D.A.S. Fraser

Department of Statistics
University of Toronto
100 St George Street, Toronto
Canada M5S 3G3

Office: Sidney Smith Hall, 100 St George Street, Rm. 6013.

phone: 416 978 4448 or 416 978 3452
fax: (416) 978-5133.
e-mail: dfraser at utstat.toronto.edu.

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

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