Combining predictive distributions
Combining predictive distributions
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DOI：
10.1214/13-ejs823
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发表时间：
2013-01-01
影响因子：
1.1
通讯作者：
Ranjan, R.
中科院分区：
文献类型：
--
作者：
Gneiting, Tilmann;Ranjan, R.
在概率预测中，需要根据过去的经验和训练数据来估计预测分布的聚合的组合公式。我们研究组合公式和聚合方法的预测累积分布函数的校准和分散的角度来看，采取原始的预测空间的方法，适用于离散，混合离散连续和连续的预测分布一样。关键的思想是，聚合方法应该是简约的，但足够灵活，以适应任何类型的分散在组件分布。研究了线性和非线性聚集方法，包括广义的、扩展调整的和β变换的线性池。理论上证明的效果和技术，在模拟的例子，并在案例研究中，我们适合的组合公式的密度预测的标准普尔500指数回报率和每日最高温度在西雅图-塔科马机场。
In probabilistic forecasting combination formulas for the aggregation of predictive distributions need to be estimated based on past experience and training data. We study combination formulas and aggregation methods for predictive cumulative distribution functions from the perspectives of calibration and dispersion, taking an original prediction space approach that applies to discrete, mixed discrete-continuous and continuous predictive distributions alike. The key idea is that aggregation methods ought to be parsimonious, yet sufficiently flexible to accommodate any type of dispersion in the component distributions. Both linear and non-linear aggregation methods are investigated, including generalized, spread-adjusted and beta-transformed linear pools. The effects and techniques are demonstrated theoretically, in simulation examples, and in case studies, where we fit combination formulas for density forecasts of S&P 500 returns and daily maximum temperature at Seattle-Tacoma Airport.