techniques for studying correlation and covariance structure n.
Download
Skip this Video
Loading SlideShow in 5 Seconds..
Techniques for studying correlation and covariance structure PowerPoint Presentation
Download Presentation
Techniques for studying correlation and covariance structure

Loading in 2 Seconds...

play fullscreen
1 / 7

Techniques for studying correlation and covariance structure - PowerPoint PPT Presentation


  • 131 Views
  • Uploaded on

Techniques for studying correlation and covariance structure. Principle Components Analysis (PCA) Factor Analysis. Principle Component Analysis. Let. Assume. Let. have a p -variate Normal distribution. with mean vector. Definition:. The linear combination.

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'Techniques for studying correlation and covariance structure' - fuller-craig


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
techniques for studying correlation and covariance structure

Techniques for studying correlation and covariance structure

Principle Components Analysis (PCA)

Factor Analysis

slide3

Let

Assume

slide4

Let

have a p-variate Normal distribution

with mean vector

Definition:

The linear combination

is called the first principle component if

is chosen to maximize

subject to

slide5

Consider maximizing

subject to

Using the Lagrange multiplier technique

Let

slide6

Now

and

slide7

Summary

is the first principle component if

is the eigenvector (length 1)of S associated with the largest eigenvalue l1 of S.