Statistical Methods with Applications in Data Mining: A Review of the Most Recent Works
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- Matias D. Cattaneo & Michael Jansson & Xinwei Ma, 2020.
"Simple Local Polynomial Density Estimators,"
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- Gao Wang & Abhishek Sarkar & Peter Carbonetto & Matthew Stephens, 2020. "A simple new approach to variable selection in regression, with application to genetic fine mapping," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(5), pages 1273-1300, December.
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- Mudong Zeng & Yujie Liao & Runze Li & Agus Sudjianto, 2022. "Local Linear Approximation Algorithm for Neural Network," Mathematics, MDPI, vol. 10(3), pages 1-22, February.
- Babacar Gaye & Dezheng Zhang & Aziguli Wulamu, 2021. "Improvement of Support Vector Machine Algorithm in Big Data Background," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-9, June.
- Tingyou Zhou & Liping Zhu & Chen Xu & Runze Li, 2020. "Model-Free Forward Screening Via Cumulative Divergence," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 115(531), pages 1393-1405, July.
- Kwon, Sunghoon & Lee, Sangin & Kim, Yongdai, 2015. "Moderately clipped LASSO," Computational Statistics & Data Analysis, Elsevier, vol. 92(C), pages 53-67.
- Bethany Lusch & J. Nathan Kutz & Steven L. Brunton, 2018. "Deep learning for universal linear embeddings of nonlinear dynamics," Nature Communications, Nature, vol. 9(1), pages 1-10, December.
- Andrew Gelman & Ben Goodrich & Jonah Gabry & Aki Vehtari, 2019. "R-squared for Bayesian Regression Models," The American Statistician, Taylor & Francis Journals, vol. 73(3), pages 307-309, July.
- Quentin F. Gronau & Alexander Ly & Eric-Jan Wagenmakers, 2020. "Informed Bayesian t-Tests," The American Statistician, Taylor & Francis Journals, vol. 74(2), pages 137-143, April.
- Jianqing Fan & Jinchi Lv, 2008. "Sure independence screening for ultrahigh dimensional feature space," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(5), pages 849-911, November.
- Qiang Sun & Wen-Xin Zhou & Jianqing Fan, 2020. "Adaptive Huber Regression," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 115(529), pages 254-265, January.
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- Adelaida Ojeda-Beltrán & Andrés Solano-Barliza & Wilson Arrubla-Hoyos & Danny Daniel Ortega & Dora Cama-Pinto & Juan Antonio Holgado-Terriza & Miguel Damas & Gilberto Toscano-Vanegas & Alejandro Cama-, 2023. "Characterisation of Youth Entrepreneurship in Medellín-Colombia Using Machine Learning," Sustainability, MDPI, vol. 15(13), pages 1-19, June.
- Khishigsuren Davagdorj & Ling Wang & Meijing Li & Van-Huy Pham & Keun Ho Ryu & Nipon Theera-Umpon, 2022. "Discovering Thematically Coherent Biomedical Documents Using Contextualized Bidirectional Encoder Representations from Transformers-Based Clustering," IJERPH, MDPI, vol. 19(10), pages 1-21, May.
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data mining; state of the art; statistical methods; machine learning; statistical learning; deep learning;All these keywords.
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