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. 5016H.
ph: 416.978.4448 or 416.978.3452
fx: 416.978.5133.
em: dfraser at utstat.toronto.edu.
What can we get from likelihood? A new prior for Bayes
Distributions for parameters
Bayes Reproducibility and the Quest for Truth
Can you integrate confidence distributions and get reproducibility?
Combining likelihoods and combining pvalues
From many small dependent likelihoods to valid global inference
Definitive combining of loglikelihood or pvalue functions
On direction in statistics theory
On combining likelihoods or significance functions: What accuracy?
Combining likelihood or pvalue functions, with or without statistical
dependence.
Can Bayes give second order reproducibility?
On resolving the Bayes enigma: How Jeffreys gives 2nd order Accuracy
How saddlepoint and continuity determine statistical inference
Priors from a differential viewpoint: How Bayes can attain 2nd order Accuracy
Deciphering Bayes: Reproducibility? What accuracy?
Invited talk: Department of Statistics, Univesity of Southern California
Friday, April 4, 2014
Deciphering Bayes: Reproducibility? What accuracy?
"How science goes wrong"
The Economist, October 19th25th 2013, Vol 409
This is the coverpage editorial in the major jounal The Economist, October 19th25th 2013, Vol 409, but read on:
Pages 2630 record the story:
and the problems are all Statistics..... like "Reject at the 5% level" .....or minor variants
and they have been well known for more than 40 years.
But statistics does have the answer!
The answer is contained in the pvalue function from likelihood theory: p(delta)
Here delta is the relevant parameter with delta_0 as the null value and delta_1 as the alternative needing detection. Then p(delta_0) is the observed pvalue, p(delta_1) is the detection probability, and the rest is judgement: the route to the Higgs boson.
Inference distributions for a parameter: Are they calibrated?
Do they mean what they say?
Why does statistics have two theories?
Contemporary Statistics: Glamour risk and aftermath.
Saw Swee Hock Visiting Professorship in Statistics: Public Lecture,
Department of Statistics & Actuarial Science, August 22, 2013.
Glamour risk and aftermath.
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 secondorder 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!
Is r* linear in r? Sort of Yes but mostly No!

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.
BarndorffNielsen (1986) gave
extensions to general models with regularity leading to wide
applicability with
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.
Is r* linear in r?
Four types of expansions with their interconnections are presented on a
poster display initiated by
JeanFrancois Plante
and available
with Google search (Temporarily unavailable):
r vs r*  Magic from Taylor Expansions
Likelihood, pvalues, 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 pvalues
Can Bayesians compete with frequentists?
Controversy or did Lindley get it wrong?
Other 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
Statistics,
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 1215, 2005.
 Case Western Reserve University, Department of Statistics
 Fields Institute Lectures: Is the future Bayesian or frequentist?
Papers by index number
Current CV