symbolically equal to the wellknown normal equation matrix of least squares. 
The limiting Variance of the estimators ü for u follows then to: 
lim Vartû) = T1”! 
n ^ 
Although theoretically interesting, this limit theorem is of limited practical 
importance due to the impatience of most surveyors to make infinitely many 
observalions. 
5. NUMERICAL SOLUTION 
In order to establish the link with the conventional least squares method 
and robust estimation, we rewrite problem (5), (6) into: 
I p(rji)rj;^ —' min ; r, = Z, - E(2;) 
1/21 for (ri) < a (9) 
with p(r;) = 
0 :sfor {r,) > a (10) 
and obtain a formulation similar to Hampels method of robust estimation, 
(Hampel, 1973). As the problem (9) cannot be readily solved, an iterative 
solution is usually used, approximating the stepfunction (10) for p by a 
continuous function, and iteratively approaching its final shape (either from 
the inside or the outside of p(r;)). Possible choices are: 
plr;).- 1/42 for Ir! <a (11) 
i 7 : 
La? exp — (r-a)* for lr,l > a 
increasing throughout iteration 
or 
1/2 for lpi: <a 
per PER a (12) 
o? .ı7ı8 for ril 2. 8 
r! 
These weight choices relate this method to the wellknown Danish Method 
(Kubik, 1982) and L8 - Method (Barrodale, 1966). The numerical method will 
converge to a unique solution, if sufficiently small changes are made in the 
approximating function for (10) from iteration to iteration. 
In the past, we experienced problems with convergence, mainly due to 
improper initial values for û and r, which were computed by the classical 
least squares method. Other numerical solution methods should, therefore, 
be investigated. 
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