Estimating the thing that is always given to you by oracles in statistics homework assignments. The meat of Gaussian process regression. A complement to Gaussian process simulation. Loosely speaking, if your stochastic distribution has only 2 free parameters and you can do mean and covariance estimation, you've done the whole thing.
- relation to PCA.
- simulation of a field with a given covariance
Estimating the covariance, precision, concentration matrices of things. Turns about to be a lot more involved than estimating means in various ways and at various times. Long story.
- -matrix methods.
For robust covariances of vector data. AKA Heteroskedasticity-consistent covariance estimators. Incorporating Eicker-Huber-White sandwich estimator, Andrews kernel HAC estimator, Newey-West and others.
Parametric covariance functions
For spatial statistics or other Cartesian-ish indexed random fields. I am told I should look up Matérn covariance matrices for a parametric covariance field.
- Basic inference using Inverse Wishart by having a very basic “process model” that increases uncertainty of the covariance estimate as some convenient monotonic function of time, i should be able to get this one.
- general moment combination tricks
- John Cook's version
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