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In general, what you really ought to do is start with a statistical model for the data y(t) as the sum of a smooth trend function m(t) selected from some probability space and zeromean errors e(t) (which may be autocorrelated) and then work out what weights give you the best estimate of the trend function from the data – linear regression being a good starting point.

The reason for the bias in the MBH98 PC1 can be understood by considering that PCA maximizes the variance described by each principal component where variance is measured as the sum of the squared record, _2 = Pt x2t , and x is not necessarily zeromean.

Assuming data is zeromean just saving space, we have

Now, if the noise is zeromean, and autocorrelated, it is not necessarily a problem.

Thus, a third normalization is proposed where records are adjusted to zeromean and unit variance over their full 1400 to 1980 duration, a standard practice in PCA Preisendorfer, 1988; Rencher, 1995 here referred to as full normalization.

“Thus, a third normalization is proposed where records are adjusted to zeromean and unit variance over their full 1400 to 1980 duration, a standard practice in PCA Preisendorfer, 1988 p22; Rencher, 2002 p393 here referred to as full normalization.”

It's basically just a matter of adding up a constant net power (the energy you have to buy), and a zeromean fluctuating power (the grid service that you sell).

The bit stream is distributed into the transmit antennas according to the following transmission format matrix (assuming 2 transmit antennas). where H is the M_N channel matrix, and s = [s1, s2, ..., sm] T is the Mdimensional transmit signal vector, w is the Ndimensional vector of zeromean noise with the variance of _2.

But gaps can work for and against you and in high frequency swing trading they end up causing a variation of risk of zeromean about the target value.

The bit stream is distributed into the transmit antennas according to the following transmission format matrix (assuming 2 transmit antennas). where H is the M_N channel matrix, and s = [s1, s2, ..., sm] T is the Mdimensional transmit signal vector, w is the Ndimensional vector of zeromean noise with the variance of _2.
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