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Robust Methods

March 4, 2021

The recorded version of this course is now available in our EigenU Recorded Courses area.

Eigenvector Research, Inc. is pleased to bring you Robust Methods, an online instructor-led live short course. Complete information about the course can be found by following the links below.

Course Description

Outliers are a common problem in industrial data sets. In fact, the presence of outliers is more the norm than the exception. These unusual, often “erroneous” observations heavily affect the classical estimates of data mean, variance and covariance. They also, of course, greatly influence regression and other machine learning models. Without proper treatment, the resulting data models are not an accurate representation of the bulk of the data. Alternately, outlier samples are sometimes the most interesting samples in a data set, revealing unique properties or trends. If these samples are not identified, opportunities for discovery can be missed.

Robust Methods deal with the problem of outliers by determining which samples represent the “consensus” in the data and base the models on those samples, while ignoring the outliers. The course starts with methods for robust estimation of the mean and variance/covariance and goes on to methods for robust Principal Components and Partial Least Squares regression. Hands-on exercises will be done using MATLAB and PLS_Toolbox/Solo.


Linear Algebra for Chemometricians, Chemometrics I — PCA, and Chemometrics II – Regression and PLS or equivalent experience.

Course Outline

  1. Introduction to Robust Methods: The outlier problem
  2. Robust estimation of the mean: Median
  3. Robust estimation of the covariance matrix: Minimum Covariance Determinant
  4. Robust linear regresssion
  5. Robust Principal Components Analysis: ROBPCA
  6. Robust Regression in High Dimensions: ROBPCR and ROBPLS
  7. Conclusions

Course Fee

Prices include instruction, course materials (provided in advance in .pdf format), a certificate of completion and access to the recorded class sessions. Prices shown are shown below. Payment must be received by 5pm PST, Monday, March 1, 2021.

Linear Algebra for Chemometricians

Note: Payment must be received by 5pm PST, Monday, March 1, 2021. Credit card orders are strongly encouraged. Acceptable forms of payment include MasterCard, VISA, American Express, and checks drawn on a US bank. Wire transfers can also be arranged.

Academic discount: University students and faculty are eligible for the academic rate. Verification of University affiliation is required by providing valid university mailing and e-mail address. Note that we define academic as “degree granting institution.”

How to Register, Deadlines and Cancellations

To register, login to your Eigenvector account, or create an account, then select the class you would like to attend under the “Purchase” tab. You can pay directly with your MasterCard, VISA or American Express using our secure credit card processing. You may ask to be invoiced, however, Payments must be received by 5pm PST, Monday, March 1, 2021.

Complete refunds will be made for cancellations prior to Friday, February 26, 2021. No refunds will be made for cancellations after that date, however, substitutions are gladly accepted.


Calibration Model Maintenance will be taught in a single session of 3.5 hours on Thursday, March 4. The schedule will be as follows.

Daily Schedule, Pacific Standard Time (PDT)
06:45 – 07:00 WebEx available for login*
07:00 – 08:00 Instruction
08:00 – 08:10 Coffee Break
08:10 – 09:10 Instruction
09:10 – 09:20 Coffee Break
09:20 – 10:20 Instruction
10:20 – 10:30 Wrap-up and questions

* 7:00 am PST is 10:00am in New York, 15:00 in London and 16:00 in Paris.

Can’t make it at this time? The sessions will be recorded and made available to course participants for their use for one year after the event.