| NPMLE {double.truncation} | R Documentation |
Nonparametric inference based on the self-consistency method
Description
Nonparametric maximum likelihood estimates are computed based on the self-consistency method (Efron and Petrosian 1999). The SE is computed from the asymptotic variance derived in Emura et al. (2015).
Usage
NPMLE(u.trunc, y.trunc, v.trunc,epsilon=1e-08)
Arguments
u.trunc |
lower truncation limit |
y.trunc |
variable of interest |
v.trunc |
upper truncation limit |
epsilon |
error tolerance for the self-consistency algorithm |
Details
Details are seen from the references.
Value
f |
density |
F |
cumulative distribution |
SE |
standard error |
convergence |
Log-likelihood, and the number of iterations |
Author(s)
Takeshi Emura
References
Efron B, Petrosian V (1999). Nonparametric methods for doubly truncated data. J Am Stat Assoc 94: 824-834
Emura T, Konno Y, Michimae H (2015). Statistical inference based on the nonparametric maximum likelihood estimator under double-truncation. Lifetime Data Analysis 21: 397-418
Dorre A, Emura T (2019) Analysis of Doubly Truncated Data, An Introduction, JSS Research Series in Statistics, Springer
Examples
## A data example from Efron and Petrosian (1999) ##
y.trunc=c(0.75, 1.25, 1.50, 1.05, 2.40, 2.50, 2.25)
u.trunc=c(0.4, 0.8, 0.0, 0.3, 1.1, 2.3, 1.3)
v.trunc=c(2.0, 1.8, 2.3, 1.4, 3.0, 3.4, 2.6)
NPMLE(u.trunc,y.trunc,v.trunc)