References in zbMATH (referenced in 1188 articles , 2 standard articles )

Showing results 21 to 40 of 1188.
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  1. Angelelli, Mario: Complexity reduction for sign configurations through the KP II equation and its information-theoretic aspects (2019)
  2. Arakelian, Veni; Dellaportas, Petros; Savona, Roberto; Vezzoli, Marika: Sovereign risk zones in Europe during and after the debt crisis (2019)
  3. Arashi, M.; Roozbeh, Mahdi: Some improved estimation strategies in high-dimensional semiparametric regression models with application to riboflavin production data (2019)
  4. Aravkin, Aleksandr Y.; Bottegal, Giulio; Pillonetto, Gianluigi: Boosting as a kernel-based method (2019)
  5. Azencott, Robert; Muravina, Viktoria; Hekmati, Rasoul; Zhang, Wei; Paldino, Michael: Automatic clustering in large sets of time series (2019)
  6. Bacinello, Anna Rita; Zoccolan, Ivan: Variable annuities with a threshold fee: valuation, numerical implementation and comparative static analysis (2019)
  7. Barrera, David; Gobet, Emmanuel: Quantitative bounds for concentration-of-measure inequalities and empirical regression: the independent case (2019)
  8. Bauer, Benedikt; Heimrich, Felix; Kohler, Michael; Krzyżak, Adam: On estimation of surrogate models for multivariate computer experiments (2019)
  9. Bax, Eric; Weng, Lingjie; Tian, Xu: Speculate-correct error bounds for (k)-nearest neighbor classifiers (2019)
  10. Bhadra, Anindya; Datta, Jyotishka; Li, Yunfan; Polson, Nicholas G.; Willard, Brandon: Prediction risk for the horseshoe regression (2019)
  11. Bhadra, Anindya; Datta, Jyotishka; Polson, Nicholas G.; Willard, Brandon: Lasso meets horseshoe: a survey (2019)
  12. Biau, G.; Cadre, B.; Rouvière, L.: Accelerated gradient boosting (2019)
  13. Bien, Jacob: Graph-guided banding of the covariance matrix (2019)
  14. Boonstra, Philip S.; Barbaro, Ryan P.; Sen, Ananda: Default priors for the intercept parameter in logistic regressions (2019)
  15. Brunori, Paolo; Peragine, Vito; Serlenga, Laura: Upward and downward bias when measuring inequality of opportunity (2019)
  16. Cerulli, Giovanni: Data-driven sensitivity analysis for matching estimators (2019)
  17. Chang, Bo; Joe, Harry: Prediction based on conditional distributions of vine copulas (2019)
  18. Chang, Haibin; Zhang, Dongxiao: Machine learning subsurface flow equations from data (2019)
  19. Chen, Li-Pang: Book review of: Mehryar Mohri et al., Foundations of machine learning. 2nd ed. (2019)
  20. Chen, Li-Pang; Yi, Grace Y.; Zhang, Qihuang; He, Wenqing: Multiclass analysis and prediction with network structured covariates (2019)

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