References in zbMATH (referenced in 181 articles , 1 standard article )

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  1. Thomas, Abin; Vishwakarma, Gajendra K.; Bhattacharjee, Atanu: Joint modeling of longitudinal and time-to-event data on multivariate protein biomarkers (2021)
  2. Aaron Cochrane: TEfits: Nonlinear regression for time-evolving indices (2020) not zbMATH
  3. 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)
  4. Cunen, Céline; Walløe, Lars; Hjort, Nils Lid: Focused model selection for linear mixed models with an application to whale ecology (2020)
  5. Gupta, Bhisham C.; Guttman, Irwin; Jayalath, Kalanka P.: Statistics and probability with applications for engineers and scientists using MINITAB, R and JMP (2020)
  6. Miller, David L.; Glennie, Richard; Seaton, Andrew E.: Understanding the stochastic partial differential equation approach to smoothing (2020)
  7. Murakami, Daisuke; Griffith, Daniel A.: A memory-free spatial additive mixed modeling for big spatial data (2020)
  8. Roustant, Olivier; Padonou, Espéran; Deville, Yves; Clément, Aloïs; Perrin, Guillaume; Giorla, Jean; Wynn, Henry: Group kernels for Gaussian process metamodels with categorical inputs (2020)
  9. Yoon, Hwan-Jin; Welsh, Alan H.: On the effect of ignoring correlation in the covariates when fitting linear mixed models (2020)
  10. Ann-Kristin Kreutzmann; Sören Pannier; Natalia Rojas-Perilla; Timo Schmid; Matthias Templ; Nikos Tzavidis: The R Package emdi for Estimating and Mapping Regionally Disaggregated Indicators (2019) not zbMATH
  11. Baey, Charlotte; Cournède, Paul-Henry; Kuhn, Estelle: Asymptotic distribution of likelihood ratio test statistics for variance components in nonlinear mixed effects models (2019)
  12. Bon, Joshua J.; Murray, Kevin; Turlach, Berwin A.: Fitting monotone polynomials in mixed effects models (2019)
  13. Flores-Agreda, Daniel; Cantoni, Eva: Bootstrap estimation of uncertainty in prediction for generalized linear mixed models (2019)
  14. Haziq Jamil, Wicher Bergsma: iprior: An R Package for Regression Modelling using I-priors (2019) arXiv
  15. Heathcote, Andrew; Holloway, Eleanor; Sauer, James: Confidence and varieties of bias (2019)
  16. Heck, Daniel W.: Accounting for estimation uncertainty and shrinkage in Bayesian within-subject intervals: a comment on Nathoo, Kilshaw, and Masson (2018) (2019)
  17. Hui, F. K. C.; Müller, Samuel; Welsh, A. H.: Testing random effects in linear mixed models: another look at the F-test (with discussion) (2019)
  18. Ippel, L.; Kaptein, M. C.; Vermunt, J. K.: Online estimation of individual-level effects using streaming shrinkage factors (2019)
  19. Lee, Wonyul; Miranda, Michelle F.; Rausch, Philip; Baladandayuthapani, Veerabhadran; Fazio, Massimo; Downs, J. Crawford; Morris, Jeffrey S.: Bayesian semiparametric functional mixed models for serially correlated functional data, with application to glaucoma data (2019)
  20. Marino, Maria Francesca; Ranalli, Maria Giovanna; Salvati, Nicola; Alfò, Marco: Semiparametric empirical best prediction for small area estimation of unemployment indicators (2019)

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