Curriculum vitae Publications Teaching Map of the site En español

Publications

Papers

• Alonso, Andrés-M., David Casado and Juan-J. Romo (2012). Supervised classification for functional data: A weighted distance approach. Computational Statistics and Data Analysis. 56 (7), 2334--46. URL Code
  A previous version was published as working paper at the Statistics Department of the Universidad Carlos III de Madrid: Alonso, Andrés-M., David Casado and Juan-J. Romo (2009). Classification of Functional Data: A Weighted Distance Approach.
  
  
• Alonso, Andrés-M., David Casado, Sara López-Pintado and Juan-J. Romo (2008). A Functional Data Based Method for Time Series Classification. Working paper. Departamento de Estadística. Universidad Carlos III de Madrid.     Code
  
  
• Alibés, Andreu, Edward R. Morrissey, Andrés Cañada, Óscar Rueda, David Casado, Patricio Yankilevich, Antonie de Weck and Ramón Díaz-Uriarte (2007). Asterias: a parallelized web-based suite for the analysis of expression and aCGH data. Cancer Informatics, 2, 1-9.
  

Code

• Collaboration on the following code, in the language MATLAB:

  Code of a Package Implementing the Supervised Classification of Functional Data by Applying a Weighted Distance Approach.

  • Helping file:
  • Code (the helping file is included) in the compressed format .rar:
  • Last update: 19th January 2012. Now, the user can select between the derivatives or the differentials. The multivariate k-nearest neighbours method is also applied to the vector of discriminant variables. A provisional estimation of the error rates is given during the computes made to generate the figures. It is possible to consider only some derivatives or differentials, not necessarily consecutive. The code is simpler and quite faster when many derivatives or differentials are considered. The functions of this and the following packages can be joined in the same directory, four of them are equal.
  • A second version of this package is being developed.  
  
  
• Collaboration on the following code, in the language MATLAB:

  Code of the working paper A Functional Data Based Method for Time Series Classification.

  • Helping file:
  • Code (the helping file is included) in the compressed format .rar:
  • Last update: 9th July 2011. A process, based on depth, has been implemented to facilitate the application of the methodology we propose when sample sizes are big. Now, the code is simpler and slightly faster. The functions of this and the previous packages can be joined in the same directory, four of them are equal.
  
  
• Collaboration on the code, in the language R, of the online tool

  Asterias: Suite for the analysis of genomic data.

  
  
• Collaboration on the code, in the language R, of the package geSignatures and the online tool:

    SignS: Finding molecular signatures with survival and censored data.

Other works

• Casado, David (2010). Classification Techniques for Time Series and Functional Data. Doctoral thesis. Departamento de Estadística. Universidad Carlos III de Madrid. Advisors: Andrés-M. Alonso y Juan-J. Romo.
  (This thesis includes the two working papers Alonso et al. [2008, 2009].)
  
  
• Casado, David (2003). Técnicas bootstrap en análisis discriminante para series temporales. Trabajo de investigación en Estadística. Programa de doctorado en Matemáticas. Universidad Autónoma de Madrid. Advisor: Andrés-M. Alonso.
  
  
• In my ever decreasing free time, I play the saxophone a little and the Castilian dulzaina (a folk instrument, you can read about it in http://en.wikipedia.org/wiki/Dulzaina). With this latter instrument, when we were younger and we had more hair, we recorded the following disk, produced by Luis Martín, member of the known Spanish folk group Nuevo Mester de Juglaría, and registered in Armando Records studios, in Valladolid:
  
  

  To listen it: http://www.deezer.com/... To buy it: http://www.sonifolk.com/... To download it legally: http://music.napster.co.uk/...

  
  I hope that this disk should add many merits to my research curriculum vitae... ()
  

David Casado de Lucas

I would like to thank peaple who make projects like Linux, Debian, GNU Emacs, GIMP, The W3C Markup Validation Service, The W3C CSS Validation Service, OpenOffice.org, LibreOffice.org, The R Project for Statistical Computing, The LaTeX project and The MiKTeX project among others, possible, and for allowing users, for free, to use such quality software.