I suggest
The Discarding of Variables in Multivariate Analysis
E. M. L. Beale; M. G. Kendall; D. W. Mann
Biometrika, Vol. 54, No. 3/4. (Dec., 1967), pp. 357-366
I thought there was no need to develop the subject since then, although it was expressed more simply in Technometrics
Regressions by Leaps and Bounds.
Technometrics | February 01, 2000 | Furnival, George M.; Wilson Jr., Robert W
..
Tony Greenfield
----- Original Message -----
From: "Lynd Bacon" <[log in to unmask]>
To: <[log in to unmask]>
Sent: Saturday, June 19, 2010 5:19 PM
Subject: Re: Variable selection in multiple regression
> 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
>>
>> You may leave the list at any time by sending the command
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>>
>
>
> --
> Lynd Bacon
> Loma Buena Associates
> Woodside CA USA UTC -08:00
> Lat 37.4184, Long -122.3207
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