Friday, 23 November 2012

Introduction to Smoothing and P-spline Techniques using R

Smoothing helps you maintain a view of the data forest, while not losing sight of the trees. Or vice-versa, if excessive globalism is your weakness. Our smoothing course with Brian Marx uses R at More on Introduction to Smoothing and P-Splines Using R.

Real-life data often are not well-described by a single simple function. Splines combine multiple functions differentially in a smooth fashion over different ranges. P-splines are widely applicable, effective, and popular: over 500 citations for the instructors' Statistical Science article that introduced P-splines. You will be introduced to P-splines via B-splines (basis splines), and learn how to balance the competing demands of fidelity to the data and smoothness, and how to optimize the smoothing.

Who Should Take This Course?
Medical and social science researchers, data miners, environmental analysts;  any researcher who must develop statistical models with "messy" data.

Course Program:

Course outline: The course is structured as follows
SESSION 1:  Smoothing via Regression - Local vs Global Bases
  • Global bases can be ineffective
  • Local bases are attractive
  • B-splines
  • Difference penalties

SESSION 2: Introducing P-splines
  • Dealing with non-normal data
  • Moving from GLM to P-spline
  • Density estimation
  • Variance smoothing

SESSION 3: Optimizing the Smoothing
  • Fidelity to the data vs smooth curve
  • Cross-validation, AIC
  • Error bands

SESSION 4: Multidimensional Smoothing
  • Generalized Addition Models
  • Varying coefficient models
  • Tensor products

The instructors:

Brian Marx is Professor of Statistics at Louisiana State University, Chair of the Statistical Modeling Society, and the Coordinating Editor of "Statistical Modeling: An International Journal."

Paul Eilers is Professor of Genetical Statistics at the Erasmus University Medical Center (Netherlands). His research interests include high throughput genomic data analysis, chemometrics, smoothing, and filtering and smoothing of time series and signals from chemical instruments.

This course takes place over the internet at the Institute for 4 weeks. During each course week, you participate at times of your own choosing - there are no set times when you must be online. Course participants will be given access to a private discussion board so that they will be able to ask questions and exchange comments with instructor, Dr. Brian Marx and Dr. Paul Eilers. In class discussions led by the instructor, you can post questions, seek clarification, and interact with your fellow students and the instructor.

The course typically requires 15 hours per week. At the beginning of each week, you receive the relevant material, in addition to answers to exercises from the previous session. During the week, you are expected to go over the course materials, work through exercises, and submit answers. Discussion among participants is encouraged. The instructor will provide answers and comments, and at the end of the week, you will receive individual feedback on your homework answers.

For Indian participants accepts registration for its courses at special prices in Indian Rupees through its partner, the Center for eLearning and Training (C-eLT), Pune.

For India Registration and pricing, please visit us at

For More details contact at
Call: 020 66009116


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