CURRICULUM VITAE

D.A.S. FRASER
Department of Statistics
University of Toronto
Toronto, Ontario
CANADA, M5S 3G3

BIRTH April 29, 1925

PLACE OF BIRTH Toronto, CANADA

CITIZENSHIP Canadian

DEGREES 

1946     B.A.     University of Toronto
1947     M.A.     University of Toronto
1948     M.A.     Princeton University
1949     Ph.D.    Princeton University
1992     D.Math.  University of Waterloo
2002     D.Sc.    University of Toronto

EMPLOYMENT

APPOINTMENTS 

1949-53          Assistant Professor    Mathematics  University of Toronto
1953-58          Associate Professor    Mathematics  University of Toronto
1958-present     Professor              Mathematics  University of Toronto
1977-83          Professor & Chairman   Statistics   University of Toronto
1983-present     Professor              Statistics   University of Toronto
1984-86          Adjunct Professor      Mathematics  York University
1986-1995        Professor              Mathematics  York University
1984-present     Adjunct Professor      Statistics   University of Waterloo

VISITING APPOINTMENTS 

1955             Associate Professor    Mathematics  Princeton University
1961-62          Professor              Statistics   Stanford University
1964             Professor              Statistics   University of Copenhagen
1965             Professor              Statistics   University of Wisconsin
1969-70          Professor              Mathematics  University of Hawaii
1978-79          Professor              Mathematics  University of Geneva
1982-83          Professor              Statistics   Stanford University
2002-03          Professor              Mathematics  EPFL Lausanne
HONOURS & AWARDS 

1946     Putnam Competition, Winning Team
1949     Member, Sigma Xi
1954     Fellow, Institute of Mathematical Statistics
1956     Fellow, Royal Statistical Society
1962     Fellow, International Statistical Institute
1962     Fellow, American Statistical Association
1967     Fellow, Royal Society of Canada
1971     Fellow, American Association for the Advancement of Science
1985     First Gold Medal, Statistical Society of Canada
1990     R.A. Fisher Award and Prize, American Statistical Association
         Annual Meeting, August 8, Anaheim, California
1991     Honorary Member, Statistical Society of Canada
1992     Doctor of Mathematics, honoris causa, University of Waterloo, June 30
1999     Distinguished Statistician, American Statistical Asociation Videotape Archives.
2000     Gold Medal, Islamic Statistical Society. 
2002     Doctor of Science, honoris causa, University of Toronto, June 24

PROFESSIONAL AFFILIATIONS AND ACTIVITIES 

Associate Editor    Journal of Multivariate Analysis
Associate Editor    Statistical Papers
Associate Editor    Utilitas Mathematica
Associate Editor    Theory and Decision Library
Editorial Board     Journal Applied Probability and Statistics

PH.D SUPERVISION

52 Ph.D. students completed; 2 current students

RESEARCH GRANTS 

1992-96     NSERC     $42,000 per annum
1996-00     NSERC     $42,000 per annum
2000-05     NSERC     $40,000 per annum
2005-10     NSERC     $40,000 per annum

PUBLICATIONS

BOOKS 

1957      Nonparametric Methods in Statistics, New York: Wiley
1958      Statistics: An Introduction, New York: Wiley
1968      The Structure of Inference, 1968, New York: Wiley
1976      Probability and Statistics, Theory and Applications,
          North Scituate, Massachusetts: Duxbury
1979      Inference and Linear Models, New York: McGraw Hill

