Preface

Welcome

Welcome to the notes for Predictive Modeling for the course 2018/2019. The subject is part of the MSc in Big Data Analytics from Carlos III University of Madrid.

The course is designed to have, roughly, one lesson per each main topic in the syllabus. The schedule is tight due to time constraints, which will inevitably make the treatment of certain methods a little superficial compared with what it would be the optimal. Nevertheless, the course will hopefully give you a respectable panoramic view of different available statistical methods for predictive modeling. A broad view of the syllabus and its planning is:

  1. Introduction (first lesson)
  2. Linear models I (first/second lesson)
  3. Linear models II (second/third lesson)
  4. Linear models III (third/fourth lesson)
  5. Generalized linear models (fifth lesson)
  6. Mixed models (sixth/seventh lesson)
  7. Nonparametric regression (seventh lesson)

Some logistics for the development of the course follow:

  • The office hours are Thursdays from 19:15 to 20:15, at the classroom in which the session took place. Make use of them, especially instead of sending me lengthy emails with questions!
  • Questions and comments during lectures are mostly welcome. So just go ahead and fire! Particularly if these are clarifications, comments or alternative perspectives that may help the rest of the class.
  • Detailed course evaluation guidelines can be found here.

Contributions

Contributions, reporting of typos, and feedback on the notes are very welcome. Either send an email to edgarcia@est-econ.uc3m.es or, if you are willing to provide several contributions, ask for access to the GitHub repository, so you can open a pull request and submit your modifications for approval. Give me a reason for writing your name in the list of contributors!

List of contributors:

License

All the material in these notes is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Public License (CC BY-NC-ND 4.0). You may not use this material except in compliance with the former license. The human-readable summary of the license states that:

  • You are free to:
    • Share – Copy and redistribute the material in any medium or format.
  • Under the following terms:
    • Attribution – You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
    • NonCommercial – You may not use the material for commercial purposes.
    • NoDerivatives – If you remix, transform, or build upon the material, you may not distribute the modified material.