Prequential test of model fit
提出一种基于序列数据概率预测成功与否来检验统计模型拟合优度的新方法，研究了特定检验统计量的渐近分布，证明在广泛条件下（无需独立性假设）该统计量在原假设下渐近服从标准正态分布。
A new approach to testing the goodness-of-fit of a statistical model is presented, based on its success at making successive probability forecasts for a sequence of realized data-values. This approach suggests the use of test-statistics of a certain form, whose asymptotic distribution is investigated. Heuristic arguments suggest that such a statistic will have an asymptotically standard normal distribution under the null hypothesis, under wide conditions which do not require independence. This is confirmed in specific examples, and a rigorous proof is supplied for the case of Bayesian probability forecasts in independence models.