Investigating Variable Selection Techniques Under Missing Data: A Simulation Study
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-55548-0_11
Reference27 articles.
1. Amene, E., Hanson, L. A., Zahn, E. A., Wild, S. R., & Döpfer, D. (2016). Variable selection and regression analysis for the prediction of mortality rates associated with foodborne diseases. Epidemiology and Infection, 144(9), 1959–1973. https://doi.org/10.1017/S0950268815003234.
2. Bain, C., Shi, D., Boness, C. L., & Loeffelman, J. (2023). A simulation study comparing the use of supervised machine learning variable selection methods in the psychological sciences. https://doi.org/10.31234/osf.io/y53t6 . Retrieved from https://psyarxiv.com/y53t6/.
3. Blanca, M. J., Alarcón, R., & Bono, R. (2018). Current practices in data analysis procedures in psychology: What has changed? Frontiers in Psychology, 9(DEC), 2558. https://doi.org/10.3389/FPSYG.2018.02558.
4. Bowns, R., Loeffelman, J. E., Steinley, D., & Sher, K. J. (2022). A brief young adult alcohol problems screening test: Short form development using combinatorics. Journal of American College Health, 1–7. https://doi.org/10.1080/07448481.2022.2095870.
5. Centofanti, F., Fontana, M., Lepore, A., & Vantini, S. (2022). Smooth lasso estimator for the function-on-function linear regression model. Computational Statistics & Data Analysis, 176, 107556. https://doi.org/10.1016/j.csda.2022.107556.
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