Optimization & Numerical Methods

Course outline

Numerical problems are frequently encountered by statisticians. Prominently, the estimation of the parameters of a statistical model requires the solution of an optimization problem. In a few simple cases, closed-form solutions exist but for many probability models the optimal parameter estimates have to be determined by means of an iterative algorithm. The goal of this course is threefold. First, we want to offer the readers an overview of some frequently used optimization algorithms in (applied) statistics. Second, we want to provide a framework for understanding the connections among several optimization algorithms as well as between optimization and aspects of statistical inference. Third, although very common, optimization is not the only numerical problem and therefore some important related topics such as numerical differentiation and integration will be covered.

Target audience

The intended target audience includes PhD students and researchers in a variety of fields, including biostatistics, psychometrics, educational measurement, public health, sociology. We aim at readers who apply and possibly develop statistical models and who wish to learn more about the basic concepts of numerical techniques, with an emphasis on optimization problems, and their use in statistics.

Course format

The course is organized in a dual format. First, web lectures need to be followed prior to the contact session. Second, a two-day contact session is organized in which we will have Q&A sessions and exercises to further deepen knowledge.


Participants should have a basic knowledge of the principles of statistical inference. This includes some familiarity with the concept of a likelihood function and likelihood-based inference for linear, binomial, multinomial, and logistic regression models. Readers should also have a basic understanding of matrix algebra. A working knowledge of the basic elements of univariate calculus is also a prerequisite, including (the concepts of continuity of a function, derivative and integration).

Course schedule

Session Date Time Location
1 November 18, 2015 10:30-12:00 TBA
2 November 18, 2015 13:30-15:00 TBA
3 November 18, 2015 15:30-17:00 TBA
4 November 19, 2015 9:00-10:30 TBA
5 November 19, 2015 11:00-12:30 TBA
6 November 19, 2015 14:00-15:30 TBA



Francis Tuerlinckx is Professor of Psychology at the KU Leuven in Belgium. He received the Master degree in psychology (1996) and a Ph.D. in psychology (2000) from the KU Leuven. He was a postdoc at the Department of Statistics of Columbia University (New York). In general, Francis Tuerlinckx’ research deals with the mathematical modeling of various aspects of human behavior. More specifically, he works on item response theory, reaction time modeling, and dynamical systems data analysis for human emotions. He is an associate editor of the Journal of Mathematical Psychology.
Geert Molenberghs is Professor of Biostatistics at the Universiteit Hasselt and KU Leuven in Belgium. He received the B.S. degree in mathematics (1988) and a Ph.D. in biostatistics (1993) from the Universiteit Antwerpen. Dr Molenberghs published methodological work on surrogate markers in clinical trials, categorical data, longitudinal data analysis, and on the analysis of non-response in clinical and epidemiological studies. He served as Joint Editor for Applied Statistics (2001-2004), Co-editor for Biometrics (2007–2009) and as President of the International Biometric Society (2004-2005). He currently is Co-editor for Biostatistics (2010–2012). He was elected Fellow of the American Statistical Association and received the Guy Medal in Bronze from the Royal Statistical Society. He has held visiting positions at the Harvard School of Public Health (Boston, MA). He is founding director of the Center for Statistics at Hasselt University and currently the director of the Interuniversity Institute for Biostatistics and statistical Bioinformatics, I-BioStat, a joint initiative of the Hasselt and Leuven universities.

Katrijn Van Deun is Assistant Professor in Methodology and Statistics at Tilburg University. She obtained a Master in psychology, a Master of Science in statistics and a PhD in psychology. Her main area of expertise is in statistical learning techniques for high-dimensional and/or integrative data including (regularized) scaling, clustering and component analysis techniques with applications to  so-called omics data. She has various publications in both methodological and substantive journals in bioinformatics. Katrijn is secretary of the Dutch/Flemish Classification society.

Tom F. Wilderjans is Assistant Professor of Psychology at Leiden University. He obtained a Masters degree (2005) and a PhD (2009) in Mathematical Psychology from the KU Leuven. Tom’s research deals with multivariate data analysis (component analysis, clustering, and combinations thereof) and model selection. He published his work in various methodological journals from the field of social sciences. Tom is a Board Member of the Dutch/Flemish Classification society.