D.A.S. Fraser

Department of Statistics
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
fx: 416.978.5133.
em: dfraser at utstat.toronto.edu.

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 but thousands dead and 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.

Invited address at the Workshop on High-Dimensional Data Ananlysis
held at The Fields Institute, June 9, 2011.
High-Dimensional: The Barrier and Bayes and Bias.

2nd Princeton Day of Statistics.

• Opening Plenary Address at the 2nd Princeton Day of Statistics
  held at Princeton University, October 22, 2010.
  
• Continuity and Statistical Methodology.
  

Higher order likelihood and the curse of curvature.

• An address at the Joint Statistical Meetings 2010
  held at Vancouver, Canada on August 5.
  
  
• Parameter curvature and Bayesian Analysis.
  

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!

• Bayesian analysis or evidence based statistics.

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
  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.
  Four types of expansions with their interconnections are presented on a poster display initiated by
  Jean-Francois Plante
  and available with Google search:
  
  r vs r* - Magic from Taylor Expansions
  

Likelihood, p-values, ancillaries and the vector quantile function

• Statistical Laboratory, U of Cambridge: 5 May, 2009.

Is Bayes really real probability?

• Colloque du CRM: 6 novembre 2009.

Is Bayes posterior just quick and dirty confidence?

• Current manuscript.

Higher accuracy for Bayesian and frequentist inference

• Statistical Science, Vol 22, May 2007.

Studentization and developing p-values

• Biometrika, Vol 95 (2008), 1-16.

Can Bayesians compete with frequentists?

• Bayesian or frequentist: Three enigmatic examples

Controversy or did Lindley get it wrong?

• Did Lindley get the argument the wrong way around?
  

Some Recent Talks and Papers

• University of Cambridge, Statistical Laboratory, Cambridge, U.K. May 8, 2009.
  
  • Likelihood, p-values, ancillaries and the vector quantile function.
    Click here for audio presentation.
    

• University of Wales; Gregynog, Wales, April 18, 19, 2008.
  
  • Parameter curvature and the Bayesian frequentist divergence.
    

• McMaster University, April 1, 2008.
  
  • Bayesian posterior probability is just confidence and inconveniently needs linearity.
    

• University of Western Ontario, February 7, 2008.
  
  • The Bayes myth - Probabilities. Or just approximate confidence.
    

• Princeton University, ORFE, February 13, 2007.
  
  • Data-based probability for parameter values
    

• Univ of Western Ontario, Dept of Actuarial Science and Statistics, December 8, 2005.
  
  • Should Bayesians and frequentists calibrate their parameters?
    

• Univ of Waterloo, Dept of Actuarial Science and Statistics, November 17, 2005.
  
  • Some thoughts on model-based priors
    

• Statistical Society of Canada, Annual meeting, Saskatoon, June13, 2005.
  
  • Bayes or Likelihood: Convergence or Divergence
    

• OBayes5, Branson, Missouri, June 8, 2005
  
  • Objective and other priors
    

• Munk Centre: Department of Statistics seminar, April 28, 2005
  
  • Why a prior?
    

• Recent likelihood theory: Anything new?
  
  • What a model with data says about theta
    

• Statistical Society of Canada, Annual meeting, Saskatoon, Saskatchewan, June 12-15, 2005.
  
  • Session: Bayes or Likelihood: Convergence or Divergence
    Speakers: T. Severini, Northwestern University, J. Rousseau, University of Paris 5, and organizer.
  • Session program: Bayes or Likelihood: Convergence or Divergence
    

• Case Western Reserve University, Department of Statistics
  
  • Is there statistical inference? The Bayesian-frequentist divergence!
    October 1, 2004

• Fields Institute Lectures: Is the future Bayesian or frequentist?
  
  • Lecture 1: April 16, 2002
    History and overview
    
  • Lecture 2: April 18, 2002
    Examples, conflicts, resolutions

Papers by index number

• In pdf format

Current CV

• In pdf format

Department of Statistics Home Page