Ulf Henning Olsson
Biographic Data
| ID | 4270689 |
|---|---|
| NAME | Ulf Henning Olsson |
| GIVEN NAMES | Ulf Henning |
| FAMILY NAME | Olsson |
| SIGNATURE | OLSSON U H |
| AFFILIATIONS | BI Norwegian Business School |
| VERIFIED | No |
| TOTAL WORKS | 6 |
| TOTAL CITATIONS | 1 |
| WORKS AS AUTHOR | 6 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 1999 |
| LATEST PUBLICATION YEAR | 2022 |
| H-INDEX | 1 |
Specification Search in Structural Equation Modeling (SEM)
Although structural equation model (SEM) is a powerful and widely applied tool particularly in social sciences, few studies have explored how SEM and statistical learning methods can be combined. The purpose of this paper is to explore how gradient component-wise boosting (GCB) can contribute to item selection. We ran 200 regressions with different farmer psychological variables collected to explain variation in an animal welfare indicator (AWI).…
A Faster Procedure for Estimating CFA Models Applying Minimum Distance Estimators with a Fixed Weight Matrix
This paper presents a numerically more efficient implementation of the quadratic form minimum distance (MD) estimator with a fixed weight matrix for confirmatory factor analysis (CFA) models. In structural equation modeling (SEM) computer software, such as EQS, lavaan, LISREL and Mplus, various MD estimators are available to the user. Standard procedures for implementing MD estimators involve a one-step approach applying non-linear optimization t…
A Simple Simulation Technique for Nonnormal Data with Prespecified Skewness, Kurtosis, and Covariance Matrix
We present and investigate a simple way to generate nonnormal data using linear combinations of independent generator (IG) variables. The simulated data have prespecified univariate skewness and kurtosis and a given covariance matrix. In contrast to the widely used Vale-Maurelli (VM) transform, the obtained data are shown to have a non-Gaussian copula. We analytically obtain asymptotic robustness conditions for the IG distribution. We show empiri…
Two Equivalent Discrepancy Functions for Maximum Likelihood Estimation
Over the years several discrepancy functions have been introduced both in the literature and in the software of Structural Equation Modeling (SEM). The test statistics for the discrepancy functions associated with Maximum Likelihood (ML), Generalized Least Squares (GLS), and Normal Theory Weighted Least Squares (NWLS) are all asymptotically equivalent. These test statistics are all approximately distributed as central chi-square under correct mod…
The Performance of ML, GLS, and WLS Estimation in Structural Equation Modeling Under Conditions of Misspecification and Nonnormality
This simulation study demonstrates how the choice of estimation method affects indexes of fit and parameter bias for different sample sizes when nested models vary in terms of specification error and the data demonstrate different levels of kurtosis. Using a fully crossed design, data were generated for 11 conditions of peakedness, 3 conditions of misspecification, and 5 different sample sizes. Three estimation methods (maximum likelihood [ML], g…
Theoretic Fit and Empirical Fit
In research the goal is often to construct models that reflect the structures and parameters of some unobservable causal mechanism. The degree of isomorphis between such a theoretic model and a "true" model can be labeled "theoretic fit." In the absence of direct evidence that the researcher's theoretic model accurately reflects the true model, indices of "empirical fit" (Chi-Square, etc.) are used as indirect evidence of versimilitude. The issue…
Two Equivalent Discrepancy Functions for Maximum Likelihood Estimation
Over the years several discrepancy functions have been introduced both in the literature and in the software of Structural Equation Modeling (SEM). The test statistics for the discrepancy functions associated with Maximum Likelihood (ML), Generalized Least Squares (GLS), and Normal Theory Weighted Least Squares (NWLS) are all asymptotically equivalent. These test statistics are all approximately distributed as central chi-square under correct mod…
Theoretic Fit and Empirical Fit
In research the goal is often to construct models that reflect the structures and parameters of some unobservable causal mechanism. The degree of isomorphis between such a theoretic model and a "true" model can be labeled "theoretic fit." In the absence of direct evidence that the researcher's theoretic model accurately reflects the true model, indices of "empirical fit" (Chi-Square, etc.) are used as indirect evidence of versimilitude. The issue…
The Performance of ML, GLS, and WLS Estimation in Structural Equation Modeling Under Conditions of Misspecification and Nonnormality
This simulation study demonstrates how the choice of estimation method affects indexes of fit and parameter bias for different sample sizes when nested models vary in terms of specification error and the data demonstrate different levels of kurtosis. Using a fully crossed design, data were generated for 11 conditions of peakedness, 3 conditions of misspecification, and 5 different sample sizes. Three estimation methods (maximum likelihood [ML], g…
Two Equivalent Discrepancy Functions for Maximum Likelihood Estimation
Over the years several discrepancy functions have been introduced both in the literature and in the software of Structural Equation Modeling (SEM). The test statistics for the discrepancy functions associated with Maximum Likelihood (ML), Generalized Least Squares (GLS), and Normal Theory Weighted Least Squares (NWLS) are all asymptotically equivalent. These test statistics are all approximately distributed as central chi-square under correct mod…
A Simple Simulation Technique for Nonnormal Data with Prespecified Skewness, Kurtosis, and Covariance Matrix
We present and investigate a simple way to generate nonnormal data using linear combinations of independent generator (IG) variables. The simulated data have prespecified univariate skewness and kurtosis and a given covariance matrix. In contrast to the widely used Vale-Maurelli (VM) transform, the obtained data are shown to have a non-Gaussian copula. We analytically obtain asymptotic robustness conditions for the IG distribution. We show empiri…
A Faster Procedure for Estimating CFA Models Applying Minimum Distance Estimators with a Fixed Weight Matrix
This paper presents a numerically more efficient implementation of the quadratic form minimum distance (MD) estimator with a fixed weight matrix for confirmatory factor analysis (CFA) models. In structural equation modeling (SEM) computer software, such as EQS, lavaan, LISREL and Mplus, various MD estimators are available to the user. Standard procedures for implementing MD estimators involve a one-step approach applying non-linear optimization t…
Specification Search in Structural Equation Modeling (SEM)
Although structural equation model (SEM) is a powerful and widely applied tool particularly in social sciences, few studies have explored how SEM and statistical learning methods can be combined. The purpose of this paper is to explore how gradient component-wise boosting (GCB) can contribute to item selection. We ran 200 regressions with different farmer psychological variables collected to explain variation in an animal welfare indicator (AWI).…
Mathematics (6 works) · Statistics (6 works) · Econometrics (5 works) · Applied Mathematics (4 works) · Estimator (4 works) · Advanced Statistical Modeling Techniques (3 works) · Applied Mathematics (3 works) · Computer Science (3 works) · Statistical Methods and Applications (3 works) · Structural equation modeling (3 works)