Predictive Modeling of GH Blunting Patterns in Aging and Exercise-Trained Individuals

This paper proposes a novel semiparametric method using Gaussian process smoothing to estimate failure rates in engineered systems. Unlike traditional models, it provides accurate estimates based on historical data without assuming specific failure rate patterns. Experiments ‎using power system failure data demonstrate the method’s accuracy and effectiveness compared to other models. The approach applies ‎to various systems, including software reliability estimation. Additionally, mathematical results align well with the Gaussian process model, ‎successfully identifying the peak of growth hormone deficiency over time‎.