Variable selection in regression—a tutorial

By C. M. Andersen, Rasmus Bro. 2010

683 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Control and Systems Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/dc44c6f1683c44a912ae25eb78b0784212a476c7

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Analytical Chemistry

1. A. Gomes
2. Edvan Cirino da Silva
3. Germano Véras
4. Roberto Kawakami Harrop Galvão
5. Mário César Ugulino de Araújo
6. Daniel M. McNeish
7. Belén Martín-Barragán
8. Asunción Jiménez-Cordero

In Control and Systems Engineering

1. Jie Ding
2. Vahid Tarokh
3. Emilio Carrizosa
4. Daniel M. McNeish
5. Belén Martín-Barragán
6. Asunción Jiménez-Cordero
7. Rasmus Bro
8. Roberto Kawakami Harrop Galvão

In Mechanical Engineering

1. Daniel M. McNeish

Citations in the corpus

• Model Selection Techniques: An Overview (2018, background): “There have been many overview papers on model selection scattered in the communities of signal processing [1], statistics [2], machine learning [3], epidemiology [4], chemometrics [5], and ecology and evolution [6].”
• Identification of Bruise and Fungi Contamination in Strawberries Using Hyperspectral Imaging Technology and Multivariate Analysis (2018, background): “mative wavelengths with noise or invalid information is eliminated in great extent (Andersen and Bro 2010).”
• Using Lasso for Predictor Selection and to Assuage Overfitting: A Method Long Overlooked in Behavioral Sciences (2015, background): “…with overfitting are not new to many behavioral science researchers and these ideas have been circulated throughout many literatures (see, e.g., Andersen & Bro, 2010; Babyuk, 2004; Cohen et al., 2003; Harrell, 2001; Harrell, Lee, Matchar, & Reichert, 1985; Hawkins, 2004), methods that are…”
• Selection of time instants and intervals with Support Vector Regression for multivariate functional data (2020, background): “Particularly, in the regression area, we can highlight (Andersen and Bro, 2010; Karagiannopoulos et al., 2007; Li et al., 2009; Mehmood et al., 2012; Van Dijck and Van Hulle, 2006; Yang and Ong, 2011; Zhang, 2009).”
• Variable selection in multi-block regression (2016, background): “From a practical point of view, selecting variables can make future acquisition of data cheaper and less time-consuming [10-11].”