Date

11 – 15 June 2018

Location

Aula A1 del Centre de Recerca Matemàtica (CRM)

Summary

Functional data arise when one of the variables of interest in a data set can be seen naturally as a smooth curve or function. Functional Data Analysis (FDA) can then be thought of as the statistical analysis of samples of curves.  In the last two decades, FDA techniques have evolved rapidly, which has allowed the FDA to reach a remarkable methodological maturity. Many standard statistical methods have been adapted to functional data: regression models (lm, glm, non-parametric regression, …), multivariate analysis (PCA, MDS, Clustering, Depth measures, …), time series, spatial statistics, among other. At the same time, its methods have been applied to quite broadly in medicine, science, business, engineering, demography and social sciences, etc. This course offers an introduction to FDA and presents some of the R libraries oriented to this type of data. The aim is that at the end of the course the students are able to identify situations in which they can treat their data as functional, to represent them computationally, to apply simple FDA techniques (descriptions, dimensionality reduction, regression) and to visualize the results.

Potential participants

Master and PhD students, postdoc researchers and any researcher with interest in the topic.

Minimum requirements and assessment criteria

The students should have basic knowledge of Statistics (e.g. lineal regression) and Multivariate Analysis (principal components or multidimensional scaling) and in programming in R.

Date

11 – 15 June 2018

Location

Aula A1 del Centre de Recerca Matemàtica (CRM)

Summary

Functional data arise when one of the variables of interest in a data set can be seen naturally as a smooth curve or function. Functional Data Analysis (FDA) can then be thought of as the statistical analysis of samples of curves.  In the last two decades, FDA techniques have evolved rapidly, which has allowed the FDA to reach a remarkable methodological maturity. Many standard statistical methods have been adapted to functional data: regression models (lm, glm, non-parametric regression, …), multivariate analysis (PCA, MDS, Clustering, Depth measures, …), time series, spatial statistics, among other. At the same time, its methods have been applied to quite broadly in medicine, science, business, engineering, demography and social sciences, etc. This course offers an introduction to FDA and presents some of the R libraries oriented to this type of data. The aim is that at the end of the course the students are able to identify situations in which they can treat their data as functional, to represent them computationally, to apply simple FDA techniques (descriptions, dimensionality reduction, regression) and to visualize the results.

Potential participants

Master and PhD students, postdoc researchers and any researcher with interest in the topic.

Minimum requirements and assessment criteria

The students should have basic knowledge of Statistics (e.g. lineal regression) and Multivariate Analysis (principal components or multidimensional scaling) and in programming in R.

Course organized by Servei d’Estadística Aplicada with the support of the BGSMath, through the ”María de Maeztu” Programme for Units of Excellence in R&D” (MDM‐2014‐0445).