[ABE-L] rede da ASA
pam em ime.usp.br
pam em ime.usp.br
Qua Nov 4 09:09:44 -03 2015
Caros leitores:
A American Statistical Association (ASA) não mantinha rede para
eventuais comunicações entre associados, como a ABE tem. Provavelmente
porque com cerca de 15000 associados, a coisa poderia facilmente virar
bagunça.
Recentemente foi criada o que se chama "ASA Connect Digest", e abaixo
reproduzo uma edição de 3/11/2015.
Os assuntos discutidos são de natureza estatística somente. Por esta
última fiquei sabendo que existe algo chamado "redundancy analysis"!
Pedro
=====================================
Please Do Not Forward, use the options to the right to forward or reply.
Email digest for the ASA Connect egroup.
------------------------------------------------------------------------------------------------------------
1. RE: What's your Twitter handle?
2. RE: Principal Components Analysis
3. RE: What's your Twitter handle?
4. RE: Principal Components Analysis
5. RE: Principal Components Analysis
6. RE: Principal Components Analysis
7. RE: Principal Components Analysis
8. Tenure track faculty position at Rice University
9. RE: Principal Components Analysis
------------------------------------------------------------------------------------------------------------
1.From: Michelle Wiest
Posted: Tuesday November 3, 2015 9:15 AM
Subject: RE: What's your Twitter handle?
Message:
@MichelleWiest ...I'm a statistician/epidemiologist at the University
of Idaho. Happy to connect!
------------------------------
Michelle Wiest
Assistant Professor
University of Idaho
-------------------------------------------------------------------------
Original Message:
Sent: 11-02-2015 18:09
From: Lara Harmon
Subject: What's your Twitter handle?
Are you on Twitter? What's your handle? No pressure to share, of
course, but if you'd like to--please do!
(I'm @Amstat_Lara. Other ASA-related accounts include @Amstatnews,
@ASA_SciPol, @Ron_Wasserstein, @Amstat_Meg, @Amstat_Sara,
@Amstat_Megan, and @AmyFarrisASA. Feel free to follow any and/or all
of us!)
------------------------------
Lara Harmon
Marketing and Online Community Coordinator
American Statistical Association
------------------------------
2.From: Simon Blomberg
Posted: Tuesday November 3, 2015 9:15 AM
Subject: RE: Principal Components Analysis
Message:
I would like to second Pieter Kroonenberg's suggestion. Redundancy
analysis is the multivariate version of regression, where you want to
regress a set of response variables onto a set of predictor variables.
This seems to be what you want? An alternative would be canonical
correlation analysis, if you are just interested in the correlations,
rather than the regression coefficients.
Cheers,
Simon.
------------------------------
Simon Blomberg
Lecturer and Consultant Statistician
University of Queensland School of Biological Sciences
-------------------------------------------------------------------------
Original Message:
Sent: 11-02-2015 01:53
From: Pieter Kroonenberg
Subject: Principal Components Analysis
Dear Ms. Landon,
What you would like to do is called redundancy analysis, it is a
special case of canonical correlation analysis. In the latter you look
for linear combinations of each set of variables such that the
correlation among the linear combinations from both set is high as
possible, in the former case the criterion is that the linear
combination of the response set can be best predicted by the linear
combination of the predictor set. Van den Wollenberg in Psychometrika
deals with redundancy analysis. The latter seems to be due to
Hotelling. A net search will give all the relevant references.
------------------------------
Pieter Kroonenberg
Leiden University
3.From: Peter Flom
Posted: Tuesday November 3, 2015 9:15 AM
Subject: RE: What's your Twitter handle?
Message:
I am on Twitter. My handle is @PeterFlomStat
Peter
------------------------------
Peter Flom
Peter Flom Consulting
-------------------------------------------------------------------------
Original Message:
Sent: 11-02-2015 18:09
From: Lara Harmon
Subject: What's your Twitter handle?
Are you on Twitter? What's your handle? No pressure to share, of
course, but if you'd like to--please do!
(I'm @Amstat_Lara. Other ASA-related accounts include @Amstatnews,
@ASA_SciPol, @Ron_Wasserstein, @Amstat_Meg, @Amstat_Sara,
@Amstat_Megan, and @AmyFarrisASA. Feel free to follow any and/or all
of us!)
------------------------------
Lara Harmon
Marketing and Online Community Coordinator
American Statistical Association
------------------------------
4.From: R. Cook
Posted: Tuesday November 3, 2015 9:15 AM
Subject: RE: Principal Components Analysis
Message:
I generally stay away from principal components in such situations
because of method can miss relevant relationships. Partial least
squares is not uniquely defined when regressing multiple responses on
multiple predictors, as there are different algorithms that produce
different answers. Canonical correlation analysis could be useful.
