On Parametric Bootstrapping and Bayesian Prediction
On Parametric Bootstrapping and Bayesian Prediction
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DOI：
10.1111/j.1467-9469.2004.02_127.x
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发表时间：
2004-09
影响因子：
1
通讯作者：
Tadayoshi Fushiki;F. Komaki;K. Aihara
中科院分区：
文献类型：
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作者：
Tadayoshi Fushiki;F. Komaki;K. Aihara
摘要。我们研究用于预测的自助法和贝叶斯方法。观测值和被预测变量依据不同分布进行分布。许多重要问题都可以在这种设定下构建。当我们处理泊松过程时会出现这种类型的预测问题。回归问题也可以在这种设定下构建。首先，我们表明当满足某些条件时，自助法预测分布在二阶展开中与贝叶斯预测分布等价。接下来，将预测分布的性能与带有估计量的插件分布的性能进行比较。通过使用库尔贝克 - 莱布勒散度来评估预测的准确性。最后，我们给出一些示例。
Abstract. We investigate bootstrapping and Bayesian methods for prediction. The observations and the variable being predicted are distributed according to different distributions. Many important problems can be formulated in this setting. This type of prediction problem appears when we deal with a Poisson process. Regression problems can also be formulated in this setting. First, we show that bootstrap predictive distributions are equivalent to Bayesian predictive distributions in the second‐order expansion when some conditions are satisfied. Next, the performance of predictive distributions is compared with that of a plug‐in distribution with an estimator. The accuracy of prediction is evaluated by using the Kullback–Leibler divergence. Finally, we give some examples.