Probability Density Estimation

  • Chong Gu
Part of the Springer Series in Statistics book series (SSS, volume 297)


For observational data, (1.5) of Example 1.2 defines penalized likelihood density estimation. Of interest are the selection of smoothing parameters, the computation of the estimates, and the asymptotic behavior of the estimates. Variants of (1.5) are also called for to accommodate samples subject to selection bias and samples from conditional distributions.


Smoothing Parameter Unity Constraint Conditional Density Newton Iteration Reproduce Kernel Hilbert Space 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Chong Gu
    • 1
  1. 1.Department of StatisticsPurdue UniversityWest LafayetteUSA

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