Description Usage Arguments Details Value Author(s) References Examples
View source: R/PMLE.SEF2.negative.R
Maximum likelihood estimates and their standard errors (SEs) are computed. Also computed are the likelihood value, AIC, and other qnantities. Since this is the model, estimates for the mean and SD are also computed.
1 | PMLE.SEF2.negative(u.trunc, y.trunc, v.trunc, epsilon = 1e-04)
|
u.trunc |
lower truncation limit |
y.trunc |
variable of interest |
v.trunc |
upper truncation limit |
epsilon |
error tolerance for Newton-Raphson |
Details are seen from the references.
eta |
estimates |
SE |
standard errors |
convergence |
Log-likelihood, degree of freedom, AIC, the number of iterations |
Score |
score vector at the converged value |
Hessian |
Hessian matrix at the converged value |
Takeshi Emura, Ya-Hsuan Hu
Hu YH, Emura T (2015) Maximum likelihood estimation for a special exponential family under random double-truncation, Computation Stat 30 (4): 1199-229
Emura T, Hu YH, Konno Y (2017) Asymptotic inference for maximum likelihood estimators under the special exponential family with double-truncation, Stat Pap 58 (3): 877-909
Dorre A, Emura T (2019) Analysis of Doubly Truncated Data, An Introduction, JSS Research Series in Statistics, Springer
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 | ### Data generation: see Appendix of Hu and Emura (2015)
n=300
eta1_true=30
eta2_true=-0.5
mu_true=30
mu_u=29.09
mu_v=30.91
a=u=v=y=c()
###generate n samples of (ui,yi,vi) subject to ui<=yi<=vi###
j=1
repeat{
u[j]=rnorm(1,mu_u,1)
v[j]=rnorm(1,mu_v,1)
y[j]=rnorm(1,mu_true,1)
if(u[j]<=y[j]&&y[j]<=v[j]) a[j]=1 else a[j]=0
if(sum(a)==n) break ###we need n data set###
j=j+1
}
mean(a) ### inclusion probability around 0.5 ###
v.trunc=v[a==1]
y.trunc=y[a==1]
u.trunc=u[a==1]
PMLE.SEF2.negative(u.trunc,y.trunc,v.trunc)
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