Dr. William Aeberhard

ASSISTANT PROFESSOR
School: Schaefer School of Engineering & Science
Department: Mathematical Sciences
Building: Peirce
Room: 304
Phone: 201.216.5441
Email: william.aeberhard@stevens.edu
Website
Education
  • PhD in Statistics (2015) University of Geneva and University of Sydney
  • MSc in Statistics (2010) University of Geneva
  • BSc in Psychology (2008) University of Geneva
Research
  • Robust statistics
  • Non- and semi-parametric methods
  • Ecological applications
  • Spatio-temporal modeling
  • Computational statistics
Selected Publications
Journals
  1. E. Lawler, K. Whoriskey, W. H. Aeberhard, J. Mills Flemming, and C. Field. (2019). "The conditionally autoregressive hidden Markov model (CarHMM): Inferring behavioural states from animal tracking data exhibiting conditional autocorrelation", Journal of Agricultural, Biological, and Environmental Statistics 24 (4), 651-668.
  2. Y. Yin, W. H. Aeberhard, S. J. Smith, and J. Mills Flemming. (2018). "Identifiable state-space models: A case study of the Bay of Fundy sea scallop fishery", Canadian Journal of Statistics 47 (1), 27-47.
  3. W. H. Aeberhard, J. Mills Flemming, and A. Nielsen. (2018). "Review of State-Space Models for Fisheries Science", Annual Review of Statistics and Its Application 5, 215-235.
  4. O. Defeo, C. A. M. Barboza, W. H. Aeberhard, F. R. Barboza, T. Cabrini, R. Cardoso, L. Ortega, V. Skinner, and B. Worm. (2017). "Aggregate patterns of macrofaunal diversity: an interocean comparison", Global Ecology and Biogeography 26 (7), 823-834.
  5. W. H. Aeberhard, E. Cantoni, and S. Heritier. (2017). "Saddlepoint tests for accurate and robust inference on overdispersed count data", Computational Statistics & Data Analysis 107, 162-175.
  6. S. Biass, G. Bagheri, W. H. Aeberhard, and C. Bonadonna. (2014). "TError: Towards a Better Quantification of the Uncertainty Propagated during the Characterization of Tephra Deposits.", Statistics in Volcanology 1 (2), 1-27.
  7. W. H. Aeberhard, E. Cantoni, and S. Heritier. (2014). "Robust Inference in the Negative Binomial Regression Model with an Application to Falls Data", Biometrics 70 (4), 920-931.
Book Chapters
  1. W. H. Aeberhard and E. Cantoni. (2015). "Le Modele lineaire generalise (GLM) robuste.", Methodes robustes en statistique, Ed. by J.-J. Droesbeke, G. Saporta, and C. Thomas-Agnan, Paris, France: Technip, 109-128.