PMLE.loglogistic: Parametric Inference for the log-logistic model

In double.truncation: Analysis of Doubly-Truncated Data

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)

Example output

$estimate
       mu     sigma 
1.8993426 0.5954395 

$SE
       mu     sigma 
0.8939715 0.4285276 

$convergence
               logL                  DF                 AIC    No.of.iterations 
          -1.999431            2.000000            7.998862            6.000000 
No.of.randomization 
           0.000000 

$Score
[1] -2.526020e-10  1.085776e-09

$Hessian
           [,1]       [,2]
[1,] -1.3079245  0.5678522
[2,]  0.5678522 -5.6920973

double.truncation documentation built on Sept. 8, 2020, 9:07 a.m.