Marlene Müller

Professor Marlene Müller serves as Professor of Applied Statistics at Beuth University of Applied Sciences Berlin within the Department of Mathematics, Physics, and Chemistry in the School of Engineering. Her academic career spans over two decades with significant contributions to nonparametric and semiparametric statistical methods, particularly applied to financial data analysis and credit scoring.

Her research interests center on Nonparametric Statistics, Semiparametric Models, and Statistical Computing, with a strong emphasis on practical applications in finance. She has developed numerous R implementations for statistical methods, making complex techniques accessible to practitioners. Her work bridges theoretical statistics with real-world financial applications, particularly in credit risk modeling and portfolio analysis.

Professor Müller's publication record shows a consistent focus on applying advanced statistical techniques to financial problems. Her work evolved from foundational theoretical contributions in the 1990s to more applied financial modeling in the 2000s, with a particular emphasis on credit scoring systems under regulatory frameworks like Basel II. She has maintained a strong focus on developing computational tools, especially through the R programming language, to implement these statistical methods.

She is co-author of the influential textbook Nonparametric and Semiparametric Models (Springer Series in Statistics, 2004) with Härdle, Sperlich, and Werwatz, which has become a standard reference in the field. Her teaching portfolio includes advanced statistics courses with a strong computational component, and she has developed extensive educational materials for teaching nonparametric methods.

Professor Müller maintains an active consultation schedule, with office hours listed through the summer semester of 2025, indicating her ongoing engagement with students and academic work. Her contributions to statistical computing through R package development and teaching materials demonstrate her commitment to advancing both theoretical and applied statistics education.