<?xml version="1.0" encoding="utf-8" standalone="yes"?>
<item>
  <id>06014712</id>
  <dt>j</dt>
  <an>06014712</an>
  <augroup>
    <au>Ruymgaart, Frits</au>
    <au>Wang, Jing</au>
    <au>Wei, Shih-Hsuan</au>
    <au>Yu, Li</au>
  </augroup>
  <ti>Some asymptotic theory for functional regression and classification.</ti>
  <so>Adv. Decis. Sci. 2011, Article ID 485974, 22 p. (2011).</so>
  <py>2011</py>
  <pu>Hindawi Publishing Corporation, New York, NY</pu>
  <lagroup>
    <la>EN</la>
  </lagroup>
  <ccgroup>
  </ccgroup>
  <utgroup>
  </utgroup>
  <cigroup>
  </cigroup>
  <ligroup>
    <li>doi:10.1155/2011/485974</li>
  </ligroup>
  <abgroup>
    <ab>Summary: Exploiting an expansion for analytic functions of operators, the asymptotic distribution of an estimator of the functional regression parameter is obtained in a rather simple way; the result is applied to testing linear hypotheses. The expansion is also used to obtain a quick proof for the asymptotic optimality of a functional classification rule, given Gaussian populations.</ab>
    <rv></rv>
  </abgroup>
</item>