论文标题
指数的家庭趋势过滤晶格
Exponential Family Trend Filtering on Lattices
论文作者
论文摘要
趋势过滤是一种现代的非参数回归方法，比花样或类似基础程序更适合局部平滑度。现有的趋势过滤分析的重点是估计与同性恋高斯噪声损坏的功能，但我们的工作将这一技术扩展到了一般指数的家庭分布。这种扩展是出于需要研究源自极性轨道卫星的大量，网格气候数据的动机。我们提出了针对大问题量身定制的算法，一般指数家庭可能性的理论结果以及用于调整参数选择的原则方法，而无需过多计算。
Trend filtering is a modern approach to nonparametric regression that is more adaptive to local smoothness than splines or similar basis procedures. Existing analyses of trend filtering focus on estimating a function corrupted by homoskedastic Gaussian noise, but our work extends this technique to general exponential family distributions. This extension is motivated by the need to study massive, gridded climate data derived from polar-orbiting satellites. We present algorithms tailored to large problems, theoretical results for general exponential family likelihoods, and principled methods for tuning parameter selection without excess computation.
