A Monte Carlo evaluation of three methods to detect local dependence in binary data latent class models. - Daniel L. Oberski; Geert H. van Kollenburg; Jeroen K. Vermunt (Adv. Data Anal. Classif., 2013)

Understanding chemical production processes by using PLS path model parameters as soft sensors. - Geert H. van Kollenburg; Jacoline van Es; Jan Gerretzen; Heleen Lanters; Roel Bouman; Willem Koelewijn; Anthony N. Davies; Lutgarde M. C. Buydens; Henk-Jan van Manen; Jeroen J. Jansen (Comput. Chem. Eng., 2020)

Predictive discarding of wafers based on power leakage predictions from single layer misalignment data. - Geert H. van Kollenburg; Mike Holenderski; Patrizia Vasquez; Nirvana Meratnia (ISM, 2021)

Process PLS: Incorporating substantive knowledge into the predictive modelling of multiblock, multistep, multidimensional and multicollinear process data. - Geert H. van Kollenburg; Roel Bouman; Tim Offermans; Jan Gerretzen; Lutgarde M. C. Buydens; Henk-Jan van Manen; Jeroen J. Jansen (Comput. Chem. Eng., 2021)

Automatically optimizing dynamic synchronization of individual industrial process variables for statistical modelling. - Tim Offermans; Ewa Szymanska; Geert H. van Kollenburg; Lutgarde M. C. Buydens; Jeroen J. Jansen (Comput. Chem. Eng., 2021)

Modeling Multivariate Relations in Multiblock Semiconductor Manufacturing Data Using Process PLS To Enhance Process Understanding. - Geert H. van Kollenburg; Richard Verhoeven; Mike Holenderski; Nirvana Meratnia; Daniele Pagano (WSC, 2023)

Evaluating How Explainable AI Is Perceived in the Medical Domain: A Human-Centered Quantitative Study of XAI in Chest X-Ray Diagnostics. - Gizem Nur Karagoz; Geert H. van Kollenburg; Tanir Ozcelebi; Nirvana Meratnia (TAI4H, 2024)