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Examples

  • 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 zero-mean 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.

    Mannomatic Smoothing and Pinned End-points « Climate Audit

  • Assuming data is zero-mean just saving space, we have

    Juckes Omnibus « Climate Audit

  • Now, if the noise is zero-mean, and autocorrelated, it is not necessarily a problem.

    More on MBH Confidence Intervals « Climate Audit

  • 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 zero-mean.

    Take a Ritalin, Dave « Climate Audit

  • Thus, a third normalization is proposed where records are adjusted to zero-mean 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.

    Reply to Huybers #3: Principal Components « Climate Audit

  • “Thus, a third normalization is proposed where records are adjusted to zero-mean 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.”

    Reply to Huybers #3: Principal Components « Climate Audit

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

    Autoblog Green

  • 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 M-dimensional transmit signal vector, w is the N-dimensional vector of zero-mean noise with the variance of _2.

    Wireless Design Asia - Latest Updates

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

    FinanceVisor - Today's News Stories

  • Noise signals introduced in the sequel are defined similarly and are taken as zero-mean Gaussian random signals, uncorrelated in time, with standard deviation equal to 0.1 times the input signal magnitude.

    PLoS ONE Alerts: New Articles

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