References in zbMATH (referenced in 54 articles , 1 standard article )

Showing results 1 to 20 of 54.
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  1. Sachs, M. C.; Gabriel, E. E.: Event History Regression with Pseudo-Observations: Computational Approaches and an Implementation in R (2022) not zbMATH
  2. Elliott, Corrine F.; Lambert, Joshua W.; Stromberg, Arnold J.; Wang, Pei; Zeng, Ting; Thompson, Katherine L.: Feasibility as a mechanism for model identification and validation (2021)
  3. Hector, Emily C.; Song, Peter X.-K.: A distributed and integrated method of moments for high-dimensional correlated data analysis (2021)
  4. Huang, Youjun; Pan, Jianxin: Joint generalized estimating equations for longitudinal binary data (2021)
  5. Kruppa, Jochen; Hothorn, Ludwig: A comparison study on modeling of clustered and overdispersed count data for multiple comparisons (2021)
  6. M. Helena Gonçalves, M. Salomé Cabral: cold: An R Package for the Analysis of Count Longitudinal Data (2021) not zbMATH
  7. Wollschläger, Daniel: R compact. The fast introduction into data analysis (2021)
  8. Achim Zeileis, Susanne Köll, Nathaniel Graham: Various Versatile Variances: An Object-Oriented Implementation of Clustered Covariances in R (2020) not zbMATH
  9. Bradley C. Saul, Michael G. Hudgens: The Calculus of M-Estimation in R with geex (2020) not zbMATH
  10. Hector, Emily C.; Song, Peter X.-K.: Doubly distributed supervised learning and inference with high-dimensional correlated outcomes (2020)
  11. Nikoloulopoulos, Aristidis K.: Weighted scores estimating equations and CL1 information criteria for longitudinal ordinal response (2020)
  12. Park, Seongoh; Lim, Johan; Choi, Hyejeong; Kwak, Minjung: Clustering of longitudinal interval-valued data via mixture distribution under covariance separability (2020)
  13. Tracie L. Shing, John S. Preisser, Richard C. Zink: GEECORR: A SAS macro for regression models of correlated binary responses and within-cluster correlation using generalized estimating equations (2020) arXiv
  14. da Silva, José L. P.; Colosimo, Enrico A.; Demarqui, Fábio N.: A general GEE framework for the analysis of longitudinal ordinal missing data and related issues (2019)
  15. Inan, Gul; Latif, Mahbub A. H. M.; Preisser, John: A PRESS statistic for working correlation structure selection in generalized estimating equations (2019)
  16. Pavlič, Klemen; Martinussen, Torben; Andersen, Per Kragh: Goodness of fit tests for estimating equations based on pseudo-observations (2019)
  17. Saul, Bradley C.; Hudgens, Michael G.; Mallin, Michael A.: Downstream effects of upstream causes (2019)
  18. Marchese, Scott; Diao, Guoqing: Joint regression analysis of mixed-type outcome data via efficient scores (2018)
  19. Wagner Bonat: Multiple Response Variables Regression Models in R: The mcglm Package (2018) not zbMATH
  20. Worku, Hailemichael M.; de Rooij, Mark: A multivariate logistic distance model for the analysis of multiple binary responses (2018)

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