Description Usage Arguments Details Value Author(s) References Examples
View source: R/PMLE.SEF3.negative.R
Maximum likelihood estimates and their standard errors (SEs) are computed. Also computed are the likelihood value, AIC, and other qnantities.
1 2 | PMLE.SEF3.negative(u.trunc, y.trunc, v.trunc, tau1 = min(y.trunc),
epsilon = 1e-04, D1=20, D2=10, D3=1, d1=6, d2=0.5)
|
u.trunc |
lower truncation limit |
y.trunc |
variable of interest |
v.trunc |
upper truncation limit |
tau1 |
lower support |
epsilon |
error tolerance for Newton-Raphson |
D1 |
Divergence condition for eta_1 |
D2 |
Divergence condition of eta_2 |
D3 |
Divergence condition of eta_3 |
d1 |
Range of randomization for eta_1 |
d2 |
Range of randomization for eta_2 |
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 | ## The first 10 samples of the childhood cancer data ##
y.trunc=c(6,7,15,43,85,92,96,104,108,123)
u.trunc=c(-1643,-24,-532,-1508,-691,-1235,-786,-261,-108,-120)
v.trunc=u.trunc+1825
PMLE.SEF3.negative(u.trunc,y.trunc,v.trunc)
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