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- Path: sparky!uunet!mcsun!uknet!warwick!strgh
- From: strgh@warwick.ac.uk (J E H Shaw)
- Newsgroups: sci.math.stat
- Subject: Re: 1) Bootstrap 2) Standard deviation
- Message-ID: <5j9nb03z@csv.warwick.ac.uk>
- Date: 17 Aug 92 17:27:31 GMT
- References: <1992Aug14.193227.9029@spss.com>
- Sender: news@csv.warwick.ac.uk (Network news)
- Organization: Computing Services, Warwick University, UK
- Lines: 27
- Nntp-Posting-Host: clover
-
- 1) Most statistical methods attempt to generalise from a set of data
- by making assumptions about the underlying population (e.g. `Normal
- with unknown mean & variance'), estimating the unknown parameters
- (here mean & variance) and using the properties of the fitted
- distribution (here Normal) to, for example, predict future data.
-
- `Bootstrapping' goes straight for the jugular (bypassing the brain
- almost entirely :-) and uses the observed data distribution as an
- estimate of the population distribution (i.e. probability mass 1/N
- at each of the N data points). Properties of the assumed underlying
- population are inferred from the corresponding data properties.
- (And if it's too hard to do analytically, simulate by generating
- further `bootstrap samples' from the data distribution).
-
- Bootstrapping is the ultimate in maximum likelihood estimation
- (attempts to produce a Bayesian analogue are, IMHO, weird).
-
- 2) Biasedness of the SD is hardly mentioned because it's too hard.
- (And in almost any practical analysis, biasedness is the least
- of my worries anyway).
-
- -- Ewart Shaw
- --
- J.E.H.Shaw, Department of Statistics, | JANET: strgh@uk.ac.warwick.cu
- University of Warwick, | BITNET: strgh%uk.ac.warwick.cu@UKACRL
- Coventry CV4 7AL, U.K. | PHONE: +44 203 523069
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