08:30

  • Recogida documentación/Inscripción
    08:30 - 18:00

08:50

  • Inauguración SEIO'2018
    Auditorio
    08:50 - 09:30

09:40

  • Sesión plenaria Estadística: Simon N. Wood
    Moderador/a: María Dolores Ugarte Martínez
    Auditorio
    09:40 - 10:40
    Smooth Regression Beyond the Exponential Family

    Regression models specified in terms of smooth functions of predictors are widely applicable in fields as diverse as fisheries, epidemiology and ecology. Their advantage is that they possess a flexibility that mitigates against model mis-specification. But flexibility brings challenges, both computational and statistical: stable computation can become a difficult task and we also need methods for objectively choosing the appropriate degree of smoothness of the model components. Within the exponential family of distributions, quite reliable methods are now available addressing both these challenges. But can the same be achieved in more general settings outside the exponential family? This plenary lecture will review some of the options, taking examples from location scale modelling, smooth survival analysis and quantile regression.

10:50

12:10

  • Pausa Café
    12:10 - 12:30

12:30

12:50

  • Sesión plenaria Estadística Pública: Eduardo Barredo Capelot
    Moderador/a: Miguel Ángel De Castro Puente
    Auditorio
    12:50 - 13:50
    Skills for the New Generation of Statisticians

    At the core of the modernisation of official statistics will be the capability to incorporate new data sources and to take benefit from disruptive technologies, for example smart meters, web technologies and user experience platforms. These new capabilities require new types of skills and competences that were not specifically found in the traditional official statistician skill sets. In this paper, we analyse the competence profile of an official statistician with a particular focus on new data science competences.

    There are numerous examples of new data sources that have potential in this sense: administrative records and registers, as well as large digital data sources, such as road sensors, scanner data or Internet-based data. These large digital data sources are known best as big data. Potential new technologies, on the other hand, range from web-scraping algorithms to linked data opportunities and from multi-mode data collection to combining survey and administrative data. The list can be continued even further.

    By embracing new data sources and technologies, National Statistical Institutes (NSIs) can produce faster, more accurate statistics and more comprehensive indicator sets adapted to understanding of increasingly complex, rapidly changing and global phenomena. In short, new data sources are a way to better meet users’ needs. This must be done in a way where we do not jeopardise the recognised robustness and quality of official statistics in order to strengthen our competitive asset in the rapidly changing information ecosystem.

    One key factor in meeting these challenges is the development and building of the necessary skills and competences. In addition, statistical organisations will have to create favourable conditions for new production methods and using data science skills with success. This means, for example, 1) establishing an innovative culture, where experimental activities are commonplace, 2) building and maintaining collaborative and multidisciplinary data science teams, 3) recruiting individual data scientists and using long-term personnel planning, 4) training and supporting personnel in identifying themselves with new competence requirements, and 5) aspiring to management and leadership practices, which make these changes possible.

13:50

  • Pausa Comida
    13:50 - 15:30

15:30

15:50

16:10

17:10

19:30

  • Visita guiada+Recepción
    19:30 - 21:30

Últimas noticias

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