For

normal random variables, X can be transformed into standard

normal random variables u through a linear transformation, as follows:

where [[alpha].sub.j] = [m.sub.j]/(1 + [b.sup.2.sub.j]), [[beta].sup.2.sub.j] = 2[[psi].sup.-1.sub.j] - 1 + 2[square root of [[psi].sup.-1.sub.j]] [square root of 2[[psi].sup.-1.sub.j] - 1], and [Z.sub.Vj] are independent standard

normal random variables.

In order to measure the (in)efficiency of Parliament in Asian countries, in this paper we propose a stochastic frontier production function for unbalanced panel data [5] assumed to be distributed as a half

normal random variables. This type of frontier and the computation method present advantages with respect other alternatives, for example the deterministic frontiers [12,16].

zero-mean

normal random variables x , y , and z with standard deviation [[sigma].sub.xyz] so that the new position of the receiver is now at coordinates (D + x',y',z').

For all scenarios, we assume that the bivariate

normal random variables (X, Y) have population parameters [[mu].sub.x] = 0, [[sigma].sub.x.sup.2] = [[sigma].sub.y.sup.2] = 1.

are

normal random variables with mean zero and covariance structure :

To view this issue from a different perspective, a chi-squared random variable with n degrees of freedom arises from summing the squares of n independent

normal random variables. Hence, a chi-squared random variable with 1 degree of freedom is simply the square of a

normal random variable.

randn for

normal random variables, and rand for continuous uniform