Dates: 31 August -2 September 2018 (Friday afternoon to Sunday afternoon)
Venue: Alexandru Ioan cuza University of Iasi, Iasi, Romania.
Organizers: Ana Colubi and Cristian Gatu on behalf of the CRoNoS COST Action.
1st edition: http://www.compstat2016.org/CRoNoS_SummerCourse.php
Manuel Febrero, University of Santiago de Compostela, Spain
Manuel Escabias, University of Granada, Spain (short tutorial on Friday afternoon, in common with the COMPSTAT programme)
Frederic Ferraty, Toulouse Jean Jaures University, France will give a short tutorial on Friday morning during COMPSTAT 2018 open to the Summer Course participants
Registration (see details below)
Functional data analysis (FDA) has received substantial attention in recent years, with applications arising from various disciplines, such as engineering, public health, finance etc. In general, the FDA approaches focus on nonparametric underlying models that assume the data are observed from realizations of stochastic processes satisfying some regularity conditions. The estimation and inference procedures usually do not depend on just a finite number of parameters, which contrasts with parametric models, and exploit techniques such as smoothing methods, dimension reduction that allow data to speak for themselves. This tutorial will cover general ideas in functional data analysis, such as functional principal component analysis, basis representation models, functional linear regression as well as more flexible regression type models, and so on. Some basic computing and data analysis using R and/or Matlab will be also introduced.
This course reviews some of the main techniques in Functional Data Analysis focusing on its computational aspects. The course covers the following list of the topics: Representation, Simulation, Exploratory Data Analysis, Regression, Classification and Testing providing examples of use using the R-package fda.usc.
Manuel Escabias, University of Granada, Spain.
The Functional Data Analysis encompasses a great variety of statistical methods for the analysis of curves, surfaces or any other function that varies continuously. In most cases we have a sample of curves that measure the time evolution of a variable such as temperature or stock market price, but they can also be functions that depend on other magnitudes, as in chemometrics where the spectrum of chemical substances depend on wavelength or in sports sciences where human movement curves are functions of the percentage of a movement cycle. In practice, it is technically impossible to record complete curves and discrete observations are available instead. The analysis of functional data makes a functional treatment of the curves taking into account its continuity, smoothness, etc. Different statistical methods have been adapted to the analysis of functional data: functional principal component analysis, functional regression models (linear and non-linear, generalized, etc.), functional classification methods or functional discriminant analysis, among others.
Most of functional data analysis methods have been programmed in R in the 'fda' package developed by J.O. Ramsay, H. Wickham, S. Graves and G. Hooker in the McGill University of Montreal (Canada) as a result of the book Functional Data Analysis written by J.O. Ramsay and B.W. Silverman in 1997 (Springer), where these statistical methods are described.
Recently, our research group "Modeling and Prediction with functional data" of the Department of Statistics and O.R. of the University of Granada (FQM307) have developed Statfda, an online application for the use of some of the functional data methods based on the 'fda package 'de R. This tool is the result of the research project of Junta de Andalucía "Statistical methods of functional data analysis. Development of a WEB interface for its application (P11-FQM-8068)" . Despite of using functions of R, the application is programmed for the use of functional data analysis methods without the need of knowing the R programming.
This talk aims to show users this application and how it works. The index of the presentation is
- Introduction to functional data analysis.
- Information management: different possibilities of data files.
- Basis expansion of sample curves.
- Functional Data Analysis: Exploratory analysis, Functional PCA, Functional principal component linear regression, Functional principal component logistic regression.
- Results of the analysis: Display by screen (graphics ) and download of results (text).
The interactive programme provides a quick overview of the schedule of the workshop, with easy access to all the sessions including their location as well as all abstracts.
You can add it to the bookmarks of your favourite device in order to access all the information during the conference.
Alternatively you can check the static list of abstracts (optimized for Mozilla and Google Chrome). .
- In order to apply for the grants candidates should submit their CV by e-mail to email@example.com.
- Deadline for applications: 10th May 2018.
- Granted candidates will be informed by e-mail after the deadline and must send their flight tickets and accommodation booking 7 days after the notification to firstname.lastname@example.org to secure their grants. Otherwise, their grants will be revoked and assigned to other candidate.
- The granted candidates must attend all the sessions of the Summer course and sign the attendance list in order to obtain their grants.
The registration fee includes participation to all sessions of the Summer Course, material, coffee breaks and a welcome reception (pre-registration is mandatory). The registration also includes attendance to the CRoNoS Workshop on Functional Data Analysis
|Early bird registration |
until March 15th, 2018
|Standard registration |
until June 3rd, 2018
|Late registration |
until August 7th, 2018
|Cash registration |
after August 7th, 2018
|Workshop and Summer Course Dinner (Saturday 1st of September 2018)||35€|