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Programación del seminario: Año 2019

 

  •  Martes, 10 de diciembre de 2019. Hora: 12:00

Jaume Barceló y Lidia Montero. Departamento de Estadística e Investigación Operativa, UPC

El uso de datos TIC en la modelización y calibración de redes urbanas. Aplicación de datos de seguimiento GPS y datos de telefonía móvil al modelo metropolitano de Barcelona 

 

  •  Viernes, 27 de septiembre de 2019. Hora: 12:00

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

Lp–norm estimators for linear regression models 

 

  •  Lunes 17 de junio de 2019. Hora: 12:00

Milena Castro

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

 

  •  Martes 18 de junio de 2019. Hora: 12:00

Aleix Ruiz de Villa, Barcelona

Introducción a la inferencia causal

 

  •  Viernes 15 de marzo de 2019. Hora: 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

  • Viernes, 22 de febrero de 2019. Hora: 12:00

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

Quality improvement: from autos and chips to nano and bio

  • Martes 22 de enero de 2019. Hora: 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


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El uso de datos TIC en la modelización y calibración de redes urbanas. Aplicación de datos de seguimiento GPS y datos de telefonía móvil al modelo metropolitano de Barcelona

PONENTES: Lidia Montero y Jaume Barceló

IDIOMA: Español
LUGAR
Edificio C5, Aula C5016, Campus Norte, UPC

FECHAMartes 10 de diciembre de 2019. Hora: 12:00

RESUMEN

Actualmente hay diversas compañías que proporcionan mediciones de sistema de posicionamiento global (GPS) procesadas o sin procesar generadas por flotas de vehículos comerciales, aplicaciones de Internet o compañías de automóviles. Otras empresas proporcionan matrices de movilidad origen-destino obtenidas a partir del procesado de datos de telefonía móvil. Las fuentes de datos procedentes de las nuevas tecnologías están entrando en el sector del transporte con fuerza y todavía es objeto de estudio los pros y contras que aportan. El objetivo del seminario es profundizar nuestra comprensión de la aplicabilidad de los datos TIC (tecnologías de la información y la comunicación) en el modelado del transporte. Desafortunadamente, los datos reales a menudo contienen ruido, incertidumbre, errores, redundancias o incluso información irrelevante. Se obtendrían modelos erróneos cuando se construyan sobre datos incorrectos o incompletos. Es por eso que el preprocesamiento es uno de los pasos más críticos en el análisis de datos. Sin embargo, el preprocesamiento no se ha sistematizado adecuadamente, por lo que, a título de ejemplo, se ilustran los pasos necesarios para el preprocesamiento de datos de rastreo GPS y se presenta una propuesta de sistematización. Además, el nivel de agregación está en la ubicación del punto de referencia para posiciones GPS de baja latencia a lo largo de la trayectoria del viaje. Se aborda la fiabilidad del tiempo de viaje en las rutas OD, y cómo en las redes urbanas densas se presentan múltiples posibilidades para la elección de ruta, y los datos de las nuevas tecnologías ofrecen una oportunidad para comprender el comportamiento de elección de ruta.

 

SOBRE LOS AUTORES

Jaume Barceló: De 1986 a 2014 catedrático del Departamento de Estadística e Investigación Operativa de la UPC, des de 2014 Profesor Emérito. Su investigación se ha orientado al estudio de modelos de transporte y a técnicas de optimización y simulación para su tratamiento. Actualmente, en colaboración con la empresa almena PTV Group, trabaja en la explotación de datos TIC para su uso en modelos de transporte.

Lídia Montero: Profesora del Departamento de Estadística e Investigación Operativa de la UPC. Doctora en Informática por la UPC. Experiencia professional en Modelización de Transporte y Análisi de la Demanda de Transporta. Investigación en filtros de Kalman para la estimación de matrices origen-destino dinámicas y Análisis de Datos TIC pera aplicaciones de transporte.

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Lp–norm estimators for linear regression models

PONENTEMassimiliano Giacalone
IDIOMA
Ingles
LUGAR
 Edificio C5, Aula C5016, Campus Nord, UPC

FECHA:  Viernes, 27 de setembre de 2019. Hora: 12:00

RESUMENIntroduction 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.