PAPERS

  1. Fraser, D.A.S. (1950). Note on the chi square-smooth test. Biometrika 37, 447-448.
  2. Fraser, D.A.S. (1951). Generalized hit probabilities with a Gaussian Target. Annals Math. Statist. 22, 248-255.
  3. Fraser, D.A.S., and Wormleighton, R. (1951). Nonparametric estimation IV. Annals Math. Statist. 22, 294-298.
  4. Fraser, D.A.S. (1951). Sequentially determined statistically equivalent blocks. Annals Math. Statist. 22, 372-381.
  5. Fraser, D.A.S. (1951). Normal samples with linear constraints and given variances. Can. J. Math. 3, 363-366.
  6. Baillie, D.C., and Fraser, D.A.S. (1951). The statistical analysis of the Rosvold and Mishkin data. Can. J. Psych. 5, 82-84.
  7. Fraser, D.A.S. (1952). Sufficient statistics and selection depending on the parameter. Annals Math. Statist. 23, 417-425.
  8. Fraser, D.A.S. (1952). Confidence bounds for a set of means. Annals Math. Statist. 23, 575-585.
  9. Fraser, D.A.S., and Guttman, I. (1952). Bhattacharyya bounds without regularity assumptions. Annals Math. Statist. 23, 629-632.
  10. Fraser, D.A.S. (1953). Nonparametric tolerance regions. Annals Math. Statist. 24 ,44-55.
  11. Fraser, D.A.S. (1953). Generalized hit probabilities with a Gaussian target II. Annals Math. Statist. 24, 288-294.
  12. Fraser, D.A.S. (1953). The Behrens-Fisher problem for regression coefficients. Annals Math. Statist. 24, 390-402.
  13. Fraser, D.A.S. (1953). Completeness of order statistics. Can. J. Math. 6, 42-45.
  14. Fraser, D.A.S. (1953). Non-parametric theory: Scale and location parameters. Can. J. Math. 6, 46-68.
  15. Fraser, D.A.S. and Guttman, I. (1956). Tolerance regions. Annals Math. Statist. 27, 162-179.
  16. Fraser, D.A.S. (1956). A vector form of the Wald-Worfowitz-Hoeffding theorem. Annals Math. Statist. 27, 540-543.
  17. Fraser, D.A.S. (1956). Sufficient statistics with nuisance parameters. Annals Math. Statist. 27, 838-842.
  18. Fraser, D.A.S. (1957). A regression analysis using the invariance method. Annals Math. Statist. 28, 517-520.
  19. Fraser, D.A.S. (1957). On the combining of interblock and intrablock estimates. Annals Math. Statist. 28, 814-816.
  20. Fraser, D.A.S. (1957). Most powerful rank-type tests. Annals Math. Statist. 28, 1040-1043.
  21. Chung, J.H., and Fraser, D.A.S. (1958). Randomization tests for a multivariate two-sample problem. J. Amer. Statist. Assoc. 53, 729-735.
  22. Fraser, D.A.S. (1961). On fiducial inference. Annals Math. Statist. 32, 661-676.
  23. Fraser, D.A.S. (1961). The fiducial method and invariance. Biometrika 48, 261-280.
  24. Fraser, D.A.S. (1962). On the consistency of the fiducial method. J. Royal Statist. Soc. B24,, 425-434.
  25. Fraser, D.A.S. (1963). On sufficiency and the exponential family. J. Royal Statist. Soc. B25, 115-123.
  26. Fraser, D.A.S. (1963). On the sufficiency and likelihood principles. J. Amer. Statist. Assoc. 58, 641-647.
  27. Fraser, D.A.S. (1964). On the definition of fiducial probability. Bull. Int. Statist. Inst. 40, 842-856.
  28. Fraser, D.A.S. (1964). On local unbiased estimation. J. Royal Statist. Soc. B26, 46-51.
  29. Fraser, D.A.S. (1964). Local conditional sufficiency. J. Royal Statist. Soc. B26, 52-62.
  30. Fraser, D.A.S. (1964). Fiducial inference for location and scale parameters. Biometrika 51, 17-24.
  31. Fraser, D.A.S. (1964). On local inference and information. J. Royal Statist. Soc. B26, 253-260.
  32. Fraser, D.A.S. (1956). On information in statistics. Annals Math. Statist. 36, 890-896.
  33. Fraser, D.A.S. (1965). Fiducial consistency and group structure. Biometrika 52, 55-65.
  34. Fraser, D.A.S. (1966). Structural probability and a generalization. Biometrika 53, 1-9.
  35. Fraser, D.A.S. (1966). Sufficiency for regular models. Sankhya A28, 137-144.
  36. Fraser, D.A.S. (1966). On sufficiency and conditional sufficiency. Sankhya A28, 145-150.
  37. Fraser, D.A.S. (1966). Sufficiency for selection models. Sankhya A28, 329-334.
  38. Fraser, D.A.S. (1967). Sufficiency or conditional sufficiency. Sankhya A29, 239-244.
  39. Fraser, D.A.S. (1967). Statistical models and invariance. Annals Math. Statist. 38, 1061-1067.
  40. Fraser, D.A.S. (1967). Data transformations and the linear model. Annals Math. Statist. 38, 1456-1465.
  41. Fraser, D.A.S. (1967). The basis of inference. Transactions Royal Society Canada V,227-231.
  42. Fraser, D.A.S. (1968). The conditional Wishart: normal and nonnormal. Annals Math. Statist. 39, 593-605.
  43. Fraser, D.A.S. (1968). A black box or a comprehensive model. Technometrics 10, 219-229.
  44. Fraser, D.A.S. (1968). Fiducial inference. International Encyclopedia Social Sciences The Macmillan Company and The Free Press, 403-406.
  45. Fraser, D.A.S., and Haq, M.S. (1969). Structural probability and prediction for the multivariate model. J.Royal Statist. Soc. B31,317-331.
  46. Ali, M.M., Fraser, D.A.S., and Lee, Y.S. (1970). Distribution of the correlation matrix. J. Statist. Research 4, 1-15.
  47. Fraser, D.A.S. and Haq, M.S. (1970). Inference and prediction for the multilinear model. J. Statist. Research 4, 93-109.
  48. Fraser, D.A.S., and Prentice, R.L. (1971). Randomized models and the dilution and bioassay problems. Annals Math. Statist. 42, 141-146.
  49. Fraser, D.A.S. (1972). Events, information processing, and the structured model. In V.P. Godambe and D.A. Sprott (Eds.), Foundations of Statistical Inference, 32-55. Toronto: Holt, Rinehart and Winston.
  50. Fraser, D.A.S. (1972). Bayes, likelihood, or structural. Annals Math. Statist. 43, 777-790.
  51. Fraser, D.A.S. (1972). The determination of likelihood and the transformed regression model. Annals Math. Statist. 43, 898-916.
