Browsing BI Open by Author "Grønneberg, Steffen"
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A PROBLEM WITH DISCRETIZING VALE–MAURELLI IN SIMULATION STUDIES
Foldnes, Njål; Grønneberg, Steffen (Journal article; Peer reviewed, 2019)Previous influential simulation studies investigate the effect of underlying nonnormality in ordinal data using the Vale–Maurelli (VM) simulation method. We show that discretized data stemming from the VM method with a ... 
Approximating Test Statistics Using Eigenvalue Block Averaging
Foldnes, Njål; Grønneberg, Steffen (Journal article; Peer reviewed, 2018)We introduce and evaluate a new class of approximations to common test statistics in structural equation modeling. Such test statistics asymptotically follow the distribution of a weighted sum of i.i.d. chisquare variates, ... 
Covariance Model Simulation Using Regular Vines
Grønneberg, Steffen; Foldnes, Njål (Journal article; Peer reviewed, 2017)We propose a new and flexible simulation method for nonnormal data with userspecified marginal distributions, covariance matrix and certain bivariate dependencies. The VITA (VIne To Anything) method is based on regular ... 
covsim: An R Package for Simulating Nonnormal Data for Structural Equation Models Using Copulas
Grønneberg, Steffen; Foldnes, Njål; Marcoulides, Katerina (Journal article; Peer reviewed, 2022)In factor analysis and structural equation modeling nonnormal data simulation is traditionally performed by specifying univariate skewness and kurtosis together with the target covariance matrix. However, this leaves ... 
Factor analyzing ordinal items requires substantive knowledge of response marginals
Grønneberg, Steffen; Foldnes, Njål (Journal article; Peer reviewed, 2022)In the social sciences, measurement scales often consist of ordinal items and are commonly analyzed using factor analysis. Either data are treated as continuous, or a discretization framework is imposed in order to take ... 
How general is the ValeMaurelli simulation approach?
Foldnes, Njål; Grønneberg, Steffen (Journal article; Peer reviewed, 2015)The ValeMaurelli (VM) approach to generating nonnormal mul tivariate data involves the use of Fleishman polynomials applied to an underly ing Gaussian random vector. This method has been extensively used in Monte Carlo ... 
Nonnormal Data Simulation using Piecewise Linear Transforms
Foldnes, Njål; Grønneberg, Steffen (Peer reviewed; Journal article, 2021)We present PLSIM, a new method for generating nonnormal data with a prespecified covariance matrix that is based on coordinatewise piecewise linear transformations of standard normal variables. In our presentation, the ... 
On Identification and Nonnormal Simulation in Ordinal Covariance and Item Response Models
Foldnes, Njål; Grønneberg, Steffen (Journal article; Peer reviewed, 2019)A standard approach for handling ordinal data in covariance analysis such as structural equation modeling is to assume that the data were produced by discretizing a multivariate normal vector. Recently, concern has been ... 
On partialsum processes of ARMAX residuals
Grønneberg, Steffen; Holcblat, Benjamin (Journal article; Peer reviewed, 2018)We establish general and versatile results regarding the limit behavior of the partialsum process of ARMAX residuals. Illustrations include ARMA with seasonal dummies, misspecified ARMAX models with autocorrelated errors, ... 
On the errors committed by sequences of estimator functionals
Grønneberg, Steffen; Hjort, Nils Lid (Journal article; Peer reviewed, 2011)Consider a sequence of estimators ˆ n which converges almost surely to 0 as the sample size n tends to infinity. Under weak smoothness conditions, we identify the asymptotic limit of the last time ˆ n is further than " ... 
Partial identification of latent correlations with binary data
Grønneberg, Steffen; Moss, Jonas; Foldnes, Njål (Journal article; Peer reviewed, 2020)The tetrachoric correlation is a popular measure of association for binary data and estimates the correlation of an underlying normal latent vector. However, when the underlying vector is not normal, the tetrachoric ... 
Partial Identification of Latent Correlations with Ordinal Data
Moss, Jonas; Grønneberg, Steffen (Others, 2023)The polychoric correlation is a popular measure of association for ordinal data. It estimates a latent correlation, i.e., the correlation of a latent vector. This vector is assumed to be bivariate normal, an assumption ... 
Pernicious Polychorics: The Impact and Detection of Underlying Nonnormality
Foldnes, Njål; Grønneberg, Steffen (Journal article; Peer reviewed, 2019)Ordinal data in social science statistics are often modeled as discretizations of a multivariate normal vector. In contrast to the continuous case, where SEM estimation is also consistent under nonnormality, violation of ... 
Risk Estimation with a TimeVarying Probability of Zero Returns
Sucarrat, Genaro; Grønneberg, Steffen (Journal article; Peer reviewed, 2020)The probability of an observed financial return being equal to zero is not necessarily zero, or constant. In ordinary models of financial return, however, e.g. ARCH, SV, GAS and continuoustime models, the zeroprobability ... 
The sensitivity of structural equation modeling with ordinal data to underlying nonnormality and observed distributional forms
Foldnes, Njål; Grønneberg, Steffen (Journal article; Peer reviewed, 2020)Structural equation modeling (SEM) of ordinal data is often performed using normal theory maximum likelihood estimation based on the Pearson correlation (contML) or using least squares principles based on the polychoric ... 
Testing Model Fit by Bootstrap Selection
Grønneberg, Steffen; Foldnes, Njål (Journal article; Peer reviewed, 2018)Over the last few decades, many robust statistics have been proposed in order to assess the fit of structural equation models. To date, however, no clear recommendations have emerged as to which test statistic performs ... 
The asymptotic covariance matrix and its use in simulation studies
Foldnes, Njål; Grønneberg, Steffen (Journal article; Peer reviewed, 2017)The asymptotic performance of structural equation modeling tests and standard errors are influenced by two factors: the model and the asymptotic covariance matrix Γ of the sample covariances. Although most simulation studies ... 
The copula information criteria
Grønneberg, Steffen; Hjort, Nils Lid (Journal article; Peer reviewed, 2014)We derive two types of Akaike information criterion (AIC)like modelselection formulae for the semiparametric pseudomaximum likelihood procedure. We first adapt the arguments leading to the original AIC formula, related ...