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2009 Jul 30
0
Constrained MLE of fixed mean Singh-Maddala distribution
...; q: shape.
# a>0, b>0, q>0. To use given mean require -a<1<aq.
# Use exponential function to ensure above constraints (except
'-a<1<aq'.
neg.LL_sinmad<-function(p, y, x)
{
a_iter<-exp(p[1])
b_iter<-exp(p[2])
q_iter<-exp(p[3])
#NEED TO PUT IN CONTITION ENSURING THAT 'q_iter-1/a_iter>0'.
z<-(-sum(log(dsinmad(y, a=a_iter, scale=b_iter,
q.arg=q_iter, log=FALSE))))
}
#Set values for initial guess of the parameters.
a_guess<-2
b_guess<-3
q_guess<-7
q_g...
2009 Jul 31
0
MLE estimation of constrained mean Singh-Maddala distribution
...bution
# Parameters: a:shape ; b: scale; q: shape.
# a>0, b>0, q>0. To use given mean require -a<1<aq.
# Use exponential function to ensure above constraints (except
'-a<1<aq'.
neg.LL_sinmad<-function(p, y, x)
{
a_iter<-exp(p[1])
b_iter<-exp(p[2])
q_iter<-exp(p[3])
#NEED TO PUT IN CONTITION ENSURING THAT 'q_iter-1/a_iter>0'.
z<-(-sum(log(dsinmad(y, a=a_iter, scale=b_iter, q.arg=q_iter,
log=FALSE))))
}
#Set values for initial guess of the parameters.
a_guess<-2
b_guess<-3
q_guess<-7
q_guess-1/a_guess
p0<-c(log(a_gu...