Spatial Smoothing and FDR Control in Climate

Oregon Convention Center 

In this paper we explore FDR control in the climate setting, focusing on applications to the commonly used gridbox-by-gridbox simple linear regression technique. In order to properly evaluate simulation results, a modification of the standard hypothesis tests is proposed and developed, and the consequences of using the new hypothesis tests is explored. In order to improve the power of the Benjamini-Hochberg method in this setting, a method for locally smoothing the data is proposed. This method estimates local spatial covariances and uses the estimated covariances to create smoothing weights. Simulation results show that the smoothing method improves the number of true rejections and the sensitivity of FDR approaches at the cost of increasing the probability of finding no rejections. The technique is applied to January sea surface temperature standardized anomalies with a simulated response.

FDR

Spatial

Climate

Smoothing

Multiple Hypotheses

Regression 

Section on Statistics and the Environment