Predictive Fit for Natural Exponential Families
研究了自然指数族中预测分布的拟合度量，提出平均自助法预测分布，证明其渐近优于估计分布，并给出泊松和二项分布的小样本结果。
The paper examines predictive distributions, concentrating on measuring their fit to the true distribution by average KulLback–Leibler divergence. The notion of an ‘averaged bootstrap’ predictive distribution is introduced. This predictive distribution is shown to be asymptotically superior to the estimative distribution, in terms of average Kullback–Leibler divergence, when the true distribution is in a natural exponential family. Small-sample results are presented for the Poisson and binomial distributions which suggest that the bootstrap distribution performs well in these cases.