ProDenICA

R package ProDenICA: Product Density Estimation for ICA using tilted Gaussian density estimates. A direct and flexible method for estimating an ICA model. This approach estimates the densities for each component directly via a tilted gaussian. The tilt functions are estimated via a GAM poisson model. Details can be found in ”Elements of Statistical Learning (2nd Edition)” Section 14.7.4

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References in zbMATH (referenced in 8 articles )

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  1. Klaus Nordhausen, Markus Matilainen, Jari Miettinen, Joni Virta, Sara Taskinen: Dimension Reduction for Time Series in a Blind Source Separation Context Using R (2021) not zbMATH
  2. Jin, Ze; Risk, Benjamin B.; Matteson, David S.: Optimization and testing in linear non-Gaussian component analysis (2019)
  3. Risk, Benjamin B.; Matteson, David S.; Ruppert, David: Linear non-Gaussian component analysis via maximum likelihood (2019)
  4. Jari Miettinen and Klaus Nordhausen and Sara Taskinen: Blind Source Separation Based on Joint Diagonalization in R: The Packages JADE and BSSasymp (2017) not zbMATH
  5. Zanini, Paolo; Shen, Haipeng; Truong, Young: Understanding resident mobility in Milan through independent component analysis of Telecom Italia mobile usage data (2016)
  6. Risk, Benjamin B.; Matteson, David S.; Ruppert, David; Eloyan, Ani; Caffo, Brian S.: An evaluation of independent component analyses with an application to resting-state fMRI (2014)
  7. Samworth, Richard J.; Yuan, Ming: Independent component analysis via nonparametric maximum likelihood estimation (2012)
  8. Hastie, Trevor; Tibshirani, Robert; Friedman, Jerome: The elements of statistical learning. Data mining, inference, and prediction (2009)