Statistics How To

Cross Covariance

Statistics Definitions >

Cross covariance of x and y, in statistics, is a measure of the similarity between x and shifted versions of y, as a function of the shift (lag). The cross covariance is given by the equation

where E is the expectation operator, and the processes have mean functions vt=E[Yt] and μt=E[Xt]

In signal processing, cross-covariance has a slightly different definition: it measures the similarity between two signals, and is a function of the time between signals. It is sometimes called the sliding dot product or the cross-correlation.

Note that, in statistics, the cross-correlation and cross covariance are related but are not the same thing.


Need help with a homework or test question? With Chegg Study, you can get step-by-step solutions to your questions from an expert in the field. If you'd rather get 1:1 study help, Chegg Tutors offers 30 minutes of free tutoring to new users, so you can try them out before committing to a subscription.

If you prefer an online interactive environment to learn R and statistics, this free R Tutorial by Datacamp is a great way to get started. If you're are somewhat comfortable with R and are interested in going deeper into Statistics, try this Statistics with R track.

Comments? Need to post a correction? Please post a comment on our Facebook page.

Check out our updated Privacy policy and Cookie Policy

Cross Covariance was last modified: September 11th, 2018 by Stephanie