Lötsch, J., & Ultsch, A. (o. J.). Pitfalls of using multinomial regression analysis to identify class-structure relevant variables in biomedical datasets: why a mixture of experts (MOE) approach is better (Preprint) [Cd]. Frankfurt am Main: Universitätsbibliothek Johann Christian Senckenberg. https://doi.org/10.20944/preprints202308.1191.v1
ISO-690 (author-date, English)LÖTSCH, Jörn und ULTSCH, Alfred, [no date]. Pitfalls of using multinomial regression analysis to identify class-structure relevant variables in biomedical datasets: why a mixture of experts (MOE) approach is better. Preprint. Frankfurt am Main: Universitätsbibliothek Johann Christian Senckenberg.
Modern Language Association 9th editionLötsch, J., und A. Ultsch. Pitfalls of using multinomial regression analysis to identify class-structure relevant variables in biomedical datasets: why a mixture of experts (MOE) approach is better. Preprint, cd, Universitätsbibliothek Johann Christian Senckenberg, https://doi.org/10.20944/preprints202308.1191.v1.
Mohr Siebeck - Recht (Deutsch - Österreich)Lötsch, Jörn/Ultsch, Alfred: Pitfalls of using multinomial regression analysis to identify class-structure relevant variables in biomedical datasets: why a mixture of experts (MOE) approach is better, Preprint. Aufl. Frankfurt am Main
Emerald - HarvardLötsch, J. und Ultsch, A. (o. J.). Pitfalls of using multinomial regression analysis to identify class-structure relevant variables in biomedical datasets: why a mixture of experts (MOE) approach is better, Preprint., Bd. , Universitätsbibliothek Johann Christian Senckenberg, Frankfurt am Main, verfügbar unter:https://doi.org/10.20944/preprints202308.1191.v1.