Tagged Questions

Covariance is a quantity used to measure the strength and direction of the linear relationship between two variables. The covariance is unscaled, & thus often difficult to interpret; when scaled by the variables' SDs, it becomes Pearson's correlation coefficient.

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Covariance function and autocovariance function of a fractional Brownian motion?

What is the difference between covariance function and autocovariance function of a fractional Brownian motion? Page 6: Eq 1.6 /1.7 http://www.columbia.edu/~ad3217/fbm/thesisold.pdf Second: How can ...
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I am trying to implement the paper Adaptive Kalman Filter for INS/GPS and there are a couple of expressions where the paper says that it comes from the Standard Kalman Filter theory, but I can't see ...
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Whitening transform for the case of two stochastic processes

The classic whitening transform allows us to find a linear transformation for a given random process X, yielding a new process Xw with a unity Covariance matrix. Given an extended problem with two ...
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How to prove that $Cov(\hat{\beta},\bar{Y}) = 0$ using given covarience properties
To quote: It is well known that, if $W_1, ..., W_n, Z_1, ..., Z_m$ are random variables and $a_1, ..., a_n, b_1, ..., b_m$ are constants, then \$Cov ( \sum_{i=1}^n a_iW_i, \sum_{j=1}^m b_jZ_j) = \...