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Spatial Smoothing and FDR Control in Climate

Abstract Number:

3283

Submission Type:

Contributed Abstract

Contributed Abstract Type:

Speed

Participants:

Kyle McEvoy (1), Karen McKinnon (2)

Institutions:

(1) University of California, Los Angeles, Los Angeles, CA, (2) University of California, Los Angeles, N/A

Co-Author:

Karen McKinnon
University of California, Los Angeles

First Author:

Kyle McEvoy
University of California, Los Angeles

Presenting Author:

Kyle McEvoy
N/A

Abstract Text:

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.

Keywords:

Sponsors:

Section on Statistics and the Environment

Tracks:

Climate and Meteorology

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