Central limit theorems for the Lp-error of smooth isotonic estimators

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Abstract

We investigate the asymptotic behavior of the Lp-distance between
a monotone function on a compact interval and a smooth estimator
of this function. Our main result is a central limit theorem for the Lp-error
of smooth isotonic estimators obtained by smoothing a Grenander-type
estimator or isotonizing the ordinary kernel estimator. As a preliminary result
we establish a similar result for ordinary kernel estimators. Our results
are obtained in a general setting, which includes estimation of a monotone
density, regression function and hazard rate. We also perform a simulation
study for testing monotonicity on the basis of the L2-distance between the
kernel estimator and the smoothed Grenander-type estimator.
Original languageEnglish
Pages (from-to)1031-1098
Number of pages68
JournalElectronic Journal of Statistics
Volume13
Issue number1
DOIs
Publication statusPublished - 2019

Keywords

  • Kernel estimator
  • Lp loss
  • central limit theorem
  • smoothed Grenander-type estimator
  • isotonized kernel estimator
  • boundary corrections
  • Hellinger loss
  • testing monotonicity

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