Hi Isobel, I used correlogram as the estimator, so the Lagrange multiplier should be added to rectify the variance.
Many thanks for your help! Regards, Yang On Mon, Dec 21, 2009 at 3:31 PM, Isobel Clark <[email protected]>wrote: > Yang > > Yes the lagrangian multipier is subtracted, assuming you used the > semi-variogram in your kriging equations. If you use the covariance, it is > added. > > The extra terms in the back transform are to correct for the difference > between the variance of the true values and the variance of the estimators. > If you are estimating at points, the estimator is a weighted average which > will have a smaller variance than single point values. Back transforming > values with a smaller variance will bias the estimates downwards. > > If you want unbiassed estimated values, you have to follow the formula. > > Hope this helps > Isobel > > http://drisobelclark.kriging.com > > --- On Mon, 21/12/09, yang yu <[email protected]> wrote: > > > From: yang yu <[email protected]> > > Subject: AI-GEOSTATS: Sign of the Lagrange Multiplier Used in > Back-transform > > To: [email protected] > > Date: Monday, 21 December, 2009, 21:02 > > Hello all, > > > > I'm trying to apply the lognormal kriging method > > to a highly negatively skewed dataset (data were reflected > > first). The back_transform formula given in the reference > > book takes the following form: > > > > Z(x) = EXP[ EstimatedValue + KrigingVariance/s - > > LagrangeMultiplier] > > > > > > in which the Lagrange multiplier is subtracted from the the > > first 2 items. Is this formula assuming that the Lagrange > > multiplier value calculated for each block/cell is POSITIVE? > > All of the Lagrange values I got for my dataset are > > NEGATIVE. In this case, should the negative Lagrange values > > be ADDED to the first 2 items? > > > > > > Many thanks for any guidance and happy hollidays > > > > Regards, > > Yang > > >
