You might see also Chapter 3, Section 5, "Variable Selection," in
Marin, J. & Robert, C. "Bayesian Core: A Practical Approach to
Computational Bayesian Statistics." Springer, 2007.
On 06/17/2010 04:09 AM, Hasinur Rahaman Khan wrote:
> 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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--
Lynd Bacon
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