The fields of machine learning and statistics have invested great efforts into designing algorithms, models, and approaches that better predict future observations. Larger and richer data have also been shown to improve predictive power. This is especially true in the world of human behavioral big data, as is evident from recent advances in behavioral prediction technology. Large internet...
It is rightly pointed out that in the midst of a pandemic crisis of enormous proportions we needed high-quality statistics with extreme urgency, but that instead we are in danger of drowning in an ocean of data and information. Rarely has the lack of adequate statistics to make essential political decisions and to win popular support for their consequences been as visible and painful as it is...
In this session, you will explore and discuss two fresh open problems. Two volunteers will briefly present an open case they are working on, you'll ask questions and give advice, and Christian will facilitate the meeting to make sure that everybody contributes. It's a session type we experimented with at a few ENBIS conferences which gives participants an opportunity to enter much more deeply...
The ease of data collection in the industry is a great opportunity to do business working to reduce costs of inefficiencies. However, having the opportunity to collect data does not imply achieving value with its treatment. There are numerous weak elements in the culture of organizations related to the ability of people to exploit the value of data. Currently the habit of looking at data as...
Although Principal Component Analysis (PCA) and Partial Least Squares regression (PLS) are currently recognised as some of the most powerful approaches for the analysis and interpretation of multivariate data especially in the field of industrial processes, strong non-linear relationships among objects and/or variables may represent a difficult issue to solve when one tries to model them by...
What do decision makers want from statisticians? What do I want from Stina or David, when I ask them for an analysis? We will have a glimpse into the dreams and headaches of managers, look at a few examples of statistical analyses from the manager’s side, and put them into a context of decision theory.
Are you interested in case studies and real-world problems for active learning of statistics? Then come and join us in this interactive session organised by the SIG Statistics in Practice. A famous project for students to apply the acquired knowledge of design of experiments is Box's paper helicopter. Although being quite simple and cheap to build, it covers various aspects of DoE. Beyond...
A short presentation on Wiley’s Statistics journals programme. The session will also cover practical ways for authors to maximise the impact of their articles.
Warranty return data from repairable systems, such as home appliances, lawn mowers, computers, and automobiles, result in recurrent event data. The non-homogeneous Poisson process (NHPP) model is used widely to describe such data. Seasonality in the repair frequencies and other variabilities, however, complicate the modeling of recurrent event data. Not much work has been done to address the...
Industrial statisticians played an important role in the success of the Industrial Revolution. The analytical methods developed in our field have been used to leverage data from machines and workers to improve processes, safety, and products for nearly 100 years. We are now in the midst of the Information Age, and the data revolution brings the challenges and opportunities of our time. Our...
Inspections according to statistical sampling plans allow conclusions to be drawn about the reliability of a whole population of e.g. measurement devices. However, confirming high reliability levels requires large sample sizes and is thus expensive or even infeasible.
When reliability is judged by not exceeding a certain threshold, considerably more efficient attribute sampling plans can be...
You have a business or research question, you’ve collected or found appropriate data, and you are ready to analyze. But which analytical methods should you try? And how will you choose a final – hopefully the most useful – model? In this seminar, we will look at several data scenarios and discuss modeling options and a framework for comparison. We will look at how different questions or goals...