Hi,
If you try to use any Bayesian approach then following reference might be
useful for you:
Sha, N., Tadesse, M.G. and Vannucci, M. (2006). Bayesian variable
selection............., *Bioinformatics*, *22 (18)*, 2262-2268
On Tue, Jun 15, 2010 at 4:16 PM, Brain, Phil <[log in to unmask]> wrote:
> If the below holds:-
>
> > If your number of candidate variables is large in relation to your number
> of
> > observations...
>
> you could also try Partial Least Squares as well...
>
> -----Original Message-----
> From: A UK-based worldwide e-mail broadcast system mailing list [mailto:
> [log in to unmask]] On Behalf Of Kjetil Halvorsen
> Sent: 15 June 2010 15:51
> To: [log in to unmask]
> Subject: Re: Variable selection in multiple regression
>
> Nobody seems to have suggested the lasso approach which could
> (or could not) be usefull. Search CRAN for "lasso", there are
> multipler packages.
>
> Kjetil
>
> On Tue, Jun 15, 2010 at 10:26 AM, David Wooff <[log in to unmask]>
> wrote:
> > I would read Chapter 15 of Draper & Smith, Applied Regression Analysis
> (3rd
> > ed), New York: Wiley (1998). Having done that, I'd go back and re-read
> > Chapter 11. Next, I'd read the contrary views expressed in Chapter 4.3
> of
> > Harrell, Regression Modelling Strategies, New York:Springer (2001)
> [which,
> > inter alia, expresses doubts about the bootstrapping suggested by
> Dorothy].
> > Having read those, you should be in a position to judge for yourself what
> > might be appropriate.
> >
> > If your number of candidate variables is large in relation to your number
> of
> > observations, you are pretty much walking blindfold in a minefield.
> >
> > Cheers,
> >
> > David
> >
> > On 15/06/2010 14:57, Dorothy Middleton wrote:
> >>
> >> Some of the variables are spurious. Strongly urge you to use the
> >> bootstrap to assess which variables are genuine. Or, for that matter, to
> >> use a decision tree approach in the first place.
> >>
> >> -------- Original Message --------
> >> Subject: [SPAM] Variable selection in multiple regression
> >> From: Noori Akhtar-Danesh<[log in to unmask]>
> >> Date: Mon, June 14, 2010 1:20 pm
> >> To: [log in to unmask]
> >>
> >> Dear List members,
> >>
> >> I would appreciate if I could be directed to a reference (journal
> >> article or book) for the following approach of variable selection in
> >> multiple regression. This approach seems to be quite common in some area
> >> of research but I have not seen any reference for it.
> >>
> >>
> >>
> >> Approach: When there are many explanatory variables, first each
> >> explanatory variable is regressed individually against the outcome
> >> (dependent) variable. Then, for each variable, if the p-value is, say
> >> <=.20, it is chosen to be included in a multiple regression model (this
> >> stage may be called the screening phase). Next, these selected variables
> >> are used in a multiple regression approach to come up with a final model
> >> (the multiple regression can be, I guess, conducted using a backward,
> >> forward, or stepwise approach).
> >>
> >> Many thanks in advance,
> >>
> >> Noori
> >>
> >> ============================
> >> Noori Akhtar-Danesh, PhD
> >> Faculty of Health Sciences,
> >> McMaster University,
> >> 1200 Main St. West, Room 3N28B
> >> Hamilton, ON L8N 3Z5,CANADA
> >> Tel: 905-525-9140 Ext. 22297& 22725
> >> Fax: 905-521-8834
> >> http://www.fhs.mcmaster.ca/ceb/faculty_member_akhtar-danesh.htm
> >> =============================
> >>
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> >
> > --
> > David Wooff,
> > Director, Statistics and Mathematics Consultancy Unit,
> > & Senior Lecturer in Statistics, University of Durham.
> > Department of Mathematical Sciences, Science Laboratories,
> > South Road, Durham, DH1 3LE, UK. email: [log in to unmask]
> > Tel. 0191 334 3121, Fax 0191 334 3051.
> > Web: http://maths.dur.ac.uk/stats/people/daw/daw.html
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Hasinur
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