◽

Yudi Pawitan ◽

Jian Huang ◽

Bill Lane

Keyword(s):

Variable Selection ◽

Least Squares ◽

Partial Least Squares ◽

Ridge Regression ◽

Partial Least Squares Regression ◽

Near Infrared ◽

Least Squares Regression ◽

Infrared Instruments

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Simultaneous Estimation and Variable Selection for Interval-Censored Data With Broken Adaptive Ridge Regression

Journal of the American Statistical Association ◽

10.1080/01621459.2018.1537922 ◽

2019 ◽

Vol 115 (529) ◽

pp. 204-216 ◽

Author(s):

Hui Zhao ◽

Qiwei Wu ◽

Gang Li ◽

Jianguo Sun

Keyword(s):

Variable Selection ◽

Censored Data ◽

Ridge Regression ◽

Simultaneous Estimation ◽

Interval Censored Data ◽

Interval Censored ◽

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Variable Selection in Prediction Equation by Ridge Regression Analysis for Body Composition Study

Japan Journal of Human Growth and Development Research ◽

10.5332/hatsuhatsu.1995.63 ◽

1995 ◽

pp. 63-66

Author(s):

Kazutoshi KIKKAWA ◽

Shuichi KOMIYA ◽

Minoru YASUDA ◽

Norie YAMADA

Keyword(s):

Body Composition ◽

Regression Analysis ◽

Variable Selection ◽

Ridge Regression ◽

Prediction Equation ◽

Body Composition Study ◽

Composition Study ◽

Ridge Regression Analysis

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Concordance Measure-based Feature Screening and Variable Selection

Statistica Sinica ◽

10.5705/ss.202016.0024 ◽

2018 ◽

Author(s):

Yunbei Ma ◽

Yi Li ◽

Huazhen Lin ◽

Yi Li

Keyword(s):

Variable Selection ◽

Feature Screening

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Interval ridge regression (iRR) as a fast and robust method for quantitative prediction and variable selection applied to edible oil adulteration

Talanta ◽

10.1016/j.talanta.2015.12.007 ◽

2016 ◽

Vol 150 ◽

pp. 37-45 ◽

Author(s):

Ozren Jović ◽

Neven Smrečki ◽

Zora Popović

Keyword(s):

Variable Selection ◽

Ridge Regression ◽

Quantitative Prediction ◽

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Protein Secondary Structure from Circular Dichroism Spectroscopy Combining Variable Selection Principle and Cluster Analysis with Neural Network, Ridge Regression and Self-consistent Methods

Journal of Molecular Biology ◽

10.1016/s0022-2836(84)71597-x ◽

1994 ◽

Vol 242 (4) ◽

pp. 497-507 ◽

Author(s):

N SREERAMA ◽

R WOODY

Keyword(s):

Neural Network ◽

Cluster Analysis ◽

Secondary Structure ◽

Variable Selection ◽

Ridge Regression ◽

Protein Secondary Structure ◽

Selection Principle ◽

Self Consistent ◽

And Cluster Analysis ◽

Circular Dichroïsm

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Variable selection for recurrent event data with broken adaptive ridge regression

Canadian Journal of Statistics ◽

10.1002/cjs.11459 ◽

2018 ◽

Vol 46 (3) ◽

pp. 416-428 ◽

Author(s):

Hui Zhao ◽

Dayu Sun ◽

Gang Li ◽

Jianguo Sun

Keyword(s):

Variable Selection ◽

Ridge Regression ◽

Recurrent Event ◽

Recurrent Event Data ◽

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