ENBIS-17 in Naples9 – 14 September 2017; Naples (Italy) Abstract submission: 21 November 2016 – 10 May 2017
The following abstracts have been accepted for this event:
Construction of Two-Level Factorial and Fractional Factorial Designs with Runs in Blocks of Size Two
Authors: Janet Godolphin (University of Surrey, UK)
Primary area of focus / application: Design and analysis of experiments
Secondary area of focus / application: Education & Thinking
Keywords: Factorial effect, Isomorphic, Confounding, Replicate generator, Fraction generator
Submitted at 13-Mar-2017 12:13 by Janet Godolphin
For p factors, and M at least as large as a given function of p, a construction approach is provided which generates all designs in which M replicates are arranged in blocks of size two so that all main effects and two factor interactions are estimable. The method incorporates recognition of isomorphic designs to avoid double counting. A design ranking is proposed to give guidance on design selection which prioritises estimation of main effects. This is useful in practice since for some p, M combinations the number of designs is large (for example, for p=8 and M=4 there are 343 designs) and there can be considerable variation in the quality of estimation between designs.
The full factorial designs can be used as a source of root designs for construction of designs in fractional replicates, again in blocks of size two. The method is illustrated by examples with up to p=15 factors.
Nonnegative Matrix Factorization with Side Information for Time Series Recovery and Prediction
Authors: Jiali Mei (EDF Lab & Université Paris-Sud), Yohann De Castro (Université Paris-Sud), Yannig Goude (EDF Lab & Université Paris-Sud), Jean-Marc Azaïs (Institut de Mathématiques Université de Toulouse), Georges Hébrail (EDF Lab)
Primary area of focus / application: Other: French SFdS session on Computer experiments and energy
Keywords: Electricity consumption, Matrix factorization, Optimization, Time Series analysis
Submitted at 16-Mar-2017 14:39 by Jiali Mei
Accepted (view paper)
We consider Nonnegative Matrix Factorization in general linear measurement schemes, and propose a general framework which models non-linear relationship between features and the response variables.
We extend previous theoretical results in NMF to obtain a sufficient condition on the identifability of matrix factorization.
Based the classical Hierarchical Alternating Least Squares (HALS) algorithm, we propose a new algorithm (HALSX, or Hierarchical Alternating Least Squares with eXogeneous variables) which estimates the factorization model.
The algorithm is validated on both simulated and real electricity consumption data, to show its performance in reconstruction and prediction.
Solving Kalai-Smorodinski Equilibria Using Gaussian Process Regression
Authors: Victor Picheny (INRA), Mickael Binois (Chicago Booth School of Business), Abderrahmane Habbal (Universite Cote d'Azur)
Primary area of focus / application: Other: ISBA session on Bayesian Optimization
Secondary area of focus / application: Other: Bayesian Optimisation
Keywords: Multi-objective optimization, Gaussian process, Game theory, Bayesian optimization
Submitted at 20-Mar-2017 11:23 by Victor Picheny
Accepted (view paper)
Analysing Ordered Categorical Data with the Generalized Taguchi’s Statistic
Authors: Pietro Amenta (Department of Law, Economics, Management and Quantitative Methods, University of Sannio), Luigi D’Ambra (Department of Economics, Management and Institutions, University of Naples), Antonello D’Ambra (Department of Economics, University of Campania “Luigi Vanvitelli”), Anna Crisci (Department of Low and Economic Sciences, Pegaso Telematic University)
Primary area of focus / application: Modelling
Secondary area of focus / application: Mining
Keywords: Ordered categorical data, Taguchi’s statistic, Data mining, Quantification process, Logistic model
Submitted at 30-Mar-2017 18:24 by Pietro Amenta
A generalization of the Taguchi’s statistic, measuring the association between a nominal explanatory and an ordered categorical response variable is here proposed for analysing ordered categorical data in quality engineering. This new measure, based also on quantification process for the ordered categories, is named “Generalized Cumulative Chi-Squared Statistic” (GCCS) and a class of GCCS-type tests is also introduced. GCCS allows a graphical investigation of the optimal combination by considering the ordinal nature of the variable as well as in the quantification process of the ordered categories. We highlight that including the quantification process within the analysis is often an overlooked aspect in statistical literature.
An empirical study from industrial experiments for quality improvement has been developed. This study has been performed on a strategy based on the conjoint use of the Generalized Taguchi’s statistic and the Logistic Model. It allows to obtain an optimal combination of factors highlighting the levels to improve process quality.
Training Data Scientists: Challenges and Issues
Authors: Gilbert Saporta (CNAM)
Primary area of focus / application: Education & Thinking
Keywords: Data scientists, Shortage of talents, Life long learning, Teaching, Skills
Submitted at 2-Apr-2017 12:11 by Gilbert Saporta
Accepted (view paper)
In a second part, we report the impact on training programs of the emergence of these new jobs, according to the qualities required. We address finally the training challenge : despite a remarkable development, initial training by universities will not be enough to provide quickly the thousands of specialists which are needed. Besides other solutions (bootcamps, on line courses, etc.) we advocate lifelong training of scientists and engineers already in service in order to respond to the mass demand for data scientists. A particular experience is
presented. The conclusion calls for learned societies to be concerned about the certification of data scientists.
Elevator Pitches & Co – Effectively Presenting Myself and my Work
Authors: Kristina Lurz (prognostica GmbH), Andrea Ahlemeyer-Stubbe (Ahlemeyer-Stubbe Data Mining and More), Anja Zernig (KAI GmbH)
Primary area of focus / application: Other: Young Statisticians Session
Keywords: Effective presentation, Elevator pitch, Interactive session, Soft skills
Submitted at 2-Apr-2017 20:39 by Kristina Krebs
In circumstances like these, time is often limited to make a lasting impression. Imagine, at the beginning of a client’s meeting we need to present ourselves and our work appealingly and efficiently. If we do not come to the point, everyone gets bored and loses interest. It is up to each of us to use our time in the spotlight as efficiently as possible.
This Young Statisticians Session is an interactive session that consists of two interconnected parts: A presentation by an experienced professional statistician and consultant, Andrea Ahlemeyer-Stubbe, who talks about her experiences with respect to the do’s and don’ts in presenting oneself, as well as short example presentations by several colleagues. Together, we will be developing ideas and rules to improve our presentations/posters/elevator pitches, using the framework of a so-called world café.
If you would like to actively participate in the session by means of preparing a 3-minute presentation, please email us before the conference (firstname.lastname@example.org). With your contribution, we are looking forward to a lively and informative session.