ENBIS-18 in Nancy

2 – 25 September 2018; Ecoles des Mines, Nancy (France) Abstract submission: 20 December 2017 – 4 June 2018

My abstracts

 

The following abstracts have been accepted for this event:

  • The Real Work of Data Science: How to Turn Data into Information, Better Decisions, and Stronger Organizations

    Authors: Ron S. Kenett (KPA Ltd., Raanana, Samuel Neaman Institute, Technion, Haifa and Institute for Drug Development, The Hebrew University, Jerusalem)
    Primary area of focus / application: Other: Keynote
    Keywords: Data science, Data scientist, Deming, Director of statistical methods, Information quality (InfoQ), Practical statistical efficiency (PSE), lLfe cycle view, Data integration, Data structure, Compositional data
    Submitted at 23-Jul-2018 13:30 by
    Accepted
    3-Sep-2018 16:40 George Box Award: Ron Kenett. Award Talk on "The Real Work of Data Science: How to Turn Data into Information, Better Decisions, and Stronger Organizations"
    In 1962, John Tukey called for a reformation of academic statistics. In a famous paper titled “The Future of Data Analysis,” he pointed to the existence of an as-yet unrecognized science, whose subject was learning from data. He called it “data analysis.” In 1997, C. F. Jeff Wu, upon his inauguration lecture as Carver Professor of Statistics at University of Michigan, presented a talk titled “Statistics = Data Science?” in which he advocated that statistics be renamed data science and statisticians data scientists. My career in Statistics involved working on data science, quality management, biostatistics, experimental design, causality models, customer surveys, compositional data, and other application domains. The implication is that, while there is considerable overlap between statistics and data science, the two are not the same. The talk will provide a statistician’s perspective on data science. The main message is that statistics needs to contribute its unique selling points (what marketing people call USPs) to the data science movement. That is, as statisticians, we need to propose methods and ideas that complement and extend the current development of data science. Over the years, I worked with colleagues on several frameworks that do so. The first framework was a simple approach to assessing impact that we labeled practical statistical efficiency (Kenett et al, 2003). The second one emphasizes a life-cycle view of statistics, starting from problem elicitation on through impact assessment (Kenett, 2015). The third framework is based on eight dimensions for assessing information quality, labeled InfoQ (Kenett and Shmueli, 2016). These dimensions can serve as a research roadmap for modern statistics. Examples of research in two of the InfoQ dimensions, Data Structure and Data Integration, will be mentioned. Moreover, building on John Tukey’s 1962 paper and Jeff Wu’s 1997 address, the role of data science (and data scientists) in organizations will be discussed on the basis of a forthcoming book on this topic (Kenett and Redman, 2019). Specifically, the talk will refer to the evolution of the leader in statistical methodology or Director of Statistical Methods advocated by Deming as necessary in organizations who aim to become more competitive (Deming, 1986). So, responding to Tukey, “the future is here, and it is called data science”, and Statistics has a major role in it.

    1. Deming, W.E. (1986), Out of the Crisis. MIT Press, Cambridge, MA.
    2. Kenett, R.S., Coleman, S.Y. and Stewardson, D. (2003), “Statistical Efficiency: The Practical Perspective,” Quality and Reliability Engineering International, 19, pp. 265-272.
    3. Kenett, R.S. (2015), “Statistics: A Life Cycle View, Quality Engineering (with discussion),” 27(1), pp. 111-129.
    4. Kenett, R.S and Shmueli, G. (2016), Information Quality: The Potential of Data and Analytics to Generate Knowledge, John Wiley and Sons.
    5. Kenett, R.S. and Redman, T.C. (2019), The Real Work of Data Science: How to turn data into information, better decisions, and stronger organizations, John Wiley and Sons.
    6. Tukey, J. (1962), “The Future of Data Analysis,” The Annals of Mathematical Statistics, 33, pp. 1–67.
  • Statistical Tools for Quantitative Management and Fraud Detection

    Authors: Sotiris Bersimis (Hellenic Organization for Health Care Services Provision (EOPYY), University of Piraeus)
    Primary area of focus / application: Other: Best Manager Award
    Keywords: Statistics, Data driven decisions, Fraud detection, Quantitative management, Statistical quality control, Quality indices
    Submitted at 24-Jul-2018 15:02 by Sotiris Bersimis
    Accepted
    A challenge, in the era of economic crisis and uncertainty, is to provide health care services in an efficient and effective manner. The location of the service centers, the geographical distribution of patients, and the provision of specialist services are some of the topics an Organization responsible for health care services provision has to arrange. Other topics are the assessment of quality, safety and effectiveness of healthcare services provided by private and public health care providers. A prominent place among these topics hold expenditure monitoring and control. A solution to this is the use of statistical models and analytical tools. In this presentation, we give a brief review of the use of statistics to improve health care decision-making under uncertainty. Case studies from the Hellenic National Organization for Healthcare Services Provision (NOHSP) will also be presented.
  • An Object Oriented Approach to the Analysis of Complex Data

    Authors: Alessandra Menafoglio (Politecnico di Milano)
    Primary area of focus / application: Other: Young statistician award
    Keywords: Object oriented data analysis, Kriging, Statistical emulators
    Submitted at 25-Jul-2018 13:41 by Alessandra Menafoglio
    Accepted
    Modern business and industrial applications often involve Big Data. I will focus on a few methodologies that allow analyzing Big Data when “Big” refers more to the data complexity than to the sample size. I will consider an object oriented approach, whose foundational idea is to interpret the data point – regardless of their complexity – as the “atom” of the analysis. The statistical analysis of these atoms is then performed by considering them as points within an appropriate mathematical space. I will illustrate a few object oriented methods addressing the problems of analyzing Big Data from business and industrial application, with a particular attention to the possible (spatial) dependence among data and to their constraints. I will finally discuss a recent object-oriented method for statistical meta-modeling.
  • Missing Data Imputation in Multivariate Data Sets

    Authors: Abel Folch-Fortuny (DSM Food Specialties B.V.)
    Primary area of focus / application: Other: Young statistician award
    Keywords: Missing data, Imputation, Trimmed score regression, Software, Networks, Near infrared spectra
    Submitted at 27-Jul-2018 14:54 by Abel Folch Fortuny
    Accepted (view paper)
    One of the big challenges in data analysis in any research and industrial field is how to deal with missing values. In multivariate model building stages, when dealing with biological, economics or business databases, practitioners usually deal with 10-30% of missing values. In complex industrial processes, where hundreds of variables are collected per batch, 30–60% of missing data can appear in their historical data sets. Finally, with the paradigm of big data, thousands of variables are collected for large sets of individuals, having sometimes more than 70% of missing values in their databases.
    A framework for missing data imputation in exploratory and predictive models will be given in this talk, together with a comparative study with state-of-the art techniques. These new algorithms have been included in a freely available software for missing data imputation, including a graphical user-friendly interface. Furthermore, the use of these new algorithms has been extended to the reconstruction of biological networks, curating data for missing values and faulty observations; and to calibration transfer between near infrared spectrometers, reconstructing unmeasured spectra using imputation methods.
  • My abstract for ENBIS-18

    Authors: Lluis Marco-Almagro (UPC BarcelonaTech)
    Primary area of focus / application: Consulting
    Keywords: Keyword 1, Keyword 2, Keyword 3, Keyword 4, Keyword 5
    Submitted at 19-Sep-2018 09:54 by Lluis Marco-Almagro
    Accepted (view paper)
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