PMLE.loglogistic {double.truncation}

Parametric Inference for the log-logistic model

Description

Maximum likelihood estimates and their standard errors (SEs) are computed. Also computed are the likelihood value, AIC, and other qnantities.

Usage

PMLE.loglogistic(u.trunc, y.trunc, v.trunc,epsilon = 1e-5,D1=2,D2=2,d1=2,d2=2)

Arguments

u.trunc

lower truncation limit

y.trunc

variable of interest

v.trunc

upper truncation limit

epsilon

error tolerance for Newton-Raphson

D1

Randomize the intial value if |mu_h-mu_h+1|>D1

D2

Randomize the intial value if |sigma_h-sigma_h+1|>D2

d1

U(-d1,d1) is added to the intial value of mu

d2

U(-d2,d2) is added to the intial value of sigma

Details

Details are seen from the references.

Value

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

Author(s)

Takeshi Emura

References

Dorre A, Huang CY, Tseng YK, Emura T (2020-) Likelihood-based analysis of doubly-truncated data under the location-scale and AFT model, Computation Stat, DOI:10.1007/s00180-020-01027-6

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)
PMLE.loglogistic(u.trunc,y.trunc,v.trunc)