The copula information criteria S Grønneberg, NL Hjort Scandinavian Journal of Statistics 41 (2), 436-459, 2014 | 134* | 2014 |

Estimation and inference in univariate and multivariate log-GARCH-X models when the conditional density is unknown G Sucarrat, S Grønneberg, A Escribano Computational statistics & data analysis 100, 582-594, 2016 | 57 | 2016 |

How general is the Vale–Maurelli simulation approach? N Foldnes, S Grønneberg Psychometrika 80, 1066-1083, 2015 | 40 | 2015 |

Assessing model fit in structural equation modeling using appropriate test statistics KM Marcoulides, N Foldnes, S Grønneberg Structural Equation Modeling: A Multidisciplinary Journal 27 (3), 369-379, 2020 | 38 | 2020 |

The sensitivity of structural equation modeling with ordinal data to underlying non-normality and observed distributional forms. N Foldnes, S Grønneberg Psychological Methods 27 (4), 541, 2022 | 35 | 2022 |

Pernicious polychorics: The impact and detection of underlying non-normality N Foldnes, S Grønneberg Structural Equation Modeling: A Multidisciplinary Journal 27 (4), 525-543, 2020 | 35 | 2020 |

Risk estimation with a time-varying probability of zero returns G Sucarrat, S Grønneberg Journal of Financial Econometrics 20 (2), 278-309, 2022 | 27* | 2022 |

Covariance model simulation using regular vines S Grønneberg, N Foldnes Psychometrika 82, 1035-1051, 2017 | 26 | 2017 |

Statistikk og dataanalyse: En moderne innføring N Foldnes, S Grønneberg, GH Hermansen Cappelen Damm akademisk, 2018 | 21* | 2018 |

On identification and non-normal simulation in ordinal covariance and item response models N Foldnes, S Grønneberg Psychometrika 84 (4), 1000-1017, 2019 | 18 | 2019 |

A problem with discretizing Vale–Maurelli in simulation studies S Grønneberg, N Foldnes Psychometrika 84 (2), 554-561, 2019 | 16 | 2019 |

covsim: An R package for simulating non-normal data for structural equation models using copulas S Grønneberg, N Foldnes, KM Marcoulides Journal of Statistical Software 102, 1-45, 2022 | 15 | 2022 |

Factor analyzing ordinal items requires substantive knowledge of response marginals. S Grønneberg, N Foldnes Psychological Methods 29 (1), 65, 2024 | 14 | 2024 |

Approximating test statistics using eigenvalue block averaging N Foldnes, S Grønneberg Structural Equation Modeling: A Multidisciplinary Journal 25 (1), 101-114, 2018 | 14 | 2018 |

Testing model fit by bootstrap selection S Grønneberg, N Foldnes Structural Equation Modeling: A Multidisciplinary Journal 26 (2), 182-190, 2019 | 13 | 2019 |

The asymptotic covariance matrix and its use in simulation studies N Foldnes, S Grønneberg Structural Equation Modeling: A Multidisciplinary Journal 24 (6), 881-896, 2017 | 13 | 2017 |

The copula information criterion and its implications for the maximum pseudo-likelihood estimator S Grønneberg Dependence Modeling: Vine Copula Handbook, 113-138, 2010 | 12 | 2010 |

Non-normal data simulation using piecewise linear transforms N Foldnes, S Grønneberg Structural Equation Modeling: A Multidisciplinary Journal 29 (1), 36-46, 2022 | 8 | 2022 |

Partial identification of latent correlations with binary data S Grønneberg, J Moss, N Foldnes Psychometrika 85 (4), 1028-1051, 2020 | 7 | 2020 |

On partial-sum processes of ARMAX residuals S Grønneberg, B Holcblat The Annals of Statistics 47 (6), 3216-3243, 2019 | 7 | 2019 |