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Section for Statistics and Data Analysis

The Statistics and Data Analysis section has special emphasis on statistics, quantitative genomics, bioinformatics, pattern recognition and software development. The section is dedicated to supporting other departments at DTU and its external partners with skills, knowledge, and consultancy within the field of statistics and data analysis - including biological data.

Statistics and Data Analysis — Statistics and Data Analysis section October 2023

DTU Compute has a long history of statistical consultancy and research collaborations with various DTU departments, other universities, and external partners. The section formalizes these collaborations to enhance research and consultancy services at DTU. With strong ties to research groups in cognitive systems, image analysis, and scientific computing, the section is a key player in both the Danish and international statistics arenas.

The section aims to strengthen connections to statistics, bioinformatics, and computational biology activities across DTU and promote high-quality statistics in Public Sector Consultancy. DTU Compute also provides internal and external statistical consultancy services.

Head of Section

Bjarne Kjær Ersbøll

Bjarne Kjær Ersbøll Phone: +45 45253413 bker@dtu.dk

Research areas

Applied Probability, Stochastic Processes

The research in this field is focused on systems with discrete state space like queueing and inventory systems. More technically we specialize in the field of matrix analytic methods which was popularized by Marcel Neuts in the 1970's and onwards. Current applications have been within call center modeling and modeling of marine systems. Most recently we are interested in parameter estimation and statistical testing in multivariate matrix-exponential distributions. These methods can be applied in design of communications systems, to determine software reliability, in call center modelling and design and in statistical modelling etc.

Lead researcher

Bo Friis Nielsen Professor, Head of Section

Modern Statistical Models

Modern statistical models are used in statistical learning and statistical engineering to analyse the increased amounts of data collected everywhere in our society on a daily basis. The methods include random forests, regularisation strategies, sparse methods, support vector machines, boot strapping, deep belief networks, and any more. The field is very active with a huge number of journal papers published. The research can be applied through automation strategies in industrial productions, in educational measurements, in bioinformatics, gene studies and in management an business intelligence.

Lead researcher

Line Katrine Harder Clemmensen Professor

Statistical Design and Analysis of Experiments

We deal with the planning of experiments where variation is present. Often someone planning to do an experiment is interested in the effect of some process or intervention on some experimental unit. Given experimental conditions, the main challenge is to formulate experimental plans which will provide informative data suitable for statistical analysis. Design of experiments is a discipline that has very broad application across all the natural and social sciences. Our research areas include design of computer experiments, design of animal experiments and the design of split plot experiments.

Lead researcher

Murat Külahci Professor

Statistical Process Control

Statistical process control (SPC) is the general methodology based on statistical methods that can be used in monitoring and control of product and/or process quality. We often expect the processes and products to exhibit variability. This inherent variability in a process is defined as the variability that occurs by chance. If a process exhibits such variation, it is said to be in a state of statistical control. However, when there is an unusual and/or unexpected occurrence of excess variability due to an assignable cause, the process is said to be out of statistical control. This could for example be due to wear and tear, defective/impure raw materials, errors in data collection schemes, etc. Statistical process control aims to identify these assignable causes generating the excessive variation and help to bring the process back in statistical control.

Lead researcher

Murat Külahci Professor

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