On 11-Oct-06 Njeri Wabiri wrote:
> Dear list
> Just a newbabie question
> What is the statistical interpretation of the Lagrange
> multiplier in kriging.
> At least I know if its positive we have a high kriging
> variance and vice versa.
> 
> Grateful for a response and a reference
> 
> Njeri 

The general interpretation of a Langrange multiplier is
as follows.

In the context of an extremal problem (find the max/min)
of a function

  f(x1,x2,...,xk)

subject to a constraint

  g(x1,x2,...,xk) = c

when you express it as solving the extremal problem for

  f(x1,x2,...,xk) - L*g(x1,x2,...,xk)

(and then using the constraint equation to eliminate L),
the value of L is equal to the rate of change of the
extremal value of f (as so found) with respect to variation
in the value of c.

Otherwise put: for a particular value of c, the extremal
value of f is (say) fmax(c). Then L = dfmax(c)/dc. The
same is true with several constraints (and a corresponding
number of lagrange multipliers), in that the i-th L is
equal to the partial derivative of fmax(c1,c2,...,cr)
with respect to ci.

[This assumes that the function f has a max/min which
can be found analytically by solving the equation[s]
obtained by setting derivative[s] equal to 0. The above
is not quite so directly true when the maximum is attained
on the boundary of the region defined by the constraints,
as in a Linear Programming problem, for instance; though
something similar is also true there.]

I can supply a demonstration of the above, if requested.

So, if (in a statistical context) a sum of squares if
minimised, or a likelihood is maximised, subject to constraints,
then the above applies to the sum of squares, or likelihood,
or whatever.

Hoping this helps,
Ted.

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E-Mail: (Ted Harding) <[EMAIL PROTECTED]>
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Date: 11-Oct-06                                       Time: 21:27:33
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