118 related articles for article (PubMed ID: 30127856)
1. Evolutionary methods for variable selection in the epidemiological modeling of cardiovascular diseases.
Brester C; Kauhanen J; Tuomainen TP; Voutilainen S; Rönkkö M; Ronkainen K; Semenkin E; Kolehmainen M
BioData Min; 2018; 11():18. PubMed ID: 30127856
[TBL] [Abstract][Full Text] [Related]
2. Epidemiological predictive modeling: lessons learned from the Kuopio ischemic heart disease risk factor study.
Brester C; Voutilainen A; Tuomainen TP; Kauhanen J; Kolehmainen M
Ann Epidemiol; 2022 Jun; 70():1-8. PubMed ID: 35354081
[TBL] [Abstract][Full Text] [Related]
3. Assessing the accuracy and stability of variable selection methods for random forest modeling in ecology.
Fox EW; Hill RA; Leibowitz SG; Olsen AR; Thornbrugh DJ; Weber MH
Environ Monit Assess; 2017 Jul; 189(7):316. PubMed ID: 28589457
[TBL] [Abstract][Full Text] [Related]
4. A variable selection method based on mutual information and variance inflation factor.
Cheng J; Sun J; Yao K; Xu M; Cao Y
Spectrochim Acta A Mol Biomol Spectrosc; 2022 Mar; 268():120652. PubMed ID: 34896682
[TBL] [Abstract][Full Text] [Related]
5. The value of Bayesian predictive projection for variable selection: an example of selecting lifestyle predictors of young adult well-being.
Bartonicek A; Wickham SR; Pat N; Conner TS
BMC Public Health; 2021 Apr; 21(1):695. PubMed ID: 33836714
[TBL] [Abstract][Full Text] [Related]
6. Comprehensive new approaches for variable selection using ordered predictors selection.
Roque JV; Cardoso W; Peternelli LA; Teófilo RF
Anal Chim Acta; 2019 Oct; 1075():57-70. PubMed ID: 31196424
[TBL] [Abstract][Full Text] [Related]
7. Post-Analysis of Predictive Modeling with an Epidemiological Example.
Brester C; Voutilainen A; Tuomainen TP; Kauhanen J; Kolehmainen M
Healthcare (Basel); 2021 Jun; 9(7):. PubMed ID: 34202622
[TBL] [Abstract][Full Text] [Related]
8. Evaluating the performance of machine learning methods and variable selection methods for predicting difficult-to-measure traits in Holstein dairy cattle using milk infrared spectral data.
Mota LFM; Pegolo S; Baba T; Peñagaricano F; Morota G; Bittante G; Cecchinato A
J Dairy Sci; 2021 Jul; 104(7):8107-8121. PubMed ID: 33865589
[TBL] [Abstract][Full Text] [Related]
9. Multivariate modeling of complications with data driven variable selection: guarding against overfitting and effects of data set size.
van der Schaaf A; Xu CJ; van Luijk P; Van't Veld AA; Langendijk JA; Schilstra C
Radiother Oncol; 2012 Oct; 105(1):115-21. PubMed ID: 22264894
[TBL] [Abstract][Full Text] [Related]
10. Random forests for feature selection in QSPR Models - an application for predicting standard enthalpy of formation of hydrocarbons.
Teixeira AL; Leal JP; Falcao AO
J Cheminform; 2013 Feb; 5(1):9. PubMed ID: 23399299
[TBL] [Abstract][Full Text] [Related]
11. Comparison of variable selection methods for clinical predictive modeling.
Sanchez-Pinto LN; Venable LR; Fahrenbach J; Churpek MM
Int J Med Inform; 2018 Aug; 116():10-17. PubMed ID: 29887230
[TBL] [Abstract][Full Text] [Related]
12. Recursive Random Forests Enable Better Predictive Performance and Model Interpretation than Variable Selection by LASSO.
Zhu XW; Xin YJ; Ge HL
J Chem Inf Model; 2015 Apr; 55(4):736-46. PubMed ID: 25746224
[TBL] [Abstract][Full Text] [Related]
13. Predicting the graft survival for heart-lung transplantation patients: an integrated data mining methodology.
Oztekin A; Delen D; Kong ZJ
Int J Med Inform; 2009 Dec; 78(12):e84-96. PubMed ID: 19497782
[TBL] [Abstract][Full Text] [Related]
14. Examining variable selection methods for the predictive performance of regression models and the proportion of selected variables and selected random variables.
Kaneko H
Heliyon; 2021 Jun; 7(6):e07356. PubMed ID: 34195450
[TBL] [Abstract][Full Text] [Related]
15. Part 1. Statistical Learning Methods for the Effects of Multiple Air Pollution Constituents.
Coull BA; Bobb JF; Wellenius GA; Kioumourtzoglou MA; Mittleman MA; Koutrakis P; Godleski JJ
Res Rep Health Eff Inst; 2015 Jun; (183 Pt 1-2):5-50. PubMed ID: 26333238
[TBL] [Abstract][Full Text] [Related]
16. Comparative efficacy of three Bayesian variable selection methods in the context of weight loss in obese women.
Pesenti N; Quatto P; Colicino E; Cancello R; Scacchi M; Zambon A
Front Nutr; 2023; 10():1203925. PubMed ID: 37533570
[TBL] [Abstract][Full Text] [Related]
17. A comparative study of forest methods for time-to-event data: variable selection and predictive performance.
Liu Y; Zhou S; Wei H; An S
BMC Med Res Methodol; 2021 Sep; 21(1):193. PubMed ID: 34563138
[TBL] [Abstract][Full Text] [Related]
18. CORRELATION PURSUIT: FORWARD STEPWISE VARIABLE SELECTION FOR INDEX MODELS.
Zhong W; Zhang T; Zhu Y; Liu JS
J R Stat Soc Series B Stat Methodol; 2012 Nov; 74(5):849-870. PubMed ID: 23243388
[TBL] [Abstract][Full Text] [Related]
19. Comparison of subset selection methods in linear regression in the context of health-related quality of life and substance abuse in Russia.
Morozova O; Levina O; Uusküla A; Heimer R
BMC Med Res Methodol; 2015 Aug; 15():71. PubMed ID: 26319135
[TBL] [Abstract][Full Text] [Related]
20. A descriptive review of variable selection methods in four epidemiologic journals: there is still room for improvement.
Talbot D; Massamba VK
Eur J Epidemiol; 2019 Aug; 34(8):725-730. PubMed ID: 31161279
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]