NPMLE: Nonparametric inference based on the self-consistency method

In double.truncation: Analysis of Doubly-Truncated Data

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,detail=FALSE)

Arguments

u.trunc	lower truncation limit
y.trunc	variable of interest
v.trunc	upper truncation limit
epsilon	error tolerance for the self-consistency algorithm
detail	if TRUE, show the details including the covariate matrix

Details

Details are seen from the references.

Value

f	density
F	cumulative distribution
SE	standard error
convergence	Log-likelihood, and the number of iterations
V	covariance matrix for the NPMLE

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)
NPMLE(u.trunc,y.trunc,v.trunc,detail=TRUE)

double.truncation documentation built on April 4, 2025, 3:31 a.m.