Inference for a Kavya-Manoharan Inverse Length Biased Exponential Distribution under Progressive-Stress Model Based...

Area of Science:

• Reliability Engineering
• Survival Analysis
• Statistical Modeling

Background:

• Existing survival models may lack the flexibility to accurately capture complex lifetime data.
• Accelerated life testing is crucial for assessing product reliability under stress.

Purpose of the Study:

• To introduce a novel one-parameter survival model, the Kavya-Manoharan inverse length biased exponential (KMILBE) distribution.
• To analyze the KMILBE distribution's statistical properties and information-theoretic measures.
• To apply the KMILBE distribution to progressive-stress accelerated life testing data.

Main Methods:

• Development of the KMILBE distribution using the Kavya-Manoharan transformation and inverse length biased exponential distribution.
• Derivation of statistical properties: quantiles, moments, and moment generating functions.
• Computation of various entropy and extropy measures.
• Application of estimation techniques (MLE, MPS, LS, WLS) under progressive type-II censoring for accelerated life tests.
• Construction of approximate confidence intervals for parameter estimation.
• Performance evaluation via Monte Carlo simulations and real-world data analysis.

Main Results:

• The KMILBE distribution exhibits high flexibility and outperforms several established distributions.
• The proposed estimation methods provide reliable parameter estimates for accelerated life testing data.
• Real-world data analysis confirms the model's practical applicability and superiority.

Conclusions:

• The KMILBE distribution offers a robust and flexible alternative for survival data analysis, particularly in reliability engineering.
• The developed estimation framework is effective for analyzing data from progressive-stress accelerated life tests.
• The model's enhanced performance suggests its utility in various fields requiring accurate lifetime predictions.