PR#896

Torsten Hothorn <Torsten.Hothorn@rzmail.uni-erlangen.de> writes:

c(3.770684,4.654342,3.496403,1.772743,1.624953,2.645835,3.0994
77,1.706758,3.507709,1.982924)
y<-c(-0.8161288,0.1632923,0.6421997,1.9270846,-
0.4668112,0.3587806,0.3312529,-0.5393900,
0.1057892,1.7963575)

`wdiff' is used for the computation of asymptotic confidence intervals for
samples with m,n > 50. I simulated the type I error rate and it works for
such large sample sizes. So maybe the problems are due to n.x = n.y = 10 ?

Torsten

I think the real problem is here:

ol<-optimize(wdiff,c(mumin,mumax),zq=qnorm(0.05))$minimum
om<-optimize(wdiff,c(mumin,mumax),zq=qnorm(0.5))$minimum
ou<-optimize(wdiff,c(mumin,mumax),zq=qnorm(0.95))$minimum

abline(v=ol)
abline(v=om,lty=4)
abline(v=ou,lty=7)

Optimize() is a gradient algorithm and wdiff is not smooth, so the
algorithm terminates at a stationary non-optimal point. Using optim()
with the default Nelder-Mead instead seemed to work better, although
not perfectly. (I played with this before going on Easter holiday, and
I'm not going to reconstruct exactly what I did then just now....)

-- 
   O__  ---- Peter Dalgaard             Blegdamsvej 3  
  c/ /'_ --- Dept. of Biostatistics     2200 Cph. N   
 (*) \(*) -- University of Copenhagen   Denmark      Ph: (+45) 35327918
~~~~~~~~~~ - (p.dalgaard@biostat.ku.dk)             FAX: (+45) 35327907