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Seminar Schedule: Year 2019


  •  Tuesday, 10th of December 2019. Time: 12:00

Jaume Barceló and Lidia Montero, Statistics and Operations Research Department, UPC

Usage of IT data in the modeling and calibration of urban networks. Application of GPS tracking and mobile phone data in the Barcelona metropolitan model.

  •  Friday, 27th of Setembrer 2019. Time: 12:00

Massimiliano Giacalone, Department of Economics and Statistics, University of Naples

Lp–norm estimators for linear regression models


  •  Monday, 17th  of June 2019. Time: 12:00

Melina Castro, Universidad de Costa Rica

Integrating Environmental Health characteristics for Health Techonology Assesstment. A systemic approach. The case of the ecosystem in Drake bay, Puntarenas, Costa Rica.

  • Friday, 15th of March 2019. Time: 12:30

David Moriña Soler, Barcelona Graduate School of Mathematics (BGSMath) – Departament de Matemàtiques, Universitat Autònoma de Barcelona (UAB)

Intervention analysis for low count time series with applications in public health

  • Friday, 22nd of February 2019. Time: 12:00

C.F. Jeff Wu, Georgia Institute of Technology, Atlanta, Georgia, USA.

Quality improvement: from autos and chips to nano and bio

  • Tuesday, 22nd of January 2019. Time: 12:30

Sharon-Lise Normand, Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.

Statistical Approaches to Health Care Quality Assessments


Usage of IT data in the modeling and calibration of urban networks. Application of GPS tracking and mobile phone data in the Barcelona metropolitan model

GUESTS: Lidia Montero and Jaume Barceló
C5 Building, Room C5016, Campus Nord, UPC

DATE:  Tuesday, December the 10th 2019. Time: 12:00


Nowadays several companies provide measurements of global positioning systems (GPS) either processed or not, generated by commercial vehicle fleets, Internet applications or car companies. Other companies provide origin-destination mobility matrices extracted from mobile telephone data processing. There is an irruption of data sources provided by new technologies in transportation, and the evaluation of the pros and cons they entail is still under study. 

The goal of this seminar is to deepen our understanding of the applicability of IT(information technology) data in transportation modeling. Unfortunately, real data are often affected by noice, uncertainty, errors, redundancy or even irrelevant information.  Wrong models will be obtained when built on incorrect or incomplete data. This is why preprocessing is among the most critical steps in data analysis. However, preprocessing has not yet been properly systematized. For this reason, as an example, we illustrate the necessary steps for the preprocessing of GPS tracking data and a standarized method is proposed. Moreover, the level of aggregation is at the location of the reference point for low-latency GPS positions along the path of the trip. The reliability of the travel time in OD routes is addressed and, how in dense urban netwoks several possibilities for route selection are avalilable and IT data provide an opportunity to understand the human behavior in route choice.



Jaume Barceló: From 1986 to 2014 full professor at the Statistics and Operations Research department at UPC and, since 2014, Emeritus Professor.  His research has been focused on the study of transportation models and optimization and simulation tecniques to deal with them. Currently, in collaboration with the german company PTV group, he works on the use of IT data for their use in transportation modeling.

Lídia Montero. Professor  at the Statistics and Operations Research Department at UPC. She holds a PhD in computer science at UPC. Professional experience in transport modelling and demand analysis in transportation. Research on Kalman filters for estimating dynamic origin-destination matrices and analysis of IT data for transportation applications.


Lp–norm estimators for linear regression models

GUEST: Massimiliano Giacalone
Edifici C5, Aula C5016, Campus Nord, UPC

DATE:  Divendres, 27 de setembre de 2019. Hora: 12:00

ABSTRACT: Introduction to Exponential Power Distribution (E.P.D.) and Lp–norm estimators. Comparison of the Lp–norm estimators with the Least Squares method (L2) by simulation studies, introducing a linear regression model in the case of multicollinearity. A comparative analysis applying three estimation methods to evaluate the empirical distribution of the regression parameters is considered. A Generalized Error Distribution Copula-based method for portfolios risk assessment. Comparison of  E.P.D with Gaussian distribution to financial markets  and examples. Final test to all seminar topics.


ABOUT THE AUTHOR:  Current position: Researcher (SECS-S/01, Statistics) at University of Naples “Federico II” Department of Economics and Statistics – Monte S. Angelo Complex, Via Cinthia, 80126 Naples. Graduate in “Statistics and Economics Sciences”, magna cum laude (Faculty of Economics – University of Palermo) he received his PhD in “Computational Statistics and Applications” from the University of Naples “Federico II”, Department of Mathematics and Statistics (supervisor Prof. N. C. Lauro). During the PhD, he was "visiting scholar" of Prof. A.H. Money in Henley Management College.  His research area encompasses the following subjects: Norm-p linear and nonlinear regression - Multidimensional Data Analysis - Big Data - Permutation Tests – Control Charts and Data Quality Control – Applications of Statistics in Economy and in Justice. Local component of the research groups funded by the University of Naples “Federico II” and co-financed by the relevant Ministry, he attended many Statistical Conferences organized by various national and international institutions, presenting numerous communications and papers. Membership of “Società Italiana di Statistica”, “International Association for Statistical Computing”, “International Biometric Society” and “Applied Statistical Association”, he gained over the years considerable teaching experience as Adjunct Professor of “Statistics”, “Economics Statistics”, “Quality Control” , “Probability”, “Statistical Inference”, “Medical Statistics”, at various Italian Universities (Bologna, Naples “Federico II”, Palermo, Catanzaro “Magna Graecia”, Cosenza ”University of Calabria”, Messina, Catania). He is author of about ninety published works concerning Methodological, Social and Economic Statistics.





