Compounded Linear Failure Rate Distribution: Properties, Simulation and Analysis
By: Suchismita Das, Akul Ameya, Cahyani Karunia Putri
This paper proposes a new extension of the linear failure rate (LFR) model to better capture real-world lifetime data. The model incorporates an additional shape parameter to increase flexibility. It helps model the minimum survival time from a set of LFR distributed variables. We define the model, derive certain statistical properties such as the mean residual life, the mean inactivity time, moments, quantile, order statistics and also discuss the results on stochastic orders of the proposed distribution. The proposed model has increasing, bathtub shaped and inverse bathtub shaped hazard rate function. We use the method of maximum likelihood estimation to estimate the unknown parameters. We conduct simulation studies to examine the behavior of the estimators. We also use three real datasets to evaluate the model, which turns out superior compared to classical alternatives.
Similar Papers
Modi linear failure rate distribution with application to survival time data
Methodology
Predicts when things will break down more accurately.
Quantile Residual Lifetime Regression for Multivariate Failure Time Data
Methodology
Helps doctors predict how long patients will live.
A comparative overview of win ratio and joint frailty models for recurrent event endpoints with applications in oncology and cardiology
Methodology
Helps doctors understand how treatments affect patients better.