Sam Weerahandi

Samaradasa Weerahandi, is the first Sri Lankan American statistician to be honored as a Fellow of the American Statistical Association.[1] Also known as Sam Weerahandi, he is a former professor last employed in Corporate America by Pfizer, Inc. as a Senior Director until December 2016.

Weerahandi introduced a number of notions, concepts, and methods for statistical analysis of small samples based on exact probability statements, which are referred to as exact statistics. [2] [3] Commonly known as generalized inferences, the new concepts include generalized p-value generalized confidence intervals and generalized point estimation. These methods, which are discussed in the two books he wrote, have been found to produce more accurate inferences compared to classical methods based on asymptotic methods when the sample size is small or when large samples tends to be noisy. [4] He used statistical techniques based on these notions to bring statistical practice into business management.

Bibliography
  • Exact Statistical Methods for Data Analysis", Springer-Verlag, New York, 1995
  • Generalized Inference in Repeated

Measures: Exact Methods in MANOVA and Mixed Models. Wiley, Hoboken, New Jersey, 2004.

gollark: Yep.
gollark: That Is Not Possible.
gollark: Or just one size-24 one running D-D.
gollark: If you use a LOT of fusion reactors that could also work.
gollark: What do you need it for?

References
  1. 1996 (choose initial W and then click submit) http://www.amstat.org/awards/fellowslist.cfm
  2. Liao, Chen-Tuo; Li, Chi-Rong (2010). "Generalized Inference". Encyclopedia of Biopharmaceutical Statistics. pp. 547–549. doi:10.3109/9781439822463.088. ISBN 978-1-4398-2246-3.
  3. http://www.ucs.louisiana.edu/~kxk4695/Biometrics-2003.pdf
  4. http://www.weerahandi.org/
  • Ananda, M. M. A. (2003). Confidence intervals for steady state availability of a system with exponential operating time and lognormal repair time. Applied Mathematics and Computation, 137, 499-509.
  • Bebu, I., and Mathew, T. (2009). Confidence intervals for limited moments and truncated moments in normal and lognormal models. Statistics and Probability Letters, 79, 375-380
  • Gamage, J., Mathew, T. and Weerahandi S. (2013). Generalized prediction intervals for BLUPs in mixed models, Journal of

Multivariate Analysis, 220, 226-233.

  • Hamada, M., and Weerahandi, S. (2000). Measurement System Assessment via Generalized Inference. Journal of Quality Technology, 32, 241-253.
  • Hanning, J., Iyer, H., and Patterson, P. (2006). Fiducial generalized confidence intervals. Journal of the American Statistical Association, 101, 254-269.
  • Krishnamoorthy, K., and Mathew, T. (2009). Statistical Tolerance Regions, Wiley Series in Probability and Statistics.
  • Lee, J. C., and Lin, S. H. (2004). Generalized confidence intervals for the ratio of means of two normal populations. Journal of Statistical Planning and Inference, 123, 49-60.
  • Li, X., Wang J., Liang H. (2011). Comparison of several means: a fiducial based approach.

Computational Statistics and Data Analysis}, 55, 1993-2002.

  • Tian, L. (2008). Generalized Inferences on the Overall Treatment Effect in Meta-analysis with Normally Distributed Outcomes, Biometrical Journal, 50, 237-247.
  • Mathew, T. and Webb, D. W. (2005). Generalized p-values and confidence intervals for variance components:

Applications to Army test and evaluation, Technometrics, 47, 312-322.

  • Mu, W., and Wang, X. (2014). Inference for One-Way ANOVA with Equicorrelation Error Structure, The Scientific World Journal.
  • Tsui, K., and Weerahandi, S. (1989). Generalized p-Values in Significance Testing of Hypotheses in the Presence of Nuisance Parameters. JASA, 18, 586-589.
  • Xiong S. (2011). An asymptotics look at the generalized inference, Journal of Multivariate Analysis, 102, 336–348.
  • Weerahandi, S. (1993). Generalized Confidence Intervals. JASA, 88, 899-905.
  • Wu, J.F., and Hamada, M.S. (2009). Experiments: Planning, Analysis, and Optimization, Wiles Series in Probability and Statistics.
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