weibull.ic: Maximum likelihood estimates with Interval Censoring

In weibullness: Goodness-of-Fit Test for Weibull Distribution (Weibullness)

Maximum likelihood estimates with Interval Censoring

Description

Calculates the maximum likelihood estimates with Interval Censoring Using the EM Algorithm.

Usage

weibull.ic(X, start=c(1,1), maxits=10000, eps=1E-5)

Arguments

X	a numeric matrix (n x 2) of observations.
start	a starting value.
maxits	the maximum number of iterations.
eps	the desired accuracy (convergence tolerance).

Details

The expectation-maximization(EM) algorithm is used for estimating the parameters with interval-censored data.

Value

Calculates the maximum likelihood estimates with interval-censored data

Author(s)

Chanseok Park

References

Park, C. (2023). A Note on Weibull Parameter Estimation with Interval Censoring Using the EM Algorithm. Mathematics, 11(14), 3156.
\Sexpr[results=rd]{tools:::Rd_expr_doi("10.3390/math11143156")}

Lawless, J. F. (2003). Statistical Models and Methods for Lifetime Data, 2nd ed.; John Wiley & Sons: New York, NY.

See Also

weibull.wp for the parameter estimation using the Weibull plot with full observations. weibull.mle for the parameter estimation using the maximum likelihood method with full observations.

Examples

library(weibullness)

attach(Wdata)
weibull.ic(radio.chemotherapy)

# Two-parameter Weibull with full observations
weibull.ic( cbind(bearings,bearings) )

# Two-parameter Weibull with full observations (using weibull.mle)
weibull.mle(bearings, threshold=0)

weibullness documentation built on Aug. 8, 2023, 5:12 p.m.