A Fav-Jerry Distribution Under Joint Type-II Censoring: Quantifying Cross-Cultural Differences in Autism Knowledge

Abstract

The given paper proposes a new statistical framework based on the combination of the Fav-Jerry distribution (FJD) and a joint type-II censoring scheme (JT-II-CS) to examine heterogeneous and censored data. The FJD offers tractability in analysis by using its closed form of the quantile function, whereas with missing or incomplete data, the JT-II-CS offers multi-sample comparisons. Bayesian estimation is based on Markov chain Monte Carlo procedures, while the maximum likelihood estimation is obtained via a Newton–Raphson method. Both estimation strategies provide estimates of the parameters along with corresponding measures of uncertainty. The proposed methodology is also used on coded survey data on the knowledge of autism in both Hong Kong and Canada, which illustrates its potential in the measurement of cultural variance. In addition to this use, the framework highlights the potential for integrating more complex distributional modeling with censoring methods for general applications in engineering, natural sciences, and social sciences.