Dear Manuel (and list).
Thank you for doing so much the work to answer my question.
Much of the theory is beyond me at present although I plan on learning
more about robust methods with time along with many other fields
I have to learn. I would like to rephrase the question- your response
deals mainly with power. What I need to know about more is Tyoe I
error. Do robust methods ever increase type I -error lead to a greater
number of false positives, for n=5 than does the classical t-test?
In particular does the Huber with k=1.345? I have a particular
reason for returning to this method even though it is apparently
no longer the robust method of choice. A collaborator used it to find
significance where the classical t-test did not, and I am wondering
which test to believe.
Thanks and best wishes,
Rich
On Jun 17, 2011, at 11:55 AM, Manuel Koller wrote:
Dear Richard,
Since we did not quite cover your specific case, I ran another small
simulation. See the attached file. It is basically the simulation
study of our paper, but for models having only an intercept. I hope I
did not overlook anything when I did this. There was a lot going on
today... I apologize for the overloaded plots. I guess Figures 4, 7
and 8 are the most interesting figures.
As Rand already stated, the asymmetric error distributions are a
problem: all the methods perform quite badly. Otherwise, the levels of
the tests are pretty much ok (even for OLS, i.e., t-test). But of
course, the power will be pretty bad. In numbers, for n = 5 you will
have approximately the correct level (+/- 2%), but a power of about
40% only for an effect size of 1 (10% for an effect size of 0.4). And
this does not really depend on which method you are using.
To conclude, I would recommend to use lmrob from robustbase with the
argument setting="KS2011".
I hope this helps,
Manuel
On Thu, Jun 16, 2011 at 8:19 PM, Richard Friedman
<fried...@cancercenter.columbia.edu> wrote:
Rand,
Thanks, I know very little about robust methods. I am
interested in
whether rlm can be used in its default
state or if I have to tearn much more to do use the methods
correctly.
Best wishes,
Rich
On Jun 16, 2011, at 2:14 PM, Rand Wilcox wrote:
When dealing with M-estimators and the goal is to compute confidence
intervals, one thing you have to be careful about is skewed
distributions.
Have not encountered any non-bootstrap method that performs well in
simulations where the confidence interval is based on an estimate
of the
standard error. Just how symmetric the distribution must be seems
unclear.
What works better is a percentile bootstrap method, even with
fairly small
sample sizes. This is why the methods in my book focus on bootstrap
techniques when dealing with M-estimators.
However, have not yet seen the Koller and Stahel paper. Maybe this
problem
has been addressed.
Rand
Rand Wilcox
Professor
Dept of Psychology
USC
Los Angeles, CA 90089-1061
FAX: 213-746-9082
For information about statistics books and software, see
http://www-rcf.usc.edu/~rwilcox/
as well as
http://college.usc.edu/labs/rwilcox/home
----- Original Message -----
From: Richard Friedman <fried...@cancercenter.columbia.edu>
Date: Thursday, June 16, 2011 9:02 am
Subject: Re: [RsR] minimum sample size for the robust counterpart
of the
t-test #2
To: Rand Wilcox <rwil...@usc.edu>, r-sig-robust@r-project.org
Dear Rand (and List),
I read the relevant sections of your book and while
informative it
did not answer my question
directly as best I can see. I will restate the question more
explicitly:
A robust analog of the two sample t-test is performed with the rlm
function with the default parameters of
the Huber method with K=1.345. Is there a minimum sample size for
which it should be trusted?
are 5 samples enough? 10 samples?
If this question does not have a simple answer please let me know.
Thanks and best wishes,
Rich
On Jun 15, 2011, at 3:19 PM, Rand Wilcox wrote:
There is general information about sample sizes and p-values, when
using robust analogs of t, in my 2005 book (Introduction to Robust
Estimation and Hypothesis Testing, Academic Press) .
(A third edition will be out early in 2012. )
Hope this helps.
Rand
Rand Wilcox
Professor
Dept of Psychology
USC
Los Angeles, CA 90089-1061
FAX: 213-746-9082
For information about statistics books and software, see
http://www-rcf.usc.edu/~rwilcox/
as well as
http://college.usc.edu/labs/rwilcox/home
----- Original Message -----
From: Richard Friedman <fried...@cancercenter.columbia.edu>
Date: Wednesday, June 15, 2011 12:11 pm
Subject: [RsR] minimum sample size for the robust counterpart of
the t-test
To: r-sig-robust@r-project.org
Dear List,
I am a beginner in the use of robust methods. Is there a
minimum
sample size
for which the robust analog of a two sample t-test using rlm with
default parameters and categorical
explanatory variables may be trusted to yield reliable p-values?
Is so, can you please point me at a reference which treats this
problem.
Thanks and best wishes,
Rich
------------------------------------------------------------
Richard A. Friedman, PhD
Associate Research Scientist,
Biomedical Informatics Shared Resource
Herbert Irving Comprehensive Cancer Center (HICCC)
Lecturer,
Department of Biomedical Informatics (DBMI)
Educational Coordinator,
Center for Computational Biology and Bioinformatics (C2B2)/
National Center for Multiscale Analysis of Genomic Networks
(MAGNet)
Room 824
Irving Cancer Research Center
Columbia University
1130 St. Nicholas Ave
New York, NY 10032
(212)851-4765 (voice)
fried...@cancercenter.columbia.edu
http://cancercenter.columbia.edu/~friedman/
I am a Bayesian. When I see a multiple-choice question on a test
and I don't
know the answer I say "eeney-meaney-miney-moe".
Rose Friedman, Age 14
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--
Manuel Koller <kol...@stat.math.ethz.ch>
Seminar für Statistik, HG G 18, Rämistrasse 101
ETH Zürich 8092 Zürich SWITZERLAND
phone: +41 44 632-4673 fax: ...-1228
http://stat.ethz.ch/people/kollerma/
<intercept_only.pdf>
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