Estimation of nuisance parameters for inference based on least absolute deviations

Wojciech Niemiro

Applicationes Mathematicae (1995)

  • Volume: 22, Issue: 4, page 515-529
  • ISSN: 1233-7234

Abstract

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Statistical inference procedures based on least absolute deviations involve estimates of a matrix which plays the role of a multivariate nuisance parameter. To estimate this matrix, we use kernel smoothing. We show consistency and obtain bounds on the rate of convergence.

How to cite

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Niemiro, Wojciech. "Estimation of nuisance parameters for inference based on least absolute deviations." Applicationes Mathematicae 22.4 (1995): 515-529. <http://eudml.org/doc/219111>.

@article{Niemiro1995,
abstract = {Statistical inference procedures based on least absolute deviations involve estimates of a matrix which plays the role of a multivariate nuisance parameter. To estimate this matrix, we use kernel smoothing. We show consistency and obtain bounds on the rate of convergence.},
author = {Niemiro, Wojciech},
journal = {Applicationes Mathematicae},
keywords = {least absolute deviations; kernel density/regression estimation; regression estimation; density estimation; multivariate nuisance parameter; kernel smoothing; consistency; bounds on the rate of convergence},
language = {eng},
number = {4},
pages = {515-529},
title = {Estimation of nuisance parameters for inference based on least absolute deviations},
url = {http://eudml.org/doc/219111},
volume = {22},
year = {1995},
}

TY - JOUR
AU - Niemiro, Wojciech
TI - Estimation of nuisance parameters for inference based on least absolute deviations
JO - Applicationes Mathematicae
PY - 1995
VL - 22
IS - 4
SP - 515
EP - 529
AB - Statistical inference procedures based on least absolute deviations involve estimates of a matrix which plays the role of a multivariate nuisance parameter. To estimate this matrix, we use kernel smoothing. We show consistency and obtain bounds on the rate of convergence.
LA - eng
KW - least absolute deviations; kernel density/regression estimation; regression estimation; density estimation; multivariate nuisance parameter; kernel smoothing; consistency; bounds on the rate of convergence
UR - http://eudml.org/doc/219111
ER -

References

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