[20° Forum SIS]Avviso di seminari Università di Palermo

[Stefano Barone]

In data13 dicembre ore 11.00-13.00, Aula Rubino (Viale delle Scienze, 
Edificio 8, 90128 Palermo), si terranno due seminari molto interessanti 
(riferimenti in calce).

Cordiali saluti.
Stefano Barone, Alberto Lombardo
Università di Palermo
Dipartimento di Ingegneria Chimica, Gestionale, Informatica e Meccanica.



*"Experimental Design for Engineering Dimensional Analysis"*
Will be presented by
Professor Christopher J. Nachtsheim
Carlson School of Management, University of Minnesota, USA
*Abstract*
Dimensional Analysis (DA) is a fundamental method in the engineering and 
physical sciences for analytically reducing the number of experimental 
variables affecting a given phenomenon prior to experimentation. Two 
powerful advantages associated with the method, relative to standard 
design of experiment (DOE) approaches are: (1) a priori dimension 
reduction, (2) scalability of results. The latter advantage permits the 
experimenter to effectively extrapolate results to similar experimental 
systems of differing scale. Unfortunately, DA experiments are 
underutilized because very few statisticians are familiar with them. In 
this paper, we first provide an overview of DA and give basic 
recommendations for designing DA experiments. Next we consider various 
risks associated with the DA approach, the foremost among them is the 
possibility that the analyst might omit a key explanatory variable, 
leading to an incorrect DA model. When this happens, the DA model will 
fail and experimentation will be largely wasted. To protect against this 
possibility, we develop a robust-DA design approach, that combines the 
best of the standard empirical DOE approach with our suggested design 
strategy. Results are illustrated with some straightforward applications 
of DA. A Matlab code for computing robust-DA designs is available as 
supplementary material online.
*Biography*
Christopher J. Nachtsheim holds the Frank A. Donaldson Chair of 
Operations Management and Chair of the Supply Chain and Operations 
Department in the Carlson School of Management at the University of 
Minnesota.  Dr. Nachtsheim received his Ph.D. in Operations Research 
from the University of Minnesota, served as staff member in the 
Statistics Group at Los Alamos National Laboratory from 1978-1981, and 
as Senior Statistician at General Mills from 1982-1984. In 1984 he 
joined the University, serving as Chair in the Department of Operations 
and Management Science from 1993-1996 and Associate Dean of Faculty and 
Research from 1996-2000.  Dr. Nachtsheim's teaching and research 
interests center on the optimal design of industrial experiments, 
regression and predictive modeling, and quality management. Among his 
major publications are two texts: Applied Linear Statistical Models, 5th 
Edition, 2005, Richard D. Irwin, and Applied Linear Regression Models, 
4th Edition, 2004, Richard D. Irwin (both with John Neter, Michael 
Kutner, and William Li).  Professor Nachtsheim has published over 50 
articles in the statistics literature and has served as associate editor 
for many of the top journals in his field, including Journal of the 
American Statistical Association, Technometrics, Journal of Quality 
Technology, Statistics and Computing, and Journal of Statistical 
Computation & Simulation.  He served as Examiner, Malcolm Baldrige 
National Quality Award in 1996.  He is a three-time recipient (1991, 
2009, and 2011) of the Brumbaugh Award of the ASQ for best paper 
published in the area of industrial quality control, two-time recipient 
of the Lloyd S. Nelson Award of the ASQ for the published paper having 
the greatest impact on practitioners (2010 and 2012), recipient of the 
Jack Youden Prize for the best expository paper published in 
Technometrics in 2011, and the recipient of the 1992 ASME (CAE/CAD/CAM) 
National Best Paper Award.  Dr. Nachtsheim is a Fellow of the American 
Statistical Association.

*"Conjoint Analysis and Discrete Choice Experiments for Quality 
Improvement"*
Will be presented by
*Professor William Li*
Carlson School of Management, University of Minnesota, USA
*Abstract*
Conjoint analysis and discrete choice experiments, which were developed 
in fields such as marketing and economics, are useful for understanding 
the voice of the customer to guide quality-improvement efforts. 
Unfortunately, these methods have received relatively little attention 
in the quality area. In this presentation, we provide some guidelines 
for the use of conjoint analysis and discrete choice experiments. We 
discuss what they are, why they are useful methodologies for quality 
improvement, and how a discrete choice experiment can be carried out. We 
demonstrate the methodology by discussing a real case study in quality 
improvement in detail. We then introduce a new class of designs for 
discrete choice experiments that are robust for a class of possible 
models. We provide several examples in which an optimal design based on 
the main-effects only models is shown to have limited capability for 
estimation of two-factor interactions, whereas the proposed robust 
designs perform well in the presence of two-factor interactions. We 
conclude with a summary of key points and directions for future research.
*Biography*
William Li is Professor of Supply Chain and Operations Department at the 
Carlson School of Management, the University of Minnesota. He received 
his B.Sc. in Applied Mathematics from the Tsinghua University in China, 
and both his M.S. and his Ph.D. in Statistics from the University of 
Waterloo in Canada. After he graduated in 1995, he worked in the 
Reliability Methods Department as a reliability engineer at Ford Motor 
Company and won the 1996 Customer-Driven Quality Award at Ford. In 1996 
he joined the Operations and Management Science Department at the 
University of Minnesota. He has taught international executive MBA 
courses in different programs in Austria, China, and Poland and won the 
2006 Teaching Award of the Carlson School of Management. He is an 
invited professor at Fudan University, Shanghai, Cina and has taught 
executive MBA courses for the School of Management at Fudan. His 
research articles have appeared in journals such as Journal of American 
Statistical Association, Technometrics, Journal of Quality Technology, 
Journal of Statistical Planning and Inference, and Neural Computation. 
Among his publications is a textbook "Applied Linear Statistical Models" 
(with Kutner, Nachtsheim, and Neter), 5th Edition, Irwin. Dr. Li is a 
Fellow of the American Statistical Association.


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