D.A.S. Fraser's Home Page
Department of Statistical Sciences
University of Toronto
100 St George Street, Toronto
Canada M5S 3G3
of: Sidney Smith Hall, 100 St George Street, Rm. 6013.
ph: 416.978.4448 or 416.978.3452
em: dfraser at utstat.toronto.edu.
Another view of composite likelihood.
Combining dependent likelihood functions.
Priors and Inference: A differential view.
Combining dependent likelihoods: some thoughts on composite likelihood.
The role of Bias in Statistics.
Invited seminar at the University of Western Ontario, Dept of Statistical and Actuarial Sciences on April 12, 2012.
Science sees Data but no role for Statistics
Drugs deemed safe so freely prescribe and collect massive data
Drug deemed safe yet thousands dead but billions in profit
And just a mild call for "Data Replication:" ..... The deaths or the dollars?
And a discipline with two logics? Physics wouldn't tolerate that!
And Statistics mildly says it is just "exploring"!
That "exploring" wouldn't wash when they acknowledge they have two logics!
Physicists find billions to test the edges of their theories and avoid contradiction
Perhaps complacency isn't the route for Statistics
or they might taste the five billion penalty for contradiction
The role of Bias in Statistics.
The Bias in Bayes: A second-order determination.
2nd Princeton Day of Statistics.
Higher order likelihood and the curse of curvature.
Do statistical tools need calibration?
An address at the conference "Data Analysis and Statistical Foundations"
held at the Fields Institute in Toronto, April 30 and May 1, 2010.
Calibration in Statistics.
The Bane of Bayes: Parameter curvature!
A Taylor view of r and r* in general models
Higher order accuracy for inference arguably began with Daniels (1954) and
Lugannani and Rice (1980)
but was restricted to exponential models and
the cumulant generating function context.
Barndorff-Nielsen (1986) gave
extensions to general models with regularity leading to wide
general data size n and with nuisance parameters alongside interest parameters.
Much of the core mechanisms however can be seen with Taylor expansions in
the scalar model case.
Four types of expansions with their interconnections are presented on a
poster display initiated by
with Google search:
r vs r* - Magic from Taylor Expansions
Likelihood, p-values, ancillaries and the vector quantile function
Is Bayes really real probability?
Is Bayes posterior just quick and dirty confidence?
Higher accuracy for Bayesian and frequentist inference
Studentization and developing p-values
Can Bayesians compete with frequentists?
Controversy or did Lindley get it wrong?
Some Recent Talks and Papers
- University of Cambridge, Statistical Laboratory, Cambridge, U.K. May 8, 2009.
- University of Wales; Gregynog, Wales, April 18, 19, 2008.
- McMaster University, April 1, 2008.
- University of Western Ontario, February 7, 2008.
- Princeton University, ORFE, February 13, 2007.
- Univ of Western Ontario, Dept of Actuarial Science and
December 8, 2005.
- Univ of Waterloo, Dept of Actuarial Science and Statistics,
November 17, 2005.
- Statistical Society of Canada, Annual meeting, Saskatoon, June13, 2005.
- OBayes5, Branson, Missouri, June 8, 2005
- Munk Centre: Department of Statistics seminar, April 28, 2005
- Recent likelihood theory: Anything new?
- Statistical Society of Canada, Annual meeting,
Saskatoon, Saskatchewan, June 12-15, 2005.
- Case Western Reserve University, Department of Statistics
- Fields Institute Lectures: Is the future Bayesian or frequentist?
Papers by index number