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17

Curriculum Vitae Rohmer Tom


Journal articles8 documents

  • Vincent Le, Tom Rohmer, Ingrid David. Impact of environmental disturbances on estimated genetic parameters and breeding values for growth traits in pigs. Animal, 2022, 16 (4), 9 p. ⟨10.1016/j.animal.2022.100496⟩. ⟨hal-03653106⟩
  • Alexandre Brouste, Christophe Dutang, Tom Rohmer. A Closed-form Alternative Estimator for GLM with Categorical Explanatory Variables. Communications in Statistics - Simulation and Computation, In press, pp.1-17. ⟨10.1080/03610918.2022.2076870⟩. ⟨hal-03689206⟩
  • Tom Rohmer, Anne Ricard, Ingrid David. Copula miss-specification in REML multivariate genetic animal model estimation. Genetics Selection Evolution, 2022, 54 (1), pp.36. ⟨10.1186/s12711-022-00729-3⟩. ⟨hal-03681151⟩
  • Alexandre Brouste, Christophe Dutang, Tom Rohmer. Closed form Maximum Likelihood Estimator for Generalized Linear Models in the case of categorical explanatory variables: Application to insurance loss modelling. Computational Statistics, 2020, ⟨10.1007/s00180-019-00918-7⟩. ⟨hal-01781504v3⟩
  • Antoine Dowek, Laetitia Minh Mai Lê, Tom Rohmer, François-Xavier Legrand, Hynd Remita, et al.. A mathematical approach to deal with nanoparticle polydispersity in surface enhanced Raman spectroscopy to quantify antineoplastic agents. Talanta, 2020, 217, pp.121040. ⟨10.1016/j.talanta.2020.121040⟩. ⟨hal-02557279⟩
  • Ivan Kojadinovic, J.-F. Quessy, Tom Rohmer. Testing the constancy of Spearman’s rho in multivariate time series. Annals of the Institute of Statistical Mathematics, Springer Verlag, 2016, 68 (5), pp.929-954. ⟨10.1007/s10463-015-0520-2⟩. ⟨hal-01581271⟩
  • Tom Rohmer. Some results on change-point detection in cross-sectional dependence of multivariate data with changes in marginal distributions. Statistics and Probability Letters, Elsevier, 2016, 119, pp.45-54. ⟨10.1016/j.spl.2016.06.026⟩. ⟨hal-03187746⟩
  • A. Bücher, Ivan Kojadinovic, Tom Rohmer, J. Segers. Detecting changes in cross-sectional dependence in multivariate time series. Journal of Multivariate Analysis, Elsevier, 2014, 132, pp.111-128. ⟨10.1016/j.jmva.2014.07.012⟩. ⟨hal-02158618⟩

Conference papers4 documents

  • Tom Rohmer, Anne Ricard, Ingrid David. Impact d'une copule non-Gaussienne dans l'estimation REML du mod èle g én étique animal bivari é pour des populations sous s élection. Journées des Statistiques 2022, Jun 2022, Lyon, France. ⟨hal-03699461⟩
  • Tom Rohmer, Anne Ricard, Ingrid David. Impact of a non-Gaussian dependence structure on REML estimation of the bivariate genetic animal model. World Congress on Genetics Applied to Livestock Production (WCGALP 2022), Jul 2022, Rotterdam, Netherlands. ⟨hal-03719859⟩
  • Vincent Le, Tom Rohmer, Florence Ytournel, Loïc Flatres-Grall, Bruno Ligonesche, et al.. Evaluation de l’impact des perturbations sur l’estimation des paramètres et la prédiction des valeurs génétiques. 51ème JRP, Feb 2021, paris, France. ⟨hal-03166109⟩
  • Tom Rohmer. Un estimateur explicite pour le GLM à variables explicatives catégorielle. Conférence de clôture du programme PanoRISK, Nov 2021, Le Mans, France. ⟨hal-03681142⟩

Conference poster3 documents

  • Vincent Le, Tom Rohmer, Ingrid David. Identifying and characterizing disturbances from high-throughput phenotyping data. EAAP, Aug 2021, Davos, Switzerland. ⟨hal-03619061⟩
  • Vincent V Le, Ingrid David, Tom Rohmer. Identifying and characterizing disturbances from high-throughput phenotyping data. EAAP, Aug 2021, davos, Switzerland. ⟨hal-03360534⟩
  • Vincent Le, Tom Rohmer, Florence Ytournel, Loïc Flatres-Grall, Bruno Ligonesche, et al.. Evaluation de l'impact des perturbations sur l'estimation des paramètres et la prédiction des valeurs génétiques. JRP, Feb 2021, Paris, France. 1p. ⟨hal-03619066⟩

Preprints, Working Papers, ...1 document

  • Alexandre Brouste, Anis Matoussi, Tom Rohmer, Christophe Dutang, Vanessa Désert, et al.. Solvency tuned premium for a composite loss distribution. 2018. ⟨hal-01883508⟩

Master thesis1 document

  • Tom Rohmer. Propagation de la lumière dans les tissus biologiques par méthode de Monte Carlo par Chaînes de Markov. Méthodologie [stat.ME]. 2011. ⟨dumas-00623050⟩