Health Research Methods, Evidence, and Impact

Angelo J. Canty

BSc (University College, Cork), MSc(Toronto), PhD (Toronto)

Assistant Professor, Department of Mathematics & Statistics

Associate Member, Department of Health Research Methods, Evidence, and Impact

905.525.9140 x27079

McMaster University
Department of Mathematics and Statistics
1280 Main Street West
Hamilton, Ontario
L8S 4K1

Office location: HH-209

Academic Interests

My research interests are mainly in the areas of computational statistics such as Monte Carlo Markov Chain methodology; bootstrap and other resampling methods; Methods for the analysis of data from genetic microarray experiments; graphical methods; Monte Carlo Inference; and Efficient computer implementation of statistical algorithms.

Selected Publications

  1. Babu, G. J., Canty, A. J. and Chaubey, Y. P. (2002) Application of Bernstein Polynomials for Smooth Estimation of a Distribution and Density Function. Journal of Statistical Planning and Inference, 105, 377–392
  2. Canty, A. J. and Davison, A. C. (2000) Discussion of “The Estimating Function Bootstrap” by F. Hu and J. D. Kalbfleisch. Canadian Journal of Statistics 28, 489–493.
  3. Canty, A. J. (1999) Hypothesis Tests of Convergence in Markov Chain Monte Carlo. Journal of Computational and Graphical Statistics, 8, 93–108
  4. Canty, A. J. and Davison, A. C. (1999) Implementation of Saddlepoint Approximations in Resampling Problems. Statistics and Computing, 9, 9–15.
  5. Canty, A. J. and Davison, A. C. (1999) Resampling-based Variance Estimation for Labour Force Surveys. The Statistician, 48, 379–391.
  6. Canty, A. J., Davison, A. C. and Hinkley, D. V. (1996) Reliable Confidence Intervals; A Discussion of “Bootstrap Confidence Intervals” by T. J. DiCiccio and B. Efron, Statistical Science 11, 214–219.

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