  52. Fraser, D.A.S., and Streit, F. (1972). On the Behrens-Fisher Problem. Aust. J. Statist. 14, 167-171.
  53. Fraser, D.A.S. (1972). Is the statistical model flat? In C.S. Carter et al (Eds.), Proceedings of the First Conference in Applied Statistics; Statistics '71 Canada, 83-86. Montreal.
  54. Fraser, D.A.S. (1973). On statistical analysis. Inference and Decision I, 17-22.
  55. Fraser, D.A.S. (1973). The elusive ancillary. In D.G. Kabe, R.P. Gupta (Eds.), Multivariate Statistical Inference, 41-48. Amsterdam: North Holland Publishing Company.
  56. Fraser, D.A.S. (1973). Inference and redundant parameters. Multivariate Analysis III, 143-156. New York: Academic Press.
  57. Fraser, D.A.S., and MacKay, J. (1975). Parameter factorization and inference based on significance, likelihood, and objective posterior. Annals Statist. 3, 559-572.
  58. Fraser, D.A.S. (1973). Comments on post-data two sample tests of location. J. Amer. Statist. Assoc. 68, 97-106.
  59. Fraser, D.A.S. (1973). Comments on the marginalization paradoxes. J. Royal Statist. Soc. B35, 225-228.
  60. Fraser, D.A.S. (1974). Comparison of inference philosophies. In G. Menges (Ed.),Information, inference and decision, 77-98. Dordrecht: D. Reidel Publishing Company.
  61. Fraser, D.A.S., and Mackay, Jock. (1976). On the equivalence of standard inference procedures. In Harper and Hooker (Eds.), Foundations of Probability Theory, Statistical Inference, and Statistical Theories of Science II, 47-62. Dordrecht: D. Reidel Publishing Companyng.
  62. Fraser, D.A.S. (1976). Necessary analysis and adaptive inference. J. Amer. Statist. Assoc. 71, 99-110, 112-113.
  63. Fraser, D.A.S., and Fick, G.H. (1975). Necessary analysis and its implementation, Proceedings of the Symposium on Statistics and related topics, Carleton Mathematical Notes 12, 5.01-5.30.
  64. Fraser, D.A.S., Guttman, I., and Styan, G.P.H. (1976). Serial correlation and distributions on the sphere. Comm. in Statist. A5(2), 97-118.
  65. Fraser, D.A.S. (1976). Comments on strong inconsistency from uniform priors. J. Amer. Statist. Assoc. 71, 122-123.
  66. Fraser, D.A.S. (1976). Comments on rereading R.A. Fisher. Annals Statist. 4, 488-489.
  67. Fraser, D.A.S., and Ng, K.W. (1977). Inference for the multivariate regression model. Multivariate Analysis IV, 35-53. Amsterdam: North Holland Publishing Co.
  68. Fick, G.H., and Fraser, D.A.S. (1976). Robustness with structural methods. Ball State University Proceedings, 75-93. Indiana, Muncie.
  69. Fraser, D.A.S. (1977). Confidence, posterior probability, and the Buehler example. Annals Statist. 5, 892-898.
  70. Brenner, D., and Fraser, D.A.S. (1979). On foundations for conditional probability with statistical models - When is a class of functions a function? Statistische Hefte 20, 148-159.
  71. Bishop, L., Fraser, D.A.S., and Ng, K.W. (1979). Some decompositions of spherical distributions. Statistische Hefte 20, 1-22.
  72. Fraser, D.A.S. (1977). Comments on resolving the controversies in statistical inference. J. Royal Statist. Soc. B39, 154-155.
  73. Evans, M., and Fraser, D.A.S. (1980). An optimum tolerance region for multivariate regression. J. Mult. Anal. 10, 268-272.
  74. Fraser, D.A.S., and Ng, K.W. (1980). Multivariate regression analysis with spherical error. Multivariate Analysis V ,369-386. Amsterdam: North Holland Publishing Co.
  75. Fraser, D.A.S. (1980). Inference and the structural model for ANOVA and MANOVA. In P.R. Krishnaiah (Ed.), Handbook of Statistics I ,389-406. Amsterdam: North Holland Publishing Co.
  76. Fraser, D.A.S. (1979). Comments on conditional independence. J. Royal Statist. Soc. B41, 22.
  77. Feuerverger, A., and Fraser, D.A.S. (1980). Categorical information and the singular linear model. Can. J. Statist. 8, 41-45.
  78. Fraser, D.A.S. (1979). Comments on reference posterior distributions for Bayesian inference. J. Royal Statist. Soc. B41, 136.
  79. Fraser, D.A.S., and McDunnough, P. (1980). Some remarks on conditional and unconditional inference for location-scale models. Statistische Hefte 21, 224-231.
  80. Brenner, D., and Fraser, D.A.S. (1980). The identification of distribution form. Statistische Hefte 21 ,296-304.
  81. Fraser, D.A.S. (1980). Comments on some statistical paradoxes and non-conconglomerability. Bayesian Statistics ,56-58. Valencia: University Press.
  82. Brenner, D., and Fraser, D.A.S. (1981). A simplification of the traditional statistical model in the presence of symmetry. Statistische Hefte 22, 195-206.
  83. Brenner, D., and Fraser, D.A.S. (1980). An analytic-algebraic approach to statistical models and inference. Mathematical Reports of the Academy of Science II, 89-93.
  84. Brenner, D., Fraser, D.A.S., and McDunnough, P. (1982). On asymptotic normality of likelihood and conditional analysis. Can. J. Statist. 10, 163-172.
  85. Fraser, D.A.S., and Streit, F. (1980). A further note on the bivariate normal distribution. Comm. Statist. Th. Meth. A9, 1097-1099.
  86. Brenner, D., Fraser, D.A.S., and Monette, G. (1981). Theories of inference or simple additives. Statistische Hefte 22, 231-233.
  87. Fraser, D.A.S. (1983). Statistical Inference. Encyclopedia of Statistical Sciences 4 ,105-114.
  88. Brenner, D., Fraser, D.A.S., and McDunnough, P. (1981). Transformation-parameter/structural models: Asymptotic conditional distributions. Mathematical Reports of the Academy of Science 3, 49-53.
  89. Brenner, D., and Fraser, D.A.S. (1982). On the foundations: Statistical models and inference. Can. J. Statist. 10, 155-161.
  90. Evans, M., Fraser, D.A.S., and Massam, H. (1982). The Weibull model, objective form, and linear analysis. Statistische Hefte 23, 110-115.
