David Kreiberg

Söderström, Torsten; Kreiberg, David & Mossberg, Magnus (2014)

Extended accuracy analysis of a covariance matching approach for identifying errors-in-variables systems

Automatica, 50(10), s. 2597- 2605. Doi: 10.1016/j.automatica.2014.08.020

Kreiberg, David; Söderström, Torsten & Wallentin, Fan Yang (2013)

Errors-in-variables identification using covariance matching and structural equation modeling

IEEE Conference on Decision and Control. Proceedings Doi: 10.1109/cdc.2013.6760812

Kreiberg, David (2023)

A confirmatory factor analysis approach for addressing the errors-in-variables problem with colored output noise

Automatica, 156 Doi: 10.1016/j.automatica.2023.111187 - Full text in research archive

Over the years, errors-in-variables (EIV) system identification has attracted considerable research interest. Among the many proposed approaches for identifying EIV models is confirmatory factor analysis (CFA), here referred to as EIV-CFA. This study extends previous research by presenting a EIV-CFA modeling framework that allows for colored output noise. Considerable attention is paid to the theoretical aspects of the minimum distance (MD) estimator. The finite sample performance of the MD estimator is briefly evaluated using simulation. The results suggest that model parameters are well estimated.

Kreiberg, David & Zhou, Xingwu (2022)

A Faster Procedure for Estimating SEMs Applying Minimum Distance Estimators With a Fixed Weight Matrix

Structural Equation Modeling Doi: 10.1080/10705511.2022.2076093 - Full text in research archive

This study presents a separable nonlinear least squares (SNLLS) implementation of the minimum distance (MD) estimator employing a fixed-weight matrix for estimating structural equation models (SEMs). In contrast to the standard implementation of the MD estimator, in which the complete set of parameters is estimated using nonlinear optimization, the SNLLS implementation allows a subset of parameters to be estimated using (linear) least squares (LS). The SNLLS implementation possesses a number of benefits, such as faster convergence, better performance in ill-conditioned estimation problems, and fewer required starting values. The present work demonstrates that SNLLS, when applied to SEM estimation problems, significantly reduces the estimation time. Reduced estimation time makes SNLLS particularly useful in applications involving some form of resampling, such as simulation and bootstrapping.

Kreiberg, David; Marcoulides, Katerina & Olsson, Ulf H. (2020)

A faster procedure for estimating CFA models applying Minimum Distance Estimators with a fixed weight matrix

Structural Equation Modeling Doi: 10.1080/10705511.2020.1835484 - Full text in research archive

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 techniques. Our implementation differs from the standard approach by utilizing a two-step estimation procedure. In the first step, only a subset of the parameters are estimated using non-linear optimization. In the second step, the remaining parameters are obtained using numerically efficient linear least squares (LLS) methods. Through examples, it is demonstrated that the proposed implementation of MD estimators may be considerably faster than what the standard implementation offer. The proposed procedure will be of particular interest in computationally intensive applications such as simulation, bootstrapping, and other procedures involving re-sampling.

Kreiberg, David; Söderström, Torsten & Wallentin, Fan Yang (2016)

Errors-in-variables system identification using structural equation modeling

Automatica, 66, s. 218- 230. Doi: 10.1016/j.automatica.2015.12.007

Forskningsrådet, NFR & Kreiberg, David (2015)

Kronglete vei fra kunnskap til handlekurv

[Internett]

Tveit, Anders & Kreiberg, David (2019)

Why can’t we just have fun? Role-Play or Case-Based Learning. What is the difference when it comes to learning?

[Academic lecture]. FIBE 2019.

Kreiberg, David (2019)

Why can't we just have fun. Role-Play or Case-Based Learning. What is the difference when it comes to learning?

[Academic lecture]. FIBE 2019.

Kreiberg, David; Söderström, Torsten & Wallentin, Fan Yang (2013)

Errors-in-variables identification using covariance matching and structural equation modeling

[Academic lecture]. 2013 IEEE 52nd Annual Conference on Decision and Control (CDC).