Masked-Data

Masked Failure Data: Looking Back, Looking Forward

A retrospective on three years of building R packages and writing papers for masked series system reliability, and what comes next.

Observation Functors: Composable Censoring for Series System Simulation

Observation functors in maskedcauses: composable functions that separate the data-generating process from the observation mechanism, enabling mixed-censoring simulation and verified Monte Carlo studies.

maskedcauses: Maximum Likelihood Estimation for Masked Series System Failures

The maskedcauses R package for MLE in series systems with masked component failures, built on composable likelihood contributions and validated through simulation.

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 the standard C1-C2-C3 masking conditions are violated.

Model Selection for Weibull Series Systems: When Simpler Models Suffice

When can reliability engineers safely use simpler models? Likelihood ratio tests on Weibull series systems give sharp boundaries.

Closed-Form Results for Masked Exponential Series Systems

Closed-form MLEs and Fisher information for exponential series systems with masked failure data. No numerical optimization required.

Masked Causes of Failure in Series Systems: A Likelihood Framework

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