An appropriate choice would seem to depend on the reason(s) for
needing to use dimension reduction in the first place. For instance,
additional issues arise if it's needed to compensate for a small
sample size.
------------------------------
R. Cook
University of Minnesota
-------------------------------------------------------------------------
Original Message:
Sent: 10-30-2015 09:27
From: Linda Landon
Subject: Principal Components Analysis
Everyone,
I'm about to make my first foray into principal component analysis
since graduate school. I'm working on a multivariate regression
problem that has multiple independent variables and multiple dependent
variables. I'm wondering if it would be possible to perform
multivariate regression by using BOTH the principal components
representing dimensions in the set of independent variables as
independent variables and the principal components representing
dimensions in the set of dependent variables as dependent variables
in the same regression model. However, I'm unsure if this application
is statistically valid.
The common examples found in texts and in the peer-reviewed literature
when PCA is applied prior to multivariate regression are
(1) multiple independent variables to find the principal components
in the independent variables and then one or more components are used
as independent variables in a regression against a single dependent
variable or
(2) to multiple dependent variables to find the principal
components in the dependent variables and then a single component is
used as a dependent variables in a regression against a more or more
independent variables.
However, I would like to know if it is possible to perform one
multivariate regression analysis on two sets of principal components,
one set of components representing the dimensions in the independent
variables and the other set of components representing the dimensions
in the dependent variables. In my rather limited literature search,
I haven't found an example of this application in the peer-reviewed
literature, which could indicate that it is not statistically valid.
Further, other sources of information don't explicitly address this
application.
Thanks in advance for your insight.
Linda
Linda A. Landon, PhD, ELS
President
Research Communiqu
Jefferson City, MO
Email: LandonPhD em ResearchCommunique.com <LandonPhD em ResearchCommunique.com>
Phone: 573-797-4517
Central Standard Time ( CST ) = GMT-6 (November February)
Central Daylight Time ( CDT ) = GMT-5 (March October)
5.From: Larry Price
Posted: Tuesday November 3, 2015 9:15 AM
Subject: RE: Principal Components Analysis
Message:
I agree with Ken Burnham, canonical correlation analysis will allow
you to answer your research questions. Canonical correlation is very
flexible yet underutilized.
Larry Price
Texas State University
------------------------------
Larry Price
Director/Professor - Interdisciplinary Initiative for Research
Texas State University
-------------------------------------------------------------------------
Original Message:
Sent: 11-02-2015 01:04
From: Kenneth Burnham
Subject: Principal Components Analysis
I think canonical correlation is the method you want.
------------------------------
Kenneth Burnham
Colorado State University
6.From: Thaddeus Tarpey
Posted: Tuesday November 3, 2015 10:09 AM
Subject: RE: Principal Components Analysis
Message:
This is a multivariate multiple regression problem. An approach to
this problem that might be useful and insightful is to perform a
reduced rank regression. The book by Reinsel and Velu might be
helpful for this approach.
Thad Tarpey
------------------------------
Thaddeus Tarpey
Wright State University
-------------------------------------------------------------------------
Original Message:
Sent: 11-02-2015 01:53
From: Pieter Kroonenberg
Subject: Principal Components Analysis
Dear Ms. Landon,
What you would like to do is called redundancy analysis, it is a
special case of canonical correlation analysis. In the latter you look
for linear combinations of each set of variables such that the
correlation among the linear combinations from both set is high as
possible, in the former case the criterion is that the linear
combination of the response set can be best predicted by the linear
combination of the predictor set. Van den Wollenberg in Psychometrika
deals with redundancy analysis. The latter seems to be due to
Hotelling. A net search will give all the relevant references.
------------------------------
Pieter Kroonenberg
Leiden University
7.From: Donald Myers
Posted: Tuesday November 3, 2015 1:37 PM
Subject: RE: Principal Components Analysis
Message: There is also also an old paper in SIAM Review on Linear Dependency
Analysis and a FORTRAN code
Donald E Myers
------Original Message------
Dear Dr. Landon,
I would recommend reading up on CANONICAL CORRELATION
analysis. This is essentially a technique that simultaneously
explores & tests the relations of a SET of variables (e.g., y1, y2,
y3, etc.) versus a SET of variables (e.g., x1, x2, x3, etc.). For
example, check the internet for SAS stats documentation re the CANCORR
procedure ("Proc Cancorr").
Joseph J. Locascio, Ph.D.