  91. Brenner, D., Fraser, D.A.S., and Monete, G. (1983). On models and theories of inference; structural or pivotal analysis. Statistische Hefte 24, 7-19.
  92. Brenner, D., Evans, M., Fraser, D.A.S., Massam, H., and Rost, E. (1984). The identification of distribution form, II. Statistische Hefte 25, 61-68.
  93. Fraser, D.A.S., and McDunnough, P. (1984). Further remarks on asymptotic normality of likelihood and conditional analyses. analysis. Can. J. Statist. 12 ,183-190.
  94. Evans, M.J., Fraser, D.A.S., and Monette, G. (1986). On principles and arguments to likelihood. Can. J. Statist. 14, 181-199.
  95. Fraser, D.A.S. (1982). Comment on the functional-model basis of fiducial inference. Annals Statist. 10, 1070-1073.
  96. Fraser, D.A.S., Evans, M., and Monette, G. Structural ancillarity. manuscript.
  97. Fraser, D.A.S. Consistency of the necessary reduction methods. manuscript.
  98. Brenner, D., Fraser, D.A.S., Menges, G., and Rost, E. (1982). Model analysis with structural and stochastic partial information. Statistische Hefte 23, 134-141.
  99. Brenner, D., and Fraser, D.A.S. (1983). Comment on Barnard's Pivotal Inference. Proceedings of the 1981 Bayesian Symposium at the University of Wisconsin Madison.
  100. Fraser, D.A.S. (1985). Statistical Modelling. In E.Schneeweiss, H. Streker (Eds.), Contributions to Econometrics and Statistics today, 89-100. Berlin: Springer-Verlag, 89-100.
  101. Fraser, D.A.S., and Evans, M. (in revision). On classifying structured models.
  102. Evans, M., Fraser, D.A.S., and Monette, G. (1985). On the role of principles in statistical inference. In K. Matusita (Ed.), Statistical Theory and Data Analysis, 225-231. New York: North-Holland.
  103. Fraser, D.A.S., and Evans, M. (manuscript). Structural models: A classification with key examples.
  104. Evans, M., Fraser, D.A.S., and Monette, G. (1985). Mixtures, embedding, and ancillarity. Can. J. Statist. 13, 1-6.
  105. Evans, M., Fraser, D.A.S., and Monette, G. (1985). Deduction and principles in statistics, Technical Report. Department of Statistics, University of Toronto.
  106. Fraser, D.A.S., Monette, G., and Ng, K.W. (1985). Marginalization, likelihood, and structured models. Multivariate Analysis VI, 209-217. New York: North-Holland.
  107. Fraser, D.A.S. (1988). Structural models. Encyclopedia of Statistical Sciences 9, 27-32.
  108. Fraser, D.A.S. (1988). Structural inference, Encyclopedia of Statistical Sciences 9, 20-27.
  109. Fraser, D.A.S., and McDunnough, P. (manuscript). Asymptotic likelihood for location models.
  110. Fraser, D.A.S., and Gebotys, R.J. (1987). Non-nested linear models: A conditional confidence approach. Can. J. Statist. 15, 375-386.
  111. Fraser, D.A.S., and Brenner, D. (manuscript)(1989). Probabilities and conditioning; critical examples.
  112. Fraser, D.A.S. (1989)(manuscript). Curved Exponential models: tests and confidence intervals.
  113. Fraser, D.A.S., and Monette, G. (manuscript). Betting assessments of probabilities.
  114. Evans, M., Fraser, D.A.S., and Monette, G. (1985). On regularity for statistical models. it Can. J. Statist. 13, 137-144.
  115. Fraser, D.A.S., and Massam, H. (1987). Second-order inference for generalized least squares. Can. J. Statist. 15, 21-30.
  116. Fraser, D.A.S. (manuscript). Conditioning as opposed to standard optimality, manuscript.
  117. Fraser, D.A.S., and McDunnough, P. (1988). On generalization of the analysis of variance. Annals Inst. Statist. Math. 40 , 353-366.
  118. Fraser, D.A.S. (1988). Rotation group, Encyclopedia of Statistical Sciences 8 , 189-190.
  119. Fraser, D.A.S. (1986). Reduced model, Encyclopedia of Statistical Sciences 7, 658-659.
  120. Fraser, D.A.S., and Massam, H. (1985). Conical tests: Observed levels of significance and confidence regions. Statistische Hefte 26, 1-17.
  121. Fraser, D.A.S., and Sackey, S. E. (1986). Prior-likelihood factorization and missing data. Comm. Statist. Th. Meth. 15(11), 3321-3331.
  122. Fraser, D.A.S. (1987). Statistics, Foundations. Encyclopedia of Physical Science and Technology 13, 286-297.
  123. Fraser, D.A.S., and Massam, H. (1989). A mixed primal-dual bases algorithm for regression under inequality constraints: Application to concave regression. Scand. J. Statist. 16, 65-74.
  124. Evans, M., Fraser, D.A.S., and Monette, G. (1987). Statistical principles and tangent models. In I.B. MacNeill, G.J. Umphrey (Eds.), Foundations of Statistical Inference, 21-29. Dordrecht: D. Reidel.
  125. Fraser, D.A.S., and Massam, H. (1987). An algorithm for concave regression. In I.B. MacNeill, G.J. Umphrey (Eds.), Foundations of Statistical Inference, 121-132. Dordrecht: D. Reidel.
  126. Fraser, D.A.S. (manuscript). Quadratic approximations for second order inference in nonlinear least squares.
  127. Fraser, D.A.S. (1985). Comment on the resolution of Godambe's paradox. Can. J. Statist. 13, 298-299.
  128. Fraser, D.A.S. (1985). Comment on a coherent view of statistical inference. Waterloo Symposium on Statistical Inference Waterloo: University of Waterloo, Department of Statistics, 52-62.
  129. Fraser, D.A.S. (1987). Fibre analysis and tangent models. Statistische Hefte 28, 163-181.
  130. Fraser, D.A.S., and Massam, H. (1988). Location inference on spheres and cylinders. J. Statist. Plan. Inf. 18, 195-201.
  131. Fraser, D.A.S. (1987). Sequential parameter structure, conditional inference, and likelihood drop. Statistische Hefte 28, 27-52.
  132. Fraser, D.A.S. (1986). Comment on parameter orthogonality and approximate conditional inference. J. Royal Statist. Soc. B49, 29.
  133. Dobriyal, A., Gebotys, R., and Fraser, D.A.S. (1987). Linear calibration and conditional inference. Comm. Statist. 16(4), 1037-1048.
