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Birkes, David S

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Statistics Department
Oregon State University
Corvallis, Oregon 97331-4606

Voice: (541) 737-1986; FAX: (541) 737-3489;
Office: Kidder 60

Education:

  • Stanford University, B.S. Mathematics, 1964
  • University of Chicago, M.S. Mathematics, 1966
  • University of Washington, Ph.D. Mathematics, 1969
  • Oregon State University, M.S. Statistics, 1972

 

Current Research Interests:

Linear models, mathematical statistics

Doctoral Student Dissertations:

  • Kerry Ritter (2001) Statistical aspects of two measurement problems: Defining taxonomic richness and testing with unanchored responses (joint with Scott Urquhart)
  • Shaun Wulff (1999) Explicit linear maximum likelihood estimation in mixed models
  • Theresa Utlaut (1999) F-tests in partially balanced and unbalanced mixed linear models
  • Dawn VanLeeuwen (1993) Completeness and sufficiency under normality in mixed model designs  (joint with Justus Seely)
  • Yulan Li (1993) Confidence intervals and tests for variance-component ratios in mixed linear models  (joint with David Thomas)
  • Adel Halawa (1988) Testing for location after transformation to normality
  • Oui Siripanich (1987) Estimating root mean squared error in the one-way random model
  • El Houssainy Rady (1986) Testing fixed effects in mixed linear models (joint with Justus Seely)
  • Abdul-Mordy Azzam (1980) Admissibility in linear models (joint with Justus Seely)

 

Selected Publications:

  1. S.S. Wulff and D. Birkes (2005). Minimum variance unbiased invariant estimation of variance components under normality. Statistics: A Journal of Theoretical and Applied Statistics 39: 53-65.
  2. D. Birkes (2005). Likelihood ratio tests. In: P. Armitage, T. Colton (eds.), Encyclopedia of Biostatistics, 2nd ed., Wiley.
  3. S.S. Wulff and D. Birkes (2005). Best unbiased estimation of fixed effects in mixed ANOVA models. Journal of Statistical Planning and Inference 133: 305-313.
  4. D. Birkes and S.S. Wulff (2003). Existence of maximum likelihood estimates in normal variance-components models. Journal of Statistical Planning and Inference 113: 35-47.
  5. D.L. VanLeeuwen,, D.S. Birkes and J.F. Seely (1999). Balance and orthogonality in designs for mixed classification models. Annals of Statistics 27: 1927-1947.
  6. D.L. VanLeeuwen, J.F. Seely and D.S. Birkes (1998). Sufficient conditions for orthogonal designs in mixed linear models. J. Statistical Planning and Inference 73: 373-389.
  7. J.F. Seely, D. Birkes and Y. Lee (1997). Characterizing sums of squares by their distributions. American Statistician 51: 55-58.
  8. B.H. Liu, S.J. Knapp and D.S. Birkes (1997). Sampling distributions, biases, variances, and confidence intervals for genetic correlations. Theoetical and Applied Genetics 94: 8-19.
  9. T.L. McDonald, D.S. Birkes and N.S. Urquhart (1996). Obtaining species: sample size considerations. Environmental and Ecological Statistics 3: 329-347.
  10. Y. Li, D. Birkes and D. Thomas (1996). The residual likelihood ratio test for the variance ratio in a linear model with two variance components. Biometrical J. 38: 961-972.
  11. Y. Lee and D. Birkes (1994). Shrinking toward submodels in regression. J. Statistical Planning and Inference, 41: 95-111.
  12. D. Birkes and Y. Dodge (1993). Alternative Methods of Regression. Wiley.
  13. W.T. Adams and D.S. Birkes (1991). Estimating patterns in forest tree populations. In: S. Fineschi, M.E. Malvolti, F. Cannata and H.H. Hattemer (eds.), Biochemical Markers in the Population Genetics of Forest Trees, SPB
    Scientific Publishing, The Hague, 157-172.