Exponential regression with Type I censoring

Version 1.02 (4.09 KB) by Statovic
Fits exponential regression models using maximum likelihood estimation. Data may be subject to Type I censoring.


Updated 22 Jul 2022

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Given covariates X [n x p] and target T [n x 1], the function fits an exponential regression model:
T_i ~ Exp(theta_i) , i = 1, ..., n
theta_i = Exp(X*beta)
using maximum likelihood estimation. The covariate matrix X should not include a constant vector. Parameter estimates are obtained using Fisher scoring with each iteration solving a weighted least squares problem. The method allows for type I censoring with a fixed censoring cut-off point c > 0. To analyse censored data, you must pass a vector of censoring indicators delta [n x 1]. The vector delta can be omitted if data is fully observed. When delta = 1, the data point is fully observed; delta = 0 implies a censored data point. Only type I censoring is supported where the maximum follow-up time is the same for all participants.
An example of how to use the function (testfit.m) is included.

Cite As

Statovic (2023). Exponential regression with Type I censoring (https://www.mathworks.com/matlabcentral/fileexchange/115325-exponential-regression-with-type-i-censoring), MATLAB Central File Exchange. Retrieved .

MATLAB Release Compatibility
Created with R2022a
Compatible with R2022a and later releases
Platform Compatibility
Windows macOS Linux

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Version Published Release Notes

-added more documentation


-fixed typos in the documentation