  134. Fraser, D.A.S., and Reid, N. (1988). On conditional inference for a real parameter: a differential approach on the sample space. Biometrika 75, 251-264.
  135. Fraser, D.A.S., and Reid, N. (1988). Fibre analysis and conditional inference. In K. Matusita (Ed.), Proceedings of the Second Pacific Area Statistical Conference, Statistical Theory and Data Analysis II, 241-247. North Holland.
  136. Dobriyal, A., Fraser, D.A.S., and Gebotys, R. (1987). Approximate conditional inference and the linear functional model. Comm. Statist. Th. Meth. 16(12), 3729-3737.
  137. Fraser, D.A.S., McDunnough, P., and Reid, N. (1987). Some aspects of conditioning. In M.S. Haq, S.B. Provost (Eds.), Recent Developments in Statistics and Actuarial Science, 1-13. London, Ontario: Scitex.
  138. Fraser, D.A.S., and Reid, N. (1988). On comparing two methods for approximate conditional inference. Statistische Hefte 29, 271-280.
  139. Fraser, D.A.S. (1988). Normed likelihood as saddlepoint approximation. J. Mult. Anal. 27,181-193.
  140. DiCiccio, T.J., Field, C.A., and Fraser, D.A.S. (1990). Approximations of marginal tail probabilities and inference for scalar parameters. Biometrika 77, 77-95.
  141. Fraser, D.A.S., and Reid, N. (1989). Adjustments to profile likelihood, Biometrika 76, 477-488.
  142. Fraser, D.A.S., Reid, N., and Wong, A. (1991). Exponential linear models: a two pass procedure for saddle point approximation. J. Royal Statist. Soc. B53, 483-492.
  143. Fraser, D.A.S. (1990). Tail probabilities from observed likelihoods. Biometrika 77, 65-76.
  144. Reid, N., and Fraser, D.A.S. (1989). Comments on the geometry of asymptotic inference. Statist. Sci. 4, 231-233.
  145. Fraser, D.A.S., Lee, H.S., and Reid, N. (1990). Nonnormal linear regression; an example of significance levels in high dimensions. Biometrika 77, 333-341.
  146. Fraser, D.A.S., Reid, N., and Wong, A. (1997). Simple and accurate inference for the mean of the gamma model. Canadian J. Statist 25, 91-99.
  147. Fraser, D.A.S. (1988). Normed likelihood as saddlepoint approximation. In Rao, C.R. (Ed.), J. Multivariate Anal. 27, 181-193. San Diego: Academic Press.
  148. Fraser, D.A.S., and Reid, N. (1990). Statistical inference: Some theoretical methods and directions. Environmetrics 1, 21-35.
  149. Fraser, D.A.S., and Reid, N. (1991). Converting observed likelihoods functions to tail probabilities. Computational Statistics and Data Analysis 12, 179-185.
  150. Fraser, D.A.S. (1990). Views on conditional and marginal methods of statistical inference. Statistische Hefte 31, 83-93.
  151. Fraser, D.A.S., and Reid, N. (1990). Discussion of an ancillarity paradox which appears in multiple linear regression. Annals Statist. 18, 503-507.
  152. Fraser, D.A.S., McDunnough, P., Naderi, A., and Plante, A. (1995). On the definition of probability densities and the sufficiency of the likelihood map. Jour.Prob. Math. Statist. 15, 301-310.
  153. Fraser, D.A.S. (1990). On R.A. Fisher. Chance 3, 30.
  154. Fraser, D.A.S., and Reid, N. (1990). From multiparameter likelihood to tail probabilities for a scalar parameter. Technical Report, University of Toronto, Department of Statistics.
  155. Fraser, D.A.S. (1990). Comment on inferential estimation, likelihood, and linear pivotals. Can. J. Statist. 18, 14-15.
  156. Fraser, D.A.S., Guttman, I., and Srivastava, M. S. (1991). Conditional inference for treatment and error in multivariate analysis, Biometrika 78, 565-72.
  157. Fraser, D.A.S. (1991). On properties of sufficiency and statistical tests. In Kotz, S., and Johnson, L.J. (Eds.), Breakthroughs in Statistics I, 109-112. New York: Springer-Verlag.
  158. Fraser, D.A.S. (1993). Directional tests and statistical frames. Statistical Papers 34, 213-236.
  159. Fraser, D.A.S. (1991). Statistical inference: Likelihood to significance. J. Amer. Statist. Assoc. 86, 258-265.
  160. Cheah, P.K., Fraser, D.A.S., and Reid, N. (1994). Multiparameter testing in exponential models: Third order approximations from likelihood. Biometrika 81, 271-278.
  161. Cheah, P.K., Reid, N., Fraser, D.A.S., and Tapia, A. (1992). Third order asymptotics: connections among test quantities. Comm. Statist Th. Meth. 21(8), 2127-33.
  162. Fraser, D.A.S., and Wong, A. C. M. (1993). Approximate Studentization with marginal and conditional inference. Can. J. Statist. 21, 313-320.
  163. Ennis, M., and Fraser, D.A.S. (1992). Higher order local unbiasedness with computer algebra. Comm. Statist. Th. Meth. 21(11), 3171-3176.
  164. Fraser, D.A.S. (1992). Sub-model selection and combination for statistical inference. In Saleh, A.K.Md.E. (Ed), Proceedings International Conference on Nonparametric Statistics and Related Topics, 411-421. New York: Elsevier.
  165. Fraser, D.A.S., and Reid, N. (1992). Aspects of modified profile likelihood . In Saleh, A.K.Md.E. (Ed.), Proceedings International Conference on Nonparametric Statistics and Related Topics, 423-432. New York: Elsevier.
  166. Abebe, F., Cakmak, S., Cheah, P.K., Fraser, D.A.S., Kuhn, J., McDunnough, P., Tapia, A., Reid, N. (1995). Third order asymptotic model: Exponential and location type approximations. Parisankhyan Samikkha 2, 25-33.
  167. Reid, N., and Fraser, D.A.S. (1990). Accurate approximation of tail probabilities using the likelihood function, Proceedings of IV CLAPEM. Contribuciones en probabilidad y estadistica matematica 4, 36-50. Mexico City.
  168. Cakmak, S., Cheah, P.K., Fraser, D.A.S., Reid, N., and Tapia, A. (1993) (in revision). Third order asymptotic model: Exponential type approximation.
  169. Cheah, P.K., Abebe, F., Cakmak, S., Fraser, D.A.S., Kuhn, J., and Reid, N. (1993) (in revision). Third order asymptotic model: Location type approximation.
