References in zbMATH (referenced in 30 articles , 1 standard article )

Showing results 1 to 20 of 30.
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  1. Tamhane, Ajit C.: Predictive analytics: parametric models for regression and classification using R (2020)
  2. Bhaumik, Dulal K.; Nordgren, Rachel K.: Prediction and calibration for multiple correlated variables (2019)
  3. Cerqueira, Vitor; Torgo, Luís; Pinto, Fábio; Soares, Carlos: Arbitrage of forecasting experts (2019)
  4. Sergio Venturini, Mehmet Mehmetoglu: plssem: A Stata Package for Structural Equation Modeling with Partial Least Squares (2019) not zbMATH
  5. Kawasumi-Kita, Aiko; Ohtsuka, Daisuke; Morishita, Yoshihiro: Morphometric staging of organ development based on cross sectional images (2018)
  6. Mair, Patrick: Modern psychometrics with R (2018)
  7. Stéphanie Bougeard; Stéphane Dray: Supervised Multiblock Analysis in R with the ade4 Package (2018) not zbMATH
  8. Cunningham, Erica; Ciampi, Antonio; Joober, Ridha; Labbe, Aurélie: Estimating and correcting optimism bias in multivariate PLS regression: application to the study of the association between single nucleotide polymorphisms and multivariate traits in attention deficit hyperactivity disorder (2016)
  9. De Niz, Carlos; Rahman, Raziur; Zhao, Xiangyuan; Pal, Ranadip: Algorithms for drug sensitivity prediction (2016)
  10. Faisal, Muhammad; Futschik, Andreas; Hussain, Ijaz; Moemen, Mitwali Abd-El.: Choosing summary statistics by least angle regression for approximate Bayesian computation (2016)
  11. Marlies Vervloet; Henk Kiers; Wim Van den Noortgate; Eva Ceulemans: PCovR: An R Package for Principal Covariates Regression (2015) not zbMATH
  12. Martin Bilodeau; Pierre Micheaux; Smail Mahdi: The R Package groc for Generalized Regression on Orthogonal Components (2015) not zbMATH
  13. Cristóbal Fresno; Mónica Balzarini; Elmer Fernández: lmdme: Linear Models on Designed Multivariate Experiments in R (2014) not zbMATH
  14. Faraway, Julian J.: Regression for non-Euclidean data using distance matrices (2014)
  15. Shah, Jasmit; Datta, Somnath; Datta, Susmita: A multi-loss super regression learner (MSRL) with application to survival prediction using proteomics (2014)
  16. Blum, M. G. B.; Nunes, M. A.; Prangle, D.; Sisson, S. A.: A comparative review of dimension reduction methods in approximate Bayesian computation (2013)
  17. Kuhn, Max; Johnson, Kjell: Applied predictive modeling (2013)
  18. Schlittgen, Rainer: Regression analyses with R (2013)
  19. Scutari, Marco; Mackay, Ian; Balding, David: Improving the efficiency of genomic selection (2013)
  20. Cano, Emilio L.; Moguerza, Javier M.; Redchuk, Andrés: Six Sigma with R. Statistical engineering for process improvement. (2012)

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