Probability theory and stochastic processes Expectation, moments and transforms Concept
Covariance
Summary
The covariance of two random variables measures how they vary together. It is positive when large values of one go with large values of the other, and negative in the opposite case. The node covariance gives the precise definition.
Where this leads
18 pages build on this one.
- Functional data analysis
- Stationary process
- Wiener–Khinchin theorem
- Brownian bridge
- Karhunen–Loève theorem
- Levinson recursion
- Variogram
- Wiener filter equation
- Yule–Walker equations
- Autocorrelation function
- Instrumental variables estimation
- Linear regression
- Fractional Brownian motion
- Gaussian process
- Partial correlation
- Errors-in-variables model
- Control variates
- Antithetic variates
Further reading
- Web https://en.wikipedia.org/wiki/Covariance, lead section
About this page
| Tag | 0KBFAWYRTE |
|---|---|
| URI | m2://0KBFAWYRTE |
| Kind | concept |
| Writing | W1 stated |
| MSC | 60A05 |
| Same as |
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What this page needs for W2
- edge instance_of in: at least two examples (0 of 2)
- edge not_instance_of in: at least one non-example (0 of 1)
- edge specializes both: the more general notions and the special cases (0 of 1)
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What links here
Definitions
Used by
- Functional data analysis
- Stationary process
- Wiener–Khinchin theorem
- Brownian bridge
- Karhunen–Loève theorem
- Variogram
- Yule–Walker equations
- Autocorrelation function
- Instrumental variables estimation
- Linear regression
- Fractional Brownian motion
- Gaussian process
- Partial correlation
- Errors-in-variables model
- Control variates
- Antithetic variates