Description

• Title:
  Asymptotic results for sliced inverse regression
• Creator:
  Kötter, Thomas
• Contributor:
  Humboldt-Universität zu Berlin
• Description:
  It is well known that nonparametric regression techniques do not have good performance in high dimensional regression. However nonparametric regression is successful in one- or low-dimensional regression problems and is much more flexible than the parametric alternative. Hence, for high dimensional regression tasks one would like to reduce the regressor space to a lower dimension and then use nonparametric methods for curve estimation. A possible dimension reduction approach is Sliced Inverse Regression (L i 1991). It allows to find a base of a subspace in the regressor space which still carries important information for the regression. The vectors spanning this subspace are found with a technique similar to Principal Component Analysis and can be judged with the eigenvalues that belong to these vectors. Asymptotic and simulation results for the eigenvalues and vectors are presented.;
  Zadanie pt. „Digitalizacja i udostępnienie w Cyfrowym Repozytorium Uniwersytetu Łódzkiego kolekcji czasopism naukowych wydawanych przez Uniwersytet Łódzki” nr 885/P-DUN/2014 dofinansowane zostało ze środków MNiSW w ramach działalności upowszechniającej naukę.
• Object availability:
  free access
• Date:
  2015.01.20;
  1997
• Type:
  Article
• Language:
  English
• Format:
  application/pdf
• Publisher:
  Wydawnictwo Uniwersytetu Łódzkiego
• Relation:
  Acta Universitatis Lodziensis. Folia Oeconomica;Nr141/1997
• Subject:
  dimension reduction;
  inverse regression;
  linear projections
• Identifier:
  0208-6018;
  http://hdl.handle.net/11089/6244
• Data provider:
  Repozytorium Uniwersytetu Łódzkiego
• Can I use it?:
  seek permission
• Type:
  image