ic_UMLE: Compute Unconstrained Maximum Likelihood Estimate for...

In iclogcondist: Log-Concave Distribution Estimation with Interval-Censored Data

Compute Unconstrained Maximum Likelihood Estimate for Interval-Censored Data

Description

This function computes the unconstrained maximum likelihood estimate (UMLE) for interval-censored data. It utilizes the non-parametric MLE from the ic_np function in the icenReg package as a starting point and prepares key components such as cumulative probabilities, log-transformed values, and knot information.

Usage

ic_UMLE(X)

Arguments

X	A matrix with two columns, where each row represents an interval (L, R] for interval-censored data. L and R are left and right endpoints, respectively, with R = Inf indicating right-censoring.

Details

The ic_np function from the icenReg package is used to compute the non-parametric MLE for interval-censored data. This provides initial estimates of probabilities (p_hat) and jump points (knot) in the cumulative distribution function. These are then processed to compute the cumulative probabilities (F_hat) and log-transformed values (phi_hat) at unique time points.

Value

A list containing:

est	A list with tau_no_Inf (finite values of tau), phi_hat (log of F_hat values), and F_hat (MLE cumulative distribution function values).
knot_info	A list with knot_index (indices of knots in tau_no_Inf), tau_on_knot (values of tau at knots), F_on_knot (MLE at knots), and phi_on_knot (log of F at knots).
neg_log_likelihood	The negative log-likelihood of the MLE fit.
weight	Vector of weights corresponding to each interval in the data.
X	The original interval-censored data matrix input.

References

Anderson-Bergman, C. (2016) An efficient implementation of the EMICM algorithm for the interval censored NPMLE Journal of Computational and Graphical Statistics.

Examples

data(lgnm)
result <- ic_UMLE(X = lgnm)

iclogcondist documentation built on April 4, 2025, 5:18 a.m.