References in zbMATH (referenced in 23 articles , 1 standard article )

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  1. Baumgartner, Matheus Tenório; Faria, Lucas Del Bianco: The sensitivity of complex dynamic food webs to the loss of top omnivores (2022)
  2. Barraquand, Frédéric; Gimenez, Olivier: Fitting stochastic predator-prey models using both population density and kill rate data (2021)
  3. Kruppa, Jochen; Hothorn, Ludwig: A comparison study on modeling of clustered and overdispersed count data for multiple comparisons (2021)
  4. Chaves, Luis Fernando; Hurtado, Lisbeth A.; Rojas, Melissa Ramírez; Friberg, Mariel D.; Rodríguez, Rodrigo Marín; Avila-Aguero, María L.: COVID-19 basic reproduction number and assessment of initial suppression policies in Costa Rica (2020)
  5. Fuetterer, Cornelia; Augustin, Thomas; Fuchs, Christiane: Adapted single-cell consensus clustering (adaSC3) (2020)
  6. Li, Meili; Wang, Hong; Song, Baojun; Ma, Junling: The spread of influenza-like-illness within the household in Shanghai, China (2020)
  7. Ellis, John; Petrovskaya, Natalia; Petrovskii, Sergei: Effect of density-dependent individual movement on emerging spatial population distribution: Brownian motion vs levy flights (2019)
  8. Chaves, Luis Fernando: Survival schedules and the estimation of the basic reproduction number ((\mathrmR_0)) without the assumption of extreme cases (2018)
  9. Tian, Yun; Al-Darabsah, Isam; Yuan, Yuan: Global dynamics in sea lice model with stage structure (2018)
  10. Gjini, Erida; Valente, Carina; Sá-Leão, Raquel; Gomes, M. Gabriela M.: How direct competition shapes coexistence and vaccine effects in multi-strain pathogen systems (2016)
  11. Peng, Liuhua; Chen, Song Xi; Zhou, Wen: More powerful tests for sparse high-dimensional covariances matrices (2016)
  12. Stoklosa, Jakub; Huang, Yih-Huei; Furlan, Elise; Hwang, Wen-Han: On quadratic logistic regression models when predictor variables are subject to measurement error (2016)
  13. Kozubowski, Tomasz J.; Panorska, Anna K.; Forister, Matthew L.: A discrete truncated Pareto distribution (2015)
  14. Xu, Ganggang; Genton, Marc G.: Efficient maximum approximated likelihood inference for Tukey’s (g)-and-(h) distribution (2015)
  15. Ma, Junling; Dushoff, Jonathan; Bolker, Benjamin M.; Earn, David J. D.: Estimating initial epidemic growth rates (2014)
  16. Millar, R. B.; McKechnie, S.: A one-step-ahead pseudo-DIC for comparison of Bayesian state-space models (2014)
  17. Cournède, P.-H.; Chen, Y.; Wu, Q.; Baey, C.; Bayol, B.: Development and evaluation of plant growth models: methodology and implementation in the PYGMALION platform (2013)
  18. Dormann, Carsten F.: Parametric statistics. Distributions, maximum likelihood and GLM in R (2013)
  19. White, S. M.; Burden, J. P.; Maini, P. K.; Hails, R. S.: Modelling the within-host growth of viral infections in insects (2012)
  20. Corcoran, Jonathan; Higgs, Gary; Rohde, David; Chhetri, Prem: Investigating the association between weather conditions, calendar events and socio-economic patterns with trends in fire incidence: an australian case study (2011) ioport

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