Description Usage Arguments Details Value Author(s) References Examples
View source: R/PMLE.SEF1.negative.R
Maximum likelihood estimates and their standard errors (SEs) are computed. Also computed are the likelihood value, AIC, and other qnantities.
1 | PMLE.SEF1.negative(u.trunc, y.trunc, v.trunc, tau1 = min(y.trunc), epsilon = 1e-04)
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u.trunc |
lower truncation limit |
y.trunc |
variable of interest |
v.trunc |
upper truncation limit |
tau1 |
lower support |
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 at the converged value |
Hessian |
Hessian 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 28 | ### Data generation: see Appendix of Hu and Emura (2015) ###
eta_true=-3
eta_u=-9
eta_v=-1
tau=10
n=300
a=u=v=y=c()
j=1
repeat{
u1=runif(1,0,1)
u[j]=tau+(1/eta_u)*log(1-u1)
u2=runif(1,0,1)
v[j]=tau+(1/eta_v)*log(1-u2)
u3=runif(1,0,1)
y[j]=tau+(1/eta_true)*log(1-u3)
if(u[j]<=y[j]&&y[j]<=v[j]) a[j]=1 else a[j]=0
if(sum(a)==n) break
j=j+1
}
mean(a) ## inclusion probability around 0.5
v.trunc=v[a==1]
u.trunc=u[a==1]
y.trunc=y[a==1]
PMLE.SEF1.negative(u.trunc,y.trunc,v.trunc)
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