  170. Fraser, D.A.S. On likelihood as a concept. News and notes of Royal Statist. Soc. 18, 2-3.
  171. Fraser, D.A.S., and Reid, N. (1993).Third Order Asymptotic Models: Likelihood functions leading to accurate approximations for distribution functions Statist. Sinica 3, 67-82.
  172. Fraser, D.A.S. Adjusted densities and likelihoods with corresponding tail probability formulas; combined with 183.(in revision)
  173. Fraser, D.A.S., Monette, G., Ng, K.W., and Wong, A. (1994). Higher order approximations with generalized linear models. In Anderson, T.W., Fang, K.T., and Olkin, I. (Eds.), Multivariate Anaylsis and Its Applications, IMS Lecture Notes, Monograph Series 24, 253-262.
  174. Cheah, P.K., Fraser, D.A.S.,Ng, K.W. and Reid, N. (1993). Some alternatives to Edgeworth. Can. J. Statist. 21, 131-138.
  175. Fraser, D.A.S., and Reid, N.(1993) Third order significance for scalar and vector parameters, included in 178. (in revision)
  176. Fraser, D.A.S., and Reid, N. (1993) Finding third order ancillaries, included in 178. (in revision)
  177. Cakmak, S., Fraser, D.A.S., McDunnough, P., Reid, N., and Yuan, X. (1998). Likelihood centered asymptotic model: exponential and location model versions. Int. J. Math. & Stat. Sci. 4, 211-222.
  178. Fraser, D.A.S., and Reid, N. (1995). Ancillaries and third order significance. Utilitas Mathematica 47, 33-53.
  179. Fraser, D.A.S. (1994). Towards close connections between mathematics and statistics. Parisankhyan Samikkha 1, 1-5.
  180. Ng, K.W., and Fraser, D.A.S. (1994). Inference for linear models with radially decomposable error. In Anderson, T.W., Fang, K.T., and Olkin, I. (Eds.), Multivariate Anaylsis and Its Applications, IMS Lecture Notes, Monograph Series 24, 359-367.
  181. Fraser, D.A.S., and Reid, N. (1993). Methods of third order statistical inference. In David Sprott (Ed.), Proceedings of Conference on Statistical Inference and Biostatistics, 27-40. Guanajuato, Mexico.
  182. Abebe, F., Fraser, D.A.S., and Wong, A. (1996). Nonlinear regression: third order significance. Utilitas Mathematica 49, 3-19.
  183. Cheah, P.K., Fraser, D.A.S., and Reid, N. (1995). Adjustment to likelihood and densities: calculating significance. J.Statist. Research 29, 1-13.
  184. Cakmak, S., Fraser, D.A.S., and Reid, N. (1994). Multivariate asymptotic model: exponential and location approximations. Utilitas Mathematica 46, 21-31.
  185. Fraser, D.A.S., and Wong, A.C.M. (1997). On the accuracy of approximate studentization. Statist. Papers 38, 351-356.
  186. Fraser, D.A.S., and Reid, N. (1996). Bayes posteriors for scalar interest parameters, Bayesian Statistics 5 , 581-585. Oxford: Clarendon Press.
  187. Fraser, D.A.S. and Plante, A. (1996) (in revision). Combining unbiased estimators: Some further remarks.
  188. Brenner, D., Fraser, D.A.S., and Zeng, Q. (1996) (in revision). The Behrens Fisher problem by third order asymptotics.
  189. Fraser, D.A.S., and Reid, N. (1995). Evolution in statistical inference: from sufficiency to likelihood asymptotics. Journal of Statistical Research 29, 59-70.
  190. Fraser, D.A.S., and Yuan, X. (1995) (in revision). The Box and Cox problem: Asymptotic significance levels.
  191. Fraser, D.A.S., and Reid, N. (1996). Separating error, nuisance effect and main effect:Tangent models and third order inference , Journal of Statistical Research 30, 1-8.
  192. Fraser, D.A.S., Wong, A.C.M, and Wu, J. (1998). An approximaition for the noncentral chi-squared distribution. Communications in Statistics-Simulations 27(2), 275-287.
  193. Fraser, D.A.S., and Naderi, A. (1996). On the definition of conditional probability. In E. Brunner and M. Denker (Eds.), Research Developments in Probability and Statistics, Utrecht: VSP, 23-26.
  194. Fraser, D.A.S., McDunnough, P., and Taback, N. (1997). Improper priors, posterior asymptotic normality, and conditional inference. In N.L. Johnson and N. Balakrishnan (Eds.), Advances in the Theory and Practice of Statistics, 563-569. New York: Wiley.
  195. Fraser, D.A.S., McDunnough, P., Naderi, A., and Plante, A. (1997). From the likelihood map to Euclidean minimal sufficiency. Jour. Prob. Math. Statist. 17 , 223-230.
  196. Fraser, D.A.S., Reid, N., and Wu, J. (1999). A simple general formula for tail probabilities for frequentist and Bayesian inference. Biometrika 86, 249-264.
  197. Fraser, D.A.S. (1996). Some remarks on pivotal models and the fiducial argument in relation to structural models. International Statistical Review 64, 231-235.
  198. Fraser, D.A.S., and Reid, N. (2001). Ancillary information for statistical inference. In S.E. Ahmed and N. Reid (Eds), Empirical Bayes and Likelihood Inference, 185-209. New Yok: Springer-Verlag.
  199. Fraser, D.A.S., and Wu, J. (in revision). Curvature measures for statistical models: Third order measures of departure from exponential or location form.
  200. Fraser, D.A.S., Wong, A., and Wu, J. (1999). Regression Analysis, Nonlinear or Nonnormal:Simple and accurate p-values from Likelihood Analysis. Journal American Statistical Assocociation 94, 1286-1295.
  201. Fraser, D.A.S., and Reid, N. (1996). Ancillary statistics, First derivative. Encyclopedia of Statistical Sciences.
  202. Fraser, D.A.S., Reid, N., and Wu, J. (1997). Estimating functions and higher order significance. In R.G. Taylor, and V.P. Godambe (Eds.), Selected Proceedings of the Symposium on Estimating Functions, IMS Lecture Notes-Monograph Series, Hayward: IMS, 105-114.
  203. Reid, N., and Fraser, D.A.S. (in revision). Cumulants and pseudo-cumulants for asymptotic expansions.
