Optimal Type I Censoring Scheme with Shannon Information Gain

Music City Center 

In life testing studies, such as industrial reliability testing or clinical trials, it is critical to use a Type I censoring strategy that limits test time while delivering adequate information about the life characteristics. This paper investigates an ideal Type I censoring scheme utilizing Shannon information gain, which is a measure of the information collected from the experiment. We use a Bayesian approach to model life tests, focusing on optimizing expected information gain during the design process. However, due to the complexity of the calculations, we use the Metropolis-Hastings algorithm to approximate the expected Shannon information gain at different censoring times and obtain the optimize setting by using an augmented probability simulation and monotone smoothing process . Our goal is to choose an optimal censoring time that produces a reasonable degree of information gain, usually about 90% of the maximum achievable value. We apply the methodology to the testing of hardened steel specimen data, demonstrating the usefulness of the suggested algorithm in determining the optimal censoring time for life tests with Type-I censoring.

Shannon Information Gain

Metropolis-Hastings Algorithm

Type-I Censoring

Augmented probability simulation 

International Indian Statistical Association