References in zbMATH (referenced in 204 articles , 1 standard article )

Showing results 1 to 20 of 204.
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  1. Alex Stringer: Implementing Adaptive Quadrature for Bayesian Inference: the aghq Package (2021) arXiv
  2. Cochrane, Courtney; Ba, Demba; Klerman, Elizabeth B.; St. Hilaire, Melissa A.: An ensemble mixed effects model of sleep loss and performance (2021)
  3. Ferri-García, Ramón; Castro-Martín, Luis; del Mar Rueda, María: Evaluating machine learning methods for estimation in online surveys with superpopulation modeling (2021)
  4. Hu, Xinyu; Qian, Min; Cheng, Bin; Cheung, Ying Kuen: Personalized policy learning using longitudinal mobile health data (2021)
  5. Thomas, Abin; Vishwakarma, Gajendra K.; Bhattacharjee, Atanu: Joint modeling of longitudinal and time-to-event data on multivariate protein biomarkers (2021)
  6. Aaron Cochrane: TEfits: Nonlinear regression for time-evolving indices (2020) not zbMATH
  7. Achim Zeileis, Susanne Köll, Nathaniel Graham: Various Versatile Variances: An Object-Oriented Implementation of Clustered Covariances in R (2020) not zbMATH
  8. Bradley, Jonathan R.; Holan, Scott H.; Wikle, Christopher K.: Bayesian hierarchical models with conjugate full-conditional distributions for dependent data from the natural exponential family (2020)
  9. Cho, Sun-Joo; Brown-Schmidt, Sarah; De Boeck, Paul; Shen, Jianhong: Modeling intensive polytomous time-series eye-tracking data: a dynamic tree-based item response model (2020)
  10. Christen, J. Andrés; Parker, Albert E.: Systematic statistical analysis of microbial data from dilution series (2020)
  11. Cunen, Céline; Walløe, Lars; Hjort, Nils Lid: Focused model selection for linear mixed models with an application to whale ecology (2020)
  12. Gupta, Bhisham C.; Guttman, Irwin; Jayalath, Kalanka P.: Statistics and probability with applications for engineers and scientists using MINITAB, R and JMP (2020)
  13. Hall, P.; Johnstone, I. M.; Ormerod, J. T.; Wand, M. P.; Yu, J. C. F.: Fast and accurate binary response mixed model analysis via expectation propagation (2020)
  14. Matthias Speidel, Jörg Drechsler, Shahab Jolani: The R Package hmi: A Convenient Tool for Hierarchical Multiple Imputation and Beyond (2020) not zbMATH
  15. Maurizio Manuguerra, Gillian Z. Heller, Jun Ma: Continuous Ordinal Regression for Analysis of Visual Analogue Scales: The R Package ordinalCont (2020) not zbMATH
  16. Miller, David L.; Glennie, Richard; Seaton, Andrew E.: Understanding the stochastic partial differential equation approach to smoothing (2020)
  17. Murakami, Daisuke; Griffith, Daniel A.: A memory-free spatial additive mixed modeling for big spatial data (2020)
  18. Qian, Tianchen; Klasnja, Predrag; Murphy, Susan A.: Linear mixed models with endogenous covariates: modeling sequential treatment effects with application to a mobile health study (2020)
  19. Roustant, Olivier; Padonou, Espéran; Deville, Yves; Clément, Aloïs; Perrin, Guillaume; Giorla, Jean; Wynn, Henry: Group kernels for Gaussian process metamodels with categorical inputs (2020)
  20. Titz, J.: mimosa: A Modern Graphical User Interface for 2-level Mixed Models (2020) not zbMATH

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