  204. Fraser, D.A.S., Reid, N., and Wu, J. (1998). On the informative presentation of likelihood. Applied Statistical Science III, 253-265. Commack, New York: Nova Science Publishers.
  205. Fraser, D.A.S., Ng, K.W., and Wong, A.C.M. (1997). A third order asymptotic test of bioequivalence in a multivariate parametric setting. Proceedings of the 1997 International Symposium on Contemporary Multivariate Analysis, Hong Kong.
  206. Fraser, D.A.S. (1998). Comment on R.A. Fisher in the 21st century Statistical Science 13, 118-120.
  207. Fraser, D.A.S., McDunnough, P., Naderi, A., and Plante, A. (2002). From the likelihood map to Euclidean minimal sufficiency. Jour. Prob. Math. Statist. 17, 223-230.
  208. Fraser, D.A.S.(in revision) Probability flow and statistical inference.
  209. Cakmak, S., Fraser, D.A.S., McDunnough, P., Reid, N., and Yuan, X. (1998). Likelihood centered asymptotic model exponential and location model versions. J. Statist. Planning and Inference 66, 211-222.
  210. Andrews, D.F., Fraser, D.A.S. and Wong, A. (2005). Computation of distribution functions from likelihood information near observed data. Journal Statist Planning and Inference 134, 180-193.
  211. Fraser, D.A.S., and Reid, N. (2002) Strong matching of frequentist and Bayesian parametric inference. Journal of Statistical Planning and Inference. 103, 263-285.
  212. Fraser, D.A.S., and Yi, G. Y. (2002). Location reparameterization and default priors for statistical analysis. Jour. Iranian Statist. Soc. 1. 55-78.
  213. Fraser, D.A.S. (2004) Fiducial and structural statistical inference. International Encyclopedia of Social and Behavioral Sciences.5616-5620.
  214. Fraser, D.A.S., and Wong, A.(2003) Algebraic extraction of the canonical asymptotic model:Scalar case.(version 1) Technical Report, Department of Statistics, Univer. of Toronto(see 222)
  215. Fraser, D.A.S., Reid, N., Li, R., and Wong, A. (2003) p-value formulas from likeliood asymptotics: Bridging the singularities. J. Statist. Research. 37, 1-15
  216. Reid, N., and Fraser, D.A.S. (2000) Higher order asymptotics: Costs and Benefits. In C.R. Rao and G.J. Szekely (Eds.), Statistics for the 21st Century, 351-365. New York: Marcel Dekker.
  217. Fraser, D.A.S. (2002). Statistics: Foundations. Encyclopedia of Physical Science and Technology 15, 843-849.
  218. Fraser, D.A.S. (2004) Ancillaries and conditional inference. Statistical Science 19, 333-369.
  219. Fraser, D.A.S. (2003) Likelihood for interest parameters. Biometrika 90, 327-339.
  220. Reid, N., Mukerjee, R., Fraser, D.A.S. (2004) Some aspects of matching priors. Mathematical Statistics and Application: Festschrift for Constance Van Eeden. 42, 31-43.
  221. Fraser, D.A.S., Rekkas, M., Wong, A. (2005) Highly accurate likelihood analysis for the seemingly unrelated regression problem. Journal of Econometrics. 127, 17-33.
  222. Fraser, D.A.S., Wong, A. (2002) Algebraic extraction of the canonical asymptotic model, scalar case.(version 2) (see 214) Journal of Statistical Studies. Special volume October 2002,29-49.
  223. Fraser, D.A.S., Reid, N., Wong, A., and Yun Yi, G.(2003) Direct Bayes for interest parameters. Valencia 7, 529-533.
  224. Fraser, D.A.S., Reid, N. and Wong, A. C. M.(2004) Setting confidence intervals for bounded parameters: a different perspective. Physics Review D 69, 033002.
  225. Fraser, D.A.S., and Reid, N.(2006) Assessing vector parameters. Student 5, 247-256.
  226. Fraser, D.A.S., Wong, A. and Sun, Ye (2007) Bayesian or frequentist: Three enigmatic examples.
  227. Fraser, D.A.S., Wong, A. and Wu, J.(2004) Simple accurate and unique:The methods of modern likelihood theory. Pakistan Jour. of Statist 20, 173-192.
  228. Fraser, D.A.S. and Reid, N.(2002) Discussion of what is a statistical model. Annals Statist. 30, 1283-1286.
  229. Fraser, D.A.S. and Rousseau, J.(2006) Developing p-values: A Bayesian frequentist convergence.
  230. Davison, A.C., Fraser, D.A.S. and Reid, N.(2006) Improved likelihood inference for discrete data. J. Royal Statist. Soc. B68,495-508.
  231. Reid, N., Fraser, D.A.S. and Wong, A.C.M. (2004) What a model with data says about theta. International Jour. Statist Science 3, 163-177.
  232. Eaton, M.L. and Fraser, D.A.S.(2005) Studentization and prediction in a multivariate normal setting. Statist Neerlandica 59, 268-276.
  233. Reid, N. and Fraser, D.A.S.(2003) Likelihood inference in the presence of nuisance parameters. In Proceedings of PHYSTAT 2003, L. Lyons, R. Mount, R. Reitmeyer, eds. SLAC e-Conf C030908, 265-271.
  234. Fraser, D.A.S.(2004) On the discussion of ancillaries and conditional inference. Statist Science , 19, 363-369.
  235. Bedard, M., Fraser, D.A.S. and Wong, A. (2005) AMCMC and the validation of likelihood based p-values and Bayesian s-values, means and variances.
  236. Fraser, D.A.S., Yuan, X.(2007) Neutral priors.
  237. Ghosh, M., Reid, N. and Fraser, D.A.S. (2007) Ancillary statistics: A review.
  238. Fraser, D.A.S. and Naderi, A. (2007) Minimal sufficient statistics emerge from the observed likelihood function.
  239. Fraser, D.A.S., Reid, N., Marras, E., and Yi, G.Y. (2007) Default priors for Bayesian and frequentist inference.
  240. Fraser, D.A.S., and Staicu, A.M. (2007) The explicit second order ancillary.
  241. Fraser, D.A.S., (2006) Fiducial inference. The New Palgrave Dictionary of Economics, 2nd Edition
  242. Bedard, M. and Fraser, D.A.S., (2007). Adaptive MCMC inference methods.
  243. Davison, A., Fraser, D.A.S., Reid, N. and Sartori, N. (2007) Second order methods for categorical data analysis.
  244. Reid, N., Fraser, D.A.S. and Lai, H.J. Directional assessment of a vector parameter.
  245. Cai, Y., Faye, L. and Fraser, D.A.S. Is r* linear in r?

