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William H. Aeberhard
William H. Aeberhard
Swiss Data Science Center, ETH Zurich
Verified email at sdsc.ethz.ch - Homepage
Title
Cited by
Cited by
Year
Robust inference in the negative binomial regression model with an application to falls data
WH Aeberhard, E Cantoni, S Heritier
Biometrics 70 (4), 920-931, 2014
512014
Review of state-space models for fisheries science
WH Aeberhard, J Mills Flemming, A Nielsen
Annual Review of Statistics and Its Application 5 (1), 215-235, 2018
502018
TError: towards a better quantification of the uncertainty propagated during the characterization of tephra deposits
S Biasse, G Bagheri, W Aeberhard, C Bonadonna
Statistics in Volcanology 1 (2), 1-27, 2014
272014
Aggregate patterns of macrofaunal diversity: An interocean comparison
O Defeo, CAM Barboza, FR Barboza, WH Aeberhard, T Cabrini, ...
Global Ecology and Biogeography 26 (7), 823-834, 2017
232017
Identifiable state‐space models: A case study of the Bay of Fundy sea scallop fishery
Y Yin, WH Aeberhard, SJ Smith, J Mills Flemming
Canadian Journal of Statistics 47 (1), 27-45, 2019
112019
The conditionally autoregressive hidden Markov model (CarHMM): Inferring behavioural states from animal tracking data exhibiting conditional autocorrelation
E Lawler, K Whoriskey, WH Aeberhard, C Field, JM Flemming
Journal of Agricultural, Biological and Environmental Statistics 24 (4), 651-668, 2019
72019
Robust Fitting and Smoothing Parameter Selection for Generalized Additive Models for Location, Scale and Shape
WH Aeberhard, E Cantoni, G Marra, R Radice
Statistics and Computing 31 (11), 1-16, 2021
52021
Saddlepoint tests for accurate and robust inference on overdispersed count data
WH Aeberhard, E Cantoni, S Heritier
Computational Statistics & Data Analysis 107, 162-175, 2017
52017
Unified natural mortality estimation for teleosts and elasmobranchs
M Dureuil, WH Aeberhard, KA Burnett, RE Hueter, JP Tyminski, B Worm
Marine Ecology Progress Series 667, 113-129, 2021
42021
Insights into the vulnerability of vegetation to tephra fallouts from interpretable machine learning and big Earth observation data
S Biass, SF Jenkins, WH Aeberhard, P Delmelle, T Wilson
Natural Hazards and Earth System Sciences Discussions, 1-55, 2022
2022
Robust estimation for discrete‐time state space models
WH Aeberhard, E Cantoni, C Field, HR Künsch, J Mills Flemming, X Xu
Scandinavian Journal of Statistics 48 (4), 1127-1147, 2021
2021
Workshop on the review and future of state space stock assessment models in ICES (WKRFSAM)
A Nielsen, A Perreault, CW Berg, C Albertsen, C Minto, C Millar, ...
Workshop on the review and future of state space stock assessment models in ICES, 2020
2020
Comparison of robust saddlepoint tests: the negative binomial regression case
S Heritier, W Aeberhard, E Cantoni
Book of Abstracts, 21, 2017
2017
State-Space Models for Improved Predictions in Fisheries Management
W Aeberhard
2017 AAAS Annual Meeting (February 16-20, 2017), 2017
2017
Le modèle linéaire généralisé (GLM) robuste
W Aeberhard, E Cantoni
Méthodes robustes en statistique, Ed. by J.-J. Droesbeke, G. Saporta, and C …, 2015
2015
Contributions to overdispersed count data modeling: robustness, small samples and other extensions
W Aeberhard
University of Geneva, 2015
2015
TError
S Biass, G Bagheri, W Aeberhard, C Bonadonna
2014
Power and Sample Size Calculation in a Negative Binomial Regression Framework: The Power of Falls: Might the" Holy Trinity" Help?
W Aeberhard
University of Geneva, 2010
2010
Web-based Supporting Material for Robust Fitting and Smoothing Parameter Selection for Generalized Additive Models for Location, Scale and Shape
WH Aeberhard, E Cantoni, G Marra, R Radice
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Articles 1–19