Maximum Likelihood

Likelihood Models for Series Systems with Masked Component Failure Data: An R Package for Maximum Likelihood Estimation

mdrelax: When Masking Conditions Don't Hold

Extending masked failure data analysis when traditional C1-C2-C3 conditions are violated.

Closed-Form Results for Masked Exponential Series Systems

This paper provides complete analytical results for maximum likelihood estimation in series systems with masked failure data under exponential component lifetimes. Unlike numerical approaches, everything here has a closed form.

The Problem

In series …

Statistical Inference for Series Systems from Masked Failure Time Data: The Exponential Case

Fisher Flow: An Information-Geometric Framework for Sequential Estimation

Reliability Estimation in Series Systems: Maximum Likelihood Techniques for Right-Censored and Masked Failure Data

Maximum likelihood estimation of component reliability from masked failure data in series systems, with BCa bootstrap confidence intervals validated through extensive simulation studies.

Model Selection for Reliability Estimation in Series Systems

Reliability Estimation in Series Systems Maximum Likelihood Techniques for Right-Censored and Masked Failure Data

likelihood.model: Composable Statistical Inference in R

Most R packages hardcode specific likelihood models. likelihood.model provides a generic framework where likelihoods are first-class composable objects—designed to work seamlessly with algebraic.mle for maximum likelihood estimation.

The Core Concept …

algebraic.mle: The Foundation of a Statistical Inference Ecosystem

An R package treating MLEs as first-class algebraic objects with composable statistical properties.

Likelihood Model

Numerical maximum likelihood estimation

Algebraic Maximum Likelihood Estimators