RECENT ADDRESSES
  1. Some directions for conditional inference. Inst. Math Statist. Lexington, LY. March 20, 1989
  2. Statistical inference: its contributions and directions. Environmetrics Conference. Cairo. April 6,1989
  3. Statistical inference: on reduction methods and new directions. University of Windsor. April 26, 1989
  4. Some recent results in inference. University of Madrid. June 9, 1989
  5. Conditional and marginal methods of inference. Amer. Statist. Assoc., Washington. August 8, 1989
  6. Some improvements for normal approximations in large samples. University of Montreal. October 4, 1989
  7. Improvements to normal approximations in large samples. University of Toronto. October 10, 1989
  8. Second order corrections to limiting normality in large samples. Amer. Math. Soc. meeting. Muncie, Indiana. October 27, 1989
  9. Some improvements for the normal approximation. York University. November 30, 1989
  10. Corrections to normality for inference. University of Waterloo. December 13, 1989
  11. How close can a normal approximation be? University of Western. January 19, 1990
  12. Processing likelihood to probability. Stanford University. February 20, 1990
  13. Data analysis and statistical foundations. Conference on Data Analysis and Statistical Foundations Toronto, Ontario. June 1, 1990
  14. Converting likelihood to significance. Statistical Society of Canada workshop. Niagara-on-the-Lake, Ontario. June 29, 1990
  15. From likelihood to significance: Linking the Fisher concepts. R.A. Fisher lecture and prize. Anaheim, California. August 8, 1990
  16. Statistical inference; likelihood to significance. Environmetrics International Meeting. Como, Italy. September 30, 1990
  17. Some new approaches to conditional inference. McMaster University. October 9, 1990.
  18. Statistical inference as likelihood to significance. University of Waterloo. November 1, 1990
  19. Likelihood functions and significance functions. Carleton University. November 23, 1990
  20. Converting likelihood to significance. Uiversity of Western Ontario. February 1, 1991
  21. Inference with semiparametric models. International Symposium on Nonparametric Methods. Carleton University. May 6, 1991
  22. Some recent asymptotics in statistical inference. University of California, Santa Barbara. March 6, 1992
  23. Higher order asymptotics with generalized linear models. International Symposium in Multivariate Analysis. Hong Kong. March 17, 1992
  24. Some recent directions in statistical inference. Department of Statistics, University of Waterloo. June 29, 1992
  25. Statistics as the meta language of science. Convocation address. University of Waterloo. June 30, 1992
  26. Ancillaries and third order significance. Invited address, Institute Mathematical Statistics, Penn State Unversity. October 26, 1992
  27. Methods of third order Significance. International Symposium on Inference and Biostatistics. Guanajuato, Mexico. March 23, 1993
  28. From likelihood to significance. University of South Florida. Tampa, Florida. April 9, 1993
  29. Significance with nuisance parameters. Rutgers University. New Jersey. April 8, 1993
  30. Towards closer connections between mathematics and statistics. 150 years of mathematics at the University of Toronto. May 15, 1993
  31. Statistical inference and significance for scalar parameters. University of Waterloo. February 10, 1994
  32. Objective priors for component parameters. Third Valencia Conference on Bayesian Statistics. Alicante, Spain. June 5, 1994
  33. On Statistics and the Environment. Environmetrics Conference. Hamilton, Canada. August 18, 1994
  34. Directions and approximations in statistical inference. Columbia Univ., Dept. of Statistics, New York. November 21, 1994
  35. Directions in recent statistical inference. Dept. of Statistics, Stanford University, California. February 28, 1995
  36. Separating error, Nuisance effect, and interest effect. International Conference on Statistical Inference. Brixen, Italy. June 25, 1995
  37. Asymptotics:Its contribution to the theory and methods of statistical inference. Institute of Mathematical Statistics. Montreal, Quebec. July 13, 1995
  38. Priors and posteriors for scalar parameters. International Bayesian Conference. Oaxaca, Mexico. September 29, 1995
  39. New theory in statistical inference. Dept of Statistics, Iowa State University. Ames, Iowa. November 3, 1995
  40. Some recent methods in statistical inference. International Conference in Pure and Applied Mathematics. University of Bahrain, Bahrain. November 19, 1995
  41. How do you like your likelihoods? Symposium on the Foundations of Statistical Inference. University of Waterloo. October 3, 1996
  42. Data dependent priors for scalar interest parameters. Workshop on Default Bayesian Methodology. Purdue University. November 2, 1996
  43. Nonsubjective priors for probability matching posteriors. 4th International Society Bayesian Analysis Meeting. Cape Town. December 17, 1996
  44. Probability matching: the frequency Bayesian Interface. University of Western Ontario.January 30, 1997
  45. The problem of inference and likelihood asymptotics. Florida Atlantic University International Conference on Combinatorics, Graph theory, and Computing Boca Raton, Florida. March 4, 1997
  46. The problem of inference and likelihood asymptotics. University of Toronto. January 30, 1997
  47. The problem of inference and likelihood asymptotics. Nonparametric Statistics and Related Topics. Carleton University, Ottawa. May 5, 1997
  48. Ancillary information for statistical inferencetics. International Conference on Multivariate Analysis. Hong Kong. June, 1997
  49. Constructing second order ancillaries. CRM Workshop on Likelihood Asymptotics. Banff. July, 1997
  50. The problem of inference and likelihood asymptotics. CRM University of Montreal Montreal. November 10, 1997
  51. Simple and accurate p-values. Dept of Statistics, Michigan State University. E Lansing, Michigan. March 24, 1998
  52. Inference from likelihood asymptotics: simple and unique. University of California. Davis, California. April 20, 1998
  53. Does the Bayesian need or want model information other than at the data point. Purdue University, Illinois. June 18, 1998
  54. Some useful integrals for asymptotic densities: The mystery of hyper-accuracy. University of Montreal, Montreal. December 3,1998
  55. Some useful integrals for asymptotic densities: The mystery of hyper-accuracy. University of Chicago. January 13, 1999
  56. Directions in likelihood asymptotics Statistical Society of Australia. Monash University, Melbourne, Australia. April 19, 1999
  57. Recent directions in likelihood asymptotics. University of Sydney. Sydney, Australia. April 23, 1999
  58. Strong matching of frequentist and Bayesian inference. International Workshop on Objective Bayesian Methodology. University of Valencia, Valencia. June 13, 1999
  59. Likelihood asymptotics: Removing the singularities. Bernoulli-IMS World Congress. Guanajuato, Mexico. May 18, 2000
  60. Ancillaries and Conditional Inference. Data Analysis and Statistical Foundations II, Fields Institute. Toronto, Canada. June 15, 2000
  61. Default priors: Deference priors from Observed Information. Workshop on Inference and Asymptotics. Center Stefano Franscini. Ascona, Italy. July 10, 2000
  62. Objective priors that defer to model shape. Third International Conference on Objective Bayes Methodology. Ixtapa. September 23, 2000
  63. Objective Bayesian analysis based on model shape. Florida State University. September 29, 2000
  64. Objective Bayesian analysis based on model shape. University of Western Ontario. November 30, 2000
  65. Frequentist or objective Bayesian analysis. University of Waterloo. December 7, 2000
  66. On the Bayesian frequentist interface. University of Toronto. January 11, 2001
  67. Likelihood for interest parameters. Stern School of Business. New York University. March 30, 2001
  68. Objective priors for scalar parameters. International Society for Bayesian Analysis. University of California Irvine. April 7, 2001
  69. Economic models and modern likelihood analysis. Fox School of Business, Temple University. April 27, 2001
  70. Some recent aspects of likelihood inference. Dalhousie University. November 8, 2001
  71. Likelihood for component parameters. University of Western Ontario. January 10, 2002
  72. Likelihood for component parameters. University of Waterloo. January 26, 2002
  73. Is the future Bayesian or frequentist I. Fields Institute, Toronto. April 16, 2002
  74. Is the future Bayesian or frequentist II. Fields Institute, Toronto. August 18, 2002
  75. Direct Bayes for an interest parameter. Vaencia 7, Tenerife. June 4, 2002
  76. Quarks and a bounded Poisson parameter. Padova, Italy. June 4, 2002. October 17, 2002
  77. How likelihood analysis gives simple inference results in general contexs. Swiss Federal Institute, Zurich. November 15, 2002.
  78. P-values without default priors. Workshop on Default priors, Granada, Spain. December 7, 2002
  79. Student analysis and higher order likelihood theory. EPFL Lausanne. January 17, 2003
  80. Recent likelihood theory: Analysis as simple as the Normal location scale case. University of Geneva. January 30, 2003
  81. Likelihood analysis and the SUR model. Dept. of Econometrics, University of Geneva. May 16, 2003
  82. Recent likelihood analysis. Dept. of Computer Science, University of Fribourg. May 23, 2003
  83. Is there total inference? Saddlepoint and beyond. Dept. of Mathematics, University of British Columbia. January 15, 2004
  84. Statistical models and the implications. Dept. of Statistics, University of Toronto. February 5, 2004
  85. Model with data; What does it say? Dept. of Statistics, University of Toronto, April 1, 2004
  86. Assessing vector parameteres. Conference for Sir David Cox Neuchatel July 16, 2004
  87. Is there statistical inference? The Baysian-frequentist divergence. Dept. of Statistics, Case Western Reserve University October 1, 2004
  88. Neutral priors. COBAL2 Baja, California, Mexico. February 6-10, 2005
  89. Why a prior? Dept of Statistics, Univ of Toronto Munk Centre April 28, 2005
  90. Objective and other priors. OBayes 5 Branson, Missouri. June 8 2005
  91. What model information is appropriate for the Bayesian paradigm? Statistical Society of Canada, Annual meeting. Saskatoon, Saskatchewan. June 15, 2005
  92. Some thoughts on model based priors. Dept of Statistics, University of Waterloo Waterloo, Canada. November 17, 2005

RECENT DOCTORAL THESES

Michael Brimacombe, 1991.

On a conditional approach to nonlinear regression

Piang Kew Cheah, 1993.

Third order approximation in multiparameter exponential models

Sabit Cakmak, 1994.

Exponential and location type approximations

Fisseha Abebe, 1994.

Nonlinear regression: Third order.

Jonathan Kuhn, 1994.

Parameter forcing

Jean Yuan, 1996.

An asymptotic analysis of the Box and Cox problem

Qingning Zeng, 1996.

The Behrens Fisher problem and likelihood asymptotics

Nathan Taback, 1998.

Likelihood analyses of location-scale-shape models

Jianrong Wu, 1999.

Likelihood analysis

Mahdi Alkhamisi, 2000.

Likelihood analysis of random effect models

Grace Yun Yi, 2000.

Location reparameterization of multivariate models

Rongcai Li, 2001.

Likelihood asymptotics: Removing the singularity

Xiaobin Yuan, 2005

Neutral priors for Bayesian inference