ENBIS-16 in Sheffield11 – 15 September 2016; Sheffield Abstract submission: 20 March – 4 July 2016
The following abstracts have been accepted for this event:
RosettaHUB, Towards a Universal Platform for Open Statistical Computing in the Cloud
Authors: Karim Chine (RosettaHUB)
Primary area of focus / application: Mining
Secondary area of focus / application: Education & Thinking
Keywords: R, Cloud computing, Education, Collaboration, Reproducible statistical computing, Dashboards, SaaS for statistics
Submitted at 31-May-2016 23:59 by Karim Chine
R, Python, Julia, SQL, Scala, Spark, ParaView, etc. can be used simultaneously and collaboratively within an interactive Google-docs like environment. The platform acts as a broker and a marketplace for statistical applications, it makes it possible to create and share resources on any infrastructure including Amazon EC2, GCE, Azure and private clouds. RosettaHUB is fully programmable. Its statistical and numerical cloud-based capabilities are accessible from Excel and from all the mainstream environments used by statisticians on their Personal Computers including the R and Python command lines.
Web site: www.rosettahub.com
Statistical Methods in Emotional Product Design Following the Kansei Engineering Model
Authors: Lluis Marco-Almagro (Universitat Politecnica de Catalunya, BarcelonaTech), Xavier Tort-Martorell (Universitat Politecnica de Catalunya, BarcelonaTech)
Primary area of focus / application: Other: Statistical methods in emotional design
Keywords: Emotional design, Kansei engineering, Multivariate methods, Quantification theory type I, Regression analysis
Submitted at 3-Jun-2016 18:33 by Lluis Marco-Almagro
Some markets are currently so crowded of similar products in terms of functionality that adding an “emotional touch” can make a difference. How do designers create “emotional products”? They usually rely on their intuition, creativity and experience. But they also use different qualitative and quantitative methods to collect information on how products are perceived and used. One of this methods is the so-called kansei engineering (KE).
Kansei engineering is a method for incorporating emotions in the product development phase. The main purpose is discovering which technical parameters of a product elicit the chosen emotions. The method was first proposed by Prof. Mitsuo Nagamachi in the 70’s and 80’s, but gained attention in this XXI century, in part due to work by Prof. Simon Schütte at Linköpings Universitet. KE studies are based on self-reporting emotional reactions with questionnaires (usually ratings on Likert or semantic differential scales). A set of different prototypes is shown to participants in the study, and ratings are given on elicited emotions. Each emotion acts as a response in a design of experiments.
There is a large range of statistical tools commonly used in KE studies, mainly multivariate techniques and regression models. Data in KE studies have a great amount of variability, and as building prototypes is costly, there is always the attempt to discover a lot of things (having a lot of factors) but only a few runs in the experiment (probably too few!). All these issues pose interesting statistical challenges; in fact, kansei engineering is a discipline “in need of statistics”.
This seminar will take the form of a workshop, where you will be asked to discuss and work with your colleagues. We will first cover the basic ideas behind kansei engineering studies, and present the model used to conduct them. After several examples, a real (simple) KE study will be prepared by participants in small groups. This small example will be used to discuss some statistical tools useful in KE. For instance, multivariate techniques for summarizing information, and for automatically detecting “crazy” participants, will be covered. As “customers” of KE studies are often designers, a great importance is placed on visual presentation of results. Quantification theory type I (QT1), a special version of regression analysis commonly used in KE, which makes interpretation of results easier when all independent variables are categorical, will also be explained (the ideas behind QT1 are, in fact, useful far beyond KE studies).
When the seminar finishes, you will have a good understanding of what kansei engineering is, and how statistics can contribute to this field. Moreover, you will have learnt some “tricks”, such as QT1, that make statistical output easier to interpret to a broad audience.
A Locally Asymptotically Optimal Test for ARCH Models
Authors: Joseph Ngatchou-Wandji (EHESP Rennes & Université de Lorraine), Tewfik Lounis (Université de Lorraine)
Primary area of focus / application: Finance
Secondary area of focus / application: Economics
Keywords: Optimal tests, ARCH models, Power of tests, Contiguity
Submitted at 4-Jun-2016 09:41 by Joseph Ngatchou-Wandji
Accepted (view paper)
The Use of the Algebraic Method in the Design of Experiments
Authors: Henry Wynn (London School of Economics)
Primary area of focus / application: Design and analysis of experiments
Secondary area of focus / application: Design and analysis of experiments
Keywords: Grobner bases, Aliasing, Factorial design, Quadrature, Intervention
Submitted at 4-Jun-2016 11:48 by Henry Wynn
showing what the limits of identifiability are for all polynomial models.
A new application is via a type of duality which explains the use
of certain type of design in quadrature. There are many open problems,
foremost of which is to link the algebraic methods to optimal design, and their use in complex areas such as intervention in causal models defined by directed acyclic graphs (DAGs).
A Bayesian Short-Term Strategy for Site-Specific Wind Parameter Estimation
Authors: Pasquale Erto (University of Naples Federico II), Antonio Lepore (University of Naples Federico II), Biagio Palumbo (University of Naples Federico II)
Primary area of focus / application: Modelling
Keywords: Markov chain Monte Carlo, Bayesian estimators, Wind speed distribution, Weibull distribution
Submitted at 6-Jun-2016 13:04 by Antonio Lepore
Case Studies on PLS-Based Procedure for Ship Fuel Consumption Monitoring and Fault Detection
Authors: Andrea D'Ambra (Grimaldi Group), Antonio Lepore (University of Naples Federico II), Biagio Palumbo (University of Naples Federico II), Luigi Vitiello (University of Naples Federico II), Christian Capezza (University of Naples Federico II)
Primary area of focus / application: Modelling
Secondary area of focus / application: Process
Keywords: Partial Least-Squares regression, Fuel consumption monitoring, Fault detection, Hotelling control chart, Squared prediction error control chart, Vessel energy efficiency
Submitted at 6-Jun-2016 19:18 by Luigi Vitiello