Parametric/Nonparametric bootstrap  is standard terminology, used in 
the books by Efrom/Tibshirani, Davison/Hinkley, Chernick, Shao/Tu, 
and so on. It's not new, it's by now 20 years old. The
parametric bootstrap is already in Efron, 1979, it's equally 
traditional as the nonparametric
one. Both are form of MC simulation (or both are not).

At 8:12 PM -0600 12/8/99, Rich Strauss wrote:
>At 12:04 PM 12/8/99 -0500, Rich Ulrich wrote:
>-- snip --
>  >Similarly, bootstrapping is a method of "robust variance estimation"
>  >but it does not change the metric like a power transformation does, or
>  >abandon the metric like a rank-order transformation does.  If it were
>  >proper  terminology to say randomization is nonparametric, you would
>  >probably want to say bootstrapping is nonparametric, too.  (I think
>  >some people have done so; but it is not widespread.)
>In my fields of interest (ecology and evolutionary biology), it is becoming
>increasing common to refer to two "kinds" of bootstrapping: nonparametric
>bootstrapping, in which replicate samples are drawn randomly with
>replacement from the original sample; and parametric bootstrapping, in
>which samples are drawn randomly from a (usually normal) distribution
>having the same mean and variance as the original sample.  The former is
>bootstrapping in the traditional sense, of course, while the latter is a
>form of Monte Carlo simulation.  Unfortunately, the new terminology seems
>to be spreading rapidly.
>Rich Strauss
>Dr Richard E Strauss
>Biological Sciences
>Texas Tech University
>Lubbock TX 79409-3131
>Phone: 806-742-2719
>Fax: 806-742-2963

Jan de Leeuw; Professor and Chair, UCLA Department of Statistics;
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