Leiden University Student Repository

Lieshout, Kenny van (2021)

Comparing Singular Value Regularization Methods in The MELODIC Family for Simultaneous Binary Logistic Regression in a Reduced Space

Master thesis | Psychology (MSc)

As the availability of data becomes more widespread and computational technology develops, the need to model several outcome variables at once increases. To do this for multiple dichotomous outcome variables, De Rooij and Groenen (2021) proposed the MELODIC family for simultaneous binary logistic regression in a reduces space. As an added feature, 4 different forms of regularization on the singular values were proposed to let the algorithm itself select the true dimensionality of the data set. In this paper a simulation study was performed to provide empirical evidence for the functionality of the regularization features on data sets with differing numbers of subjects, predictor variables, and outcome variables. The results show that the hard-thresholding regularization consistently estimates the correct dimensionality with regularization on the logarithm of the singular values slightly outperforming the regularization on the unaltered singular values.

MELODIC

Multivariate Logistic Regression

Author

Lieshout, Kenny van

Faculty

Faculty of Social & Behavioural Sciences

Specialisation

Methodology and Statistics in Psychology

Supervisors

Rooij, M. de
Busing, F.

ECTS Credits

20

Language

en