Hosted by the Interactive AI Centre for Doctoral Training and the Intelligent Systems Lab

Contact iai-cdt@bristol.ac.uk if you would like lunch, served between 13.30-14.00 

Two talks:

Dr. Luiz Medeiros de Araújo Lima Filho, Associate Professor at the Department of Statistics, Federal University of Paraíba, Brazil. He is currently a Visiting Researcher here at University of Bristol.

Title: Control Charts: essential tools for quality and process efficiency

Abstract: Control charts are the main statistical process control techniques being the most used tools to monitor a particular quality characteristic. This technique has been used in various fields of knowledge, such as: industry, financial indicators, infection control, energy consumption, inventory control, among others. This presentation will provide a comprehensive overview of this technique, application and development, highlighting the importance of choosing the appropriate control chart for the type of variable being analyzed. The approach includes variables in the real, positive variables, variables in the standard unit interval, circular data.  

This will be followed by a talk from Dr. Tarciana Liberal Pereira de Araujo is an Associate Professor at the Department of Statistics, Federal University of Paraíba, Brazil. She is currently a Visiting Researcher here at University of Bristol.

Title: Enhancing Data Analysis with Inflated Continuous Distributions

Abstract: In order to provide greater flexibility in data modelling, we will address in this talk the use of statistical methodologies based on inflated continuous distributions. Inflated distributions are especially useful for dealing with data that exhibit excess zeros and/or ones, which are common in various research fields. We will cover the concept and application of some inflated continuous distributions and discuss various statistical methodologies based on these distributions, such as regression models, time series models, and control charts. The use of inflated distributions demonstrates how these techniques can provide more robust and accurate solutions, overcoming the limitations of traditional continuous distributions and better capturing the structure of the data.