FDA Workshop. Dr.Jim Ramsay. Dec 9-11, 2004

Internet

Web: Functional Data Analysis
FTP: Software for R, S-PLUS, Matlab.

Slides material

    Day 1

  • An Introduction to Functional Data Analysis (Intro): pdf or ppt
  • Human Growth: From Data to Functions: pdf or ppt
  • Basis Basics: pdf or ppt
  • Smoothness with Roughness Penalties: pdf
  • Phase-Plane Plotting the Nondurable Goods Index: pdf or ppt
  • Phase and Amplitude Variation in Montreal Weather: pdf or ppt
  • Human Growth: Separating Phase from Amplitude Variation: pdf
  • Functional Principal Components Analysis: pdf
  • Canonical Correlation Analysis: pdf
  • Mouse Livers: Derivatives and Functional Linear Models: pdf or ppt
  • Functional Data Analysis of Continuous Judgments in Music Cognition: pdf or ppt

    • Day 2

  • An Overview of the Functional Linear Model: pdf
  • Modeling functional responses with multivariate covariates: pdf
  • Functional Responses, Functional Covariates, and the Concurrent Model: pdf
  • Functional Linear Models for Scalar Responses: pdf
  • Functional Responses and Functional Covariates: pdf
  • Functional Methods for Testing Data: pdf or ppt
  • Derivatives and Functional Linear Models: pdf
  • From Data to Differential Equations: pdf or ppt
  • Psychometrics, Dynamics, and Functional Data Analysis: pdf or ppt
  • Handwriting: Registration and Differential Equations: pdf or ppt
  • The Lupus Project: pdf

    • Miscellaneous

  • An Introduction to Functional Data Analysis (Lecture) [duplicates much of Day 1 material]: pdf ppt
  • A Predator-Prey Model: pdf or ppt
  • ADHD Reaction Times: Densities, Mixed Effects and PCA: pdf or ppt

Abstract

The workshop is designed to provide something of value to as wide a range of participants as possible, ranging from those interested in whether FDA might prove useful in their research, to statistical methodologists looking for research problems and interested in new techniques.

Each lecture will begin with one or more case studies, and the initial lectures will be almost entirely case studies. These aim to show the range of applications possible, show what insights might be gained from using FDA methods, and illustrate the challenges that are specific or particularly relevant to the analysis functional data. Case studies are not "how to" sessions, but rather address questions like, "Why should I consider this approach?" and "What should I watch out for?"

The first half of the first day will also be more oriented to the preliminaries of functional data analysis:

  • What are functional data?
  • How should they be prepared for analysis?
  • How do we convert discrete noisy data to smooth functions?
  • What data exploration tools are useful?
  • Do the data display both phase and amplitude variation?
  • What about principal components analysis and other exploratory methods?

The remainder of the first day and some of the second day will consider linear models for functional data. This is a vast topic, and includes relatively basic topics like functional versions of analysis of variance and regression analysis, as well as issues less familiar to statisticians such as how differential equations can be used to model functional data. All approaches assume that the goal is to explain variation in one or more response variables by variation in one or more input or independent variables where, naturally, at least one of the variables involved is functional.

For more information on functional data analysis, see the FDA website.

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