Statistical inference in two-stage observation models including algorithmic randomness
包含算法随机性的两阶段观测模型统计推断
Randomized algorithms, such as random sampling, random projections, and stochastic optimization, are increasingly used to reduce the computational cost of modern statistical analysis. These algorithms introduce algorithmic randomness in addition to the sampling randomness in the data, and this extra source of variation complicates statistical inference. We develop a framework for valid inference in such two-stage observation models, where data are first generated from an…