• DocumentCode
    847806
  • Title

    Undersmoothed Kernel Entropy Estimators

  • Author

    Paninski, Liam ; Yajima, Masanao

  • Author_Institution
    Dept. of Stat., Columbia Univ., New York, NY
  • Volume
    54
  • Issue
    9
  • fYear
    2008
  • Firstpage
    4384
  • Lastpage
    4388
  • Abstract
    We develop a ldquoplug-inrdquo kernel estimator for the differential entropy that is consistent even if the kernel width tends to zero as quickly as 1/N, where N is the number of independent and identically distributed (i.i.d.) samples. Thus, accurate density estimates are not required for accurate kernel entropy estimates; in fact, it is a good idea when estimating entropy to sacrifice some accuracy in the quality of the corresponding density estimate.
  • Keywords
    differential equations; entropy; differential entropy; independent and identically distributed samples; undersmoothed kernel entropy estimators; Density measurement; Engineering profession; Entropy; Estimation error; Histograms; Kernel; Length measurement; Mutual information; Probability distribution; Smoothing methods; Approximation theory; bias; consistency; density estimation; distribution-free bounds;
  • fLanguage
    English
  • Journal_Title
    Information Theory, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0018-9448
  • Type

    jour

  • DOI
    10.1109/TIT.2008.928251
  • Filename
    4608988
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=847806