SOBRE EL AUTOR:  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.

 

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


PONENTE: Milena Castro

IDIOMA: Castellano
LUGAR: Edifici B6, Sala d'Actes Manuel Martí Recober, Campus Nord, UPC
FECHA:  Lunes, 17 de Junio de 2019. Hora: 12:00


RESUMEN:

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.

SOBRE EL AUTOR: Milena Castro actualmente trabaja en la Universidad de Costa Rica. Es doctora en Bioestadística por la universidad de Leicester, United Kingdom, y su formación abraza la estadística, la epidimiologia clínica y la filosofia. También ha participado en diversos proyectos tano de investigación com de acción social.

 


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Introduccióm a la inferencia causal


PONENTE: Aleix Ruiz de Villa
IDIOMA: Catalán
LUGAR: Seminario EIO, ETSEIB (Edificio Ing. Industrial), Planta 6, Campus Sur, UPC, Avda. Diagonal, 647, 08028, Barcelona
FECHA:  Martes, 18 de Junio de 2019. Hora: 12:00
RESUMEN: Una de las partes más importantes del análisis de datos es estimar el efecto que han tenido ciertas decisiones. La forma más eficiente es llevar a cabo experimentos (Randomized Controlled Trials). Aun así, éstos pueden resultar muy costosos, no éticos o inviables. Además, muchas veces ya disponemos de datos, y quisiéramos sacarles algun provecho. Desafortunadamente, el análisis directo de datos no experimentales puede llevar, incluso en casos sencillos, a conclusiones erróneas o a callejones sin salida. Un ejemplo destacado es la paradoja de Simpson. Algunos de estos problemas se pueden soslayar si se incluyen elementos de causalidad en el análisis. La causalidad ha sido objeto de estudio para los filósofos desde hace siglos. En los años 80 se empezó a formalizar desde el punto de vista estadístico. Actualmente la modelización de la causalidad tiene tres fuentes científicas distintas: cmputacionales, biomédicas y ecnométricas. en esta charla veremos algunos ejemplos donde modelizando la causalidad se obtienen conclusiones bastante distintas a las obtenidas cuando no se utiliza. También veremos cuándo es necesaria, qué riesgos tiene, y qué tipo de lenguaje y herramientas requiere.

SOBRE EL AUTOR: Aleix Ruiz de Villa es doctor en matemáticas por la UAB, ex director de data science de LaVanguardia.com, SCRM (responsables de la app de movil en Lidl) i Onna. Fundador del Barcelona Data Science and Machine Learning Meetup (2014) y cofundador del grupo de usuarios de R de Barcelona (2011-2017).

 


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

PONENTE: David Moriña Soler
IDIOMA:
Catalán
LUGAR:
Seminari EIO, ETSEIB (Edifici Eng. Industrial), Planta 6, Campus Sud, Universitat Politècnica de Catalunya, Avda. Diagonal, 647, 08028, Barcelona

FECHA: Viernes 15 de marzo de 2019. Hora: 12:30

RESUMEN: 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.

SOBRE EL AUTOR: 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 https://bgsmath.cat/people/?person=david-morina-soler


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Quality improvement: from autos and chips to nano and bio 

PONENTE: C.F. Jeff Wu
IDIOMA:
Inglés
LUGAR:
Seminari EIO, ETSEIB (Edifici Eng. Industrial), Planta 6, Campus Sud, Universitat Politècnica de Catalunya, Avda. Diagonal, 647, 08028, Barcelona.

FECHA: Viernes 22 de febrero de 2019. Hora: 12:00

RESUMEN: 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).

SOBRE EL AUTOR: 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).


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Statistical Approaches to Health Care Quality Assessments
PONENTE: Sharon-Lise Normand
IDIOMA: Inglés
LUGAR: Edificio B6, Sala d'Actes Manuel Martí, Campus Nord, UPC (ver mapa)

FECHA: Martes 22 de enero de 2019. Hora:12:30

RESUMEN: 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.

SOBRE EL AUTOR: 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.


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