------------------------------
Joseph J. Locascio, Ph.D.,
Assistant Professor of Neurology,
Harvard Medical School,
and Statistician,
Memory and Movement Disorders Units,
Massachusetts Alzheimer's Disease Research Center,
Neurology Dept.,
Massachusetts General Hospital (MGH),
Boston, Massachusetts 02114
Phone: (617) 724-7192
Email: JLocascio em partners.org
------------------------------
8.From: Marina Vannucci
Posted: Tuesday November 3, 2015 4:22 PM
Subject: Tenure track faculty position at Rice University
Message:
Please share this announcements with interested parties.
The Department of Statistics at Rice University invites applications
for a tenure-track position. Priority hiring is at the Assistant
professor level, however, highly qualified, experienced candidates may
be considered at the rank of Associate Professor. A doctorate in
statistics or a related field is required, with potential or proven
excellence in research and teaching.
Applications are sought from areas of modern statistics that will
enhance the George R. Brown School of Engineerings focus on Data
Science and the understanding of complex data, while strengthening the
core research of the department. Example of areas include but are not
limited to Bayesian methods, computational finance, functional data,
multivariate analysis, networks or graphical models, probability
theory, statistical machine learning, spatial and temporal processes,
statistical computing, stochastic processes and optimization. Several
hires within the School of Engineering in the general area of Data
Science are expected.
The position begins Fall 2016. Please browse our website for a
description of departmental activities and people. To apply go to
http://facultysearch.statistics.rice.edu/
Applicants are requested to upload a letter of application, curriculum
vita, graduate transcripts, and reprints/preprints. Please include
names and addresses, including e-mail, of three individuals who will
be contacted for letters of recommendation.
Inquiries to: phyllis em stat.rice.edu. Include Faculty Search in the
subject line of the e-mail.
The review of applications will begin December 16, 2015. Applications
will continue to be accepted beyond this date until the position is
filled. Interviews will begin mid January.
Rice University is an Equal Opportunity/Affirmative Action employer
and is committed to increasing the diversity of its faculty.
------------------------------
Marina Vannucci
Professor and Chair
Department of Statistics
Rice University
6100 Main street
Houston, TX 77005
------------------------------
9.From: James Frane
Posted: Tuesday November 3, 2015 5:25 PM
Subject: RE: Principal Components Analysis
Message:
I don't understand how a method of analysis can be recommended without
understanding precisely what the variables are and what is already
known about each individually and what their relationships with each
other are.
There are some things that I don't recommend, e.g., principal
components without rotation to simple structure because of a general
lack of practical interpretation of the unrotated principal
components. Moreover, by default the rotation should be oblique
rather than orthogonal, i.e., orthogonal rotation should be used only
when oblique rotation reveals nearly orthogonal factors. It follows
that canonical correlation is not recommended because is yields
orthogonal uninterpretable factors.
Dimension reduction may be in order so I accept orthogonally rotated
prinicpal components (or maximum likelihood factors if you want to go
elegant) followed by oblique rotation for each of the two sets of
variables as a possibility, but only after such procedures pass common
sense acceptance on the basis of the first paragraph above.
------------------------------
James Frane
Self-Employed
-------------------------------------------------------------------------
Original Message:
Sent: 10-30-2015 09:27
From: Linda Landon
Subject: Principal Components Analysis
Everyone,
I'm about to make my first foray into principal component analysis
since graduate school. I'm working on a multivariate regression
problem that has multiple independent variables and multiple dependent
variables. I'm wondering if it would be possible to perform
multivariate regression by using BOTH the principal components
representing dimensions in the set of independent variables as
independent variables and the principal components representing
dimensions in the set of dependent variables as dependent variables
in the same regression model. However, I'm unsure if this application
is statistically valid.
The common examples found in texts and in the peer-reviewed literature
when PCA is applied prior to multivariate regression are
(1) multiple independent variables to find the principal components
in the independent variables and then one or more components are used
as independent variables in a regression against a single dependent
variable or
(2) to multiple dependent variables to find the principal
components in the dependent variables and then a single component is
used as a dependent variables in a regression against a more or more
independent variables.
However, I would like to know if it is possible to perform one
multivariate regression analysis on two sets of principal components,
one set of components representing the dimensions in the independent
variables and the other set of components representing the dimensions
in the dependent variables. In my rather limited literature search,
I haven't found an example of this application in the peer-reviewed
literature, which could indicate that it is not statistically valid.
Further, other sources of information don't explicitly address this
application.
Thanks in advance for your insight.
Linda
Linda A. Landon, PhD, ELS
President
Research Communiqu
Jefferson City, MO
Email: LandonPhD em ResearchCommunique.com <LandonPhD em ResearchCommunique.com>
Phone: 573-797-4517
Central Standard Time ( CST ) = GMT-6 (November February)
Central Daylight Time ( CDT ) = GMT-5 (March October)
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