Integrating Environmental Health characteristics for Health Techonology Assesstment. A systemic approach. The case of the ecosystem in Drake bay, Puntarenas, Costa Rica.

GUEST: Milena Castro
Languae: spanish
PLACE: Edifici B6, Sala d'Actes Manuel Martí Recober, Campus Nord, UPC

DATE:  Monday, June the 17th,  2019. Time: 12:00

ABSTRACT:  Policy making for environmental health implies consideration of a variety of indicators proposed by theWorld Health Organization and started by Centro de Investigación sobre el Síndrome del Aceite Tóxico y Enfermedades Raras (CISATER). Different observational perspectives can be identified with air, radiation, water, soil, residuals, sanitation, noise, traffic accidents, food safety, infraestructure, ocupational conditions, chemical emergencies and polluted areas. These spatial characteristics can be contrasted with longitudinal community observations of the socio-economic dynamics. However, challenges arise when data available is heterogeneous as comes from a collection of sources of information. A complex model can be defined when integrating more than one conceptual dimension. Dimensions can be specified according to observational techniques: survey data for a socioeconomical and epidemiological characterization of the population, environmental data based on analytic screening of water sources, and clinical epidemiology data can be obtained, in order to elaborate a systemic approach using Markov model simulation. The response of the model is related to the quality of the environment, to identify community development strategies according to its potential and needs satisfaction, like food safety. Model specification allows evaluation of technologies to be implemented at a populational level. Bio-Sand filters were designed and an experimental observation was undertaken with a family in Drake. A decrease in Escherichia coli was observed, but termotolerant coliforms had an increase, after comparing before and after bio-sand filtered water samples from Drake’s main basins. Evidencing a health policy for Drake’s ecosystem implies overtaking microbiological assessments. How an aqueduct should be developed for a population living around areas under forestal and water conservation? Nowadays, this is a relevant research question for Drake’s ecosystem, where biodiversity and water resources represent an important component of its turism based economy.

ABOUT THE AUTHOR: Milena Castro currently works at the University of Costa Rica. She is a doctor in Biostatistics from the University of Leicester, United Kingdom, and her education also includes statistics, clinical epidemiology and philosophy. He has also participated in various projects of research and social action.


Introduction to causal inference
GUEST: Aleix Ruiz de Villa
Languae: Catalan
PLACE: Seminari EIO, ETSEIB (Edifici Eng. Industrial), 6th floor, Campus Sud, UPC, Avda. Diagonal, 647, 08028, Barcelona
DATE:  Tuesday, June the 18th,  2019. Time: 12:00
ABSTRACT:  One of the most important parts in data analysis is estimating the effect of certain decisions. The most efficient way is using Randomized Controlled Trials. However these may result very costly, unethical or not viable. Moreover, often data are already available and we would like to exploit them. Unfortunately, direct analysis of non-experimental data may lead, even in very simple situations, to erroneous conclusions. A clear example of this is Simpson's paradox. Some of these problems can be dealt with if causality elements are included in the analysis. Causality has been a research object for philosophers for several centuries. During the 80’s, it started being formalized from a statistical point of view. Nowadays, causality modelling has three different scientific sources: computational, biomedical and econometric. In this talk we will see some examples where the conclusions drawn are very different if causality is modelled. We will also see when it is necessary, what are the associated risks, and the type of language and tools it involves.

ABOUT THE AUTHOR: Aleix Ruiz de Villa holds a Ph.D. in mathematics by UAB. He has acted as data science director in, SCRM (responsible for the mobile app by Lidl) and Onna. Fundator of Barcelona Data Science and Machine Learning Meetup (2014) and cofounder of the R users group in  Barcelona (2011-2017).



Intervention analysis for low count time series with applications in public health

GUEST: David Moriña SolerLANGUAGE: CatalanPlace: Seminari EIO, ETSEIB (Edifici Eng. Industrial), Planta 6, Campus Sud, Universitat Politècnica de Catalunya, Avda. Diagonal, 647, 08028, Barcelona.DATE: Friday, 15th of March 2019. Time: 12:30

ABSTRACT: It is common in many fields to be interested in the evaluation of the impact of an intervention over a particular phenomenon. In the context of classical time series analysis a possible choice might be intervention analysis, but there is no analogous methodology developed for low count time series. In this talk, we will introduce a modified INAR model that allows to quantify the effect of an intervention and is also capable of taking into account possible trends or seasonal behaviour. Several examples of application in different real and simulated contexts will also be discussed.

ABOUT THE AUTHOR: David Moriña holds a PhD in Mathematics obtained at Autònoma University in 2013. His area of interest is focused in mathematical modelling applied to health sciences, especially in the handling and analysis of longitudinal data, specifically time series data. He has broad experience in the design, development and analysis of clinical trials and epidemiological studies-working in several research centres, including the Technological Center in Nutrition and Health (CTNS), the Centre for Research in Environmental Epidemiology (CREAL) and the Catalan Institute of Oncology, developing new models for cancer research. He joined BGSMath - UAB in 2018, working on the development of new mathematical and statistical models with applications to cancer epidemiology. For more information on his research, see 


Quality improvement: from autos and chips to nano and bio

GUEST: C.F. Jeff WuLANGUAGE: EnglishPlace: Seminari EIO, ETSEIB (Edifici Eng. Industrial), Planta 6, Campus Sud, Universitat Politècnica de Catalunya, Avda. Diagonal, 647, 08028, BarcelonaDATE: Friday, 22nd of February, 2019. Time: 12:00

ABSTRACT: Quality improvement (QI) has a glorious history, starting from Shewhart’s path-breaking work on statistical process control to Deming’s high-impact work on quality management. Statistical concepts and tools played a key role in such work. As the applications became more sophisticated, elaborate statistical methods were required to tackle the problems. In the last three decades, QI has seen more use of experimental design and analysis, particularly the methodology of robust parameter design (RPD). I will first review some major ideas in RPD, focusing on its engineering origin and statistical methodology. I will then discuss more recent work that expands the original approach, including the use of feedback control and operating window. To have an effective solution, the subject matter knowledge often needs to be incorporated. Techniques for fusing data with knowledge will be presented. For advanced manufacturing and high-tech applications, there are new challenges and possible paradigm shift posed by three features: large varieties, small volume and high added value. I will speculate on some new directions and technical development. Throughout the talk, the ideas will be illustrated with real examples, ranging from the traditional (autos and chips) to the modern (nano and bio).

ABOUT THE AUTHOR: C.F. Jeff Wu is Professor and Coca Cola Chair in Engineering Statistics at the School of Industrial and Systems Engineering, Georgia Institute of Technology. He was the first academic statistician elected to the National Academy of Engineering (2004); also a Member (Academician) of Academia Sinica (2000). A Fellow of American Society for Quality, Institute of Mathematical Statistics, of INFORMS, and American Statistical Association. He received the COPSS (Committee of Presidents of Statistical Societies) Presidents’ Award in 1987, the COPSS Fisher Lecture Award in 2011, the Deming Lecture Award in 2012, the inaugural Akaike Memorial Lecture Award in 2016, the George Box Medal from Enbis in 2017, and numerous other awards and honors. He has published more than 175 research articles and supervised 48 Ph.D.'s. He has published two books "Experiments: Planning, Analysis, and Parameter Design Optimization" (with Hamada) and “A Modern Theory of Factorial Designs” (with Mukerjee).


Statistical Approaches to Health Care Quality Assessments

GUEST: Sharon-Lise NormandLANGUAGE: EnglishPlace: Building B6, Sala d'Actes Manuel Martí, Campus Nord, UPC (see map)DATE: Tuesday, 22nd of January, 2019. Time: 12:30

ABSTRACT: Health plan, hospital, and physician quality assessments are ubiquitous in the U.S. Hospital assessments in particular are used for licensure, maintenance, and some assessments are the basis for modification of hospital payments.  For instance, in 2017 the U.S. federal government withheld $528 million from 2597 hospitals as part of the Hospital Readmissions Reduction program described in the Affordable Care Act. This talk will describe the key statistical challenges in determining whether a hospital has higher than "expected outcome" including defining the "treatment", determining the counterfactual outcome, characterizing the role of unmeasured confounders, and accounting for data sparsity and uncertainty.

ABOUT THE AUTHOR:  Sharon-Lise Normand, Ph.D., is S. James Adelstein Professor of Health Care Policy (Biostatistics) in the Department of Health Care Policy at Harvard Medical School and Professor in the Department of Biostatistics at the Harvard TH Chan School of Public Health. Dr. Normand’s research focuses on the development of statistical methods for health services and outcomes research, primarily using Bayesian approaches, including the evaluation of medical devices in randomized and non-randomized settings for  pre- and post-market assessments,  causal inference, provider profiling, evidence synthesis, item response theory, and latent variables analyses. Her application areas include cardiovascular disease, severe mental illness, medical device safety and effectiveness, and medical technology diffusion. She earned her Ph.D. in Biostatistics from the University of Toronto, holds a Master of Science as well as a Bachelor of Science degrees in Statistics from the University of Western Ontario, and completed a post-doctoral fellowship in Health Care Policy at